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🚨 #Maxon #Redshift 2026.2 is here! ⚡️ Tessellation-free displacement delivers ultra-detailed surfaces—no tessellation overhead, just speed. 🎯 UV Context Projection node lets you control texture placement with multiple transforms & projection types, including triplanar

Maxon Redshift

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21,808 次观看 • 8 个月前 •via X (Twitter)

#Maxon #Redshift

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🔴 INTRODUCING BLOOD v3 🔴 The most advanced Solana bot just got even better. After months of relentless work, testing, and fine-tuning, we're proud to unveil BLOOD GUI – the most intuitive, lightning-fast, and powerful UI ever created. Built for speed, simplicity, and precision, it redefines the way you interact with bot. Newcomers can set everything up in minutes, while pros will execute their strategies with unmatched efficiency. 🔥 WHAT’S NEW? 🔥 ✅ Informative homepage – get all essential bot details at a glance. ✅ Sleek, optimized UI – clean design with zero performance loss. ✅ Smart settings management – hide unnecessary options or find the right setting instantly with a search bar. ✅ Enhanced position control – manage multiple trading modes with full control over your trades. ✅ 🔁 Unlimited wallet copying – mirror an infinite number of wallets. ✅ ⚡️ Upgraded nodes – FREE NODE + FASTEST GEYSERS FOR ALL HOLDERS. ✅ 📊 PNL & Winrate analytics – track performance, optimize strategies, and maximize profits with in-depth wallet analysis. And the best part? You get ALL of this with ZERO TRADING TAXES 💰. 🚨 This is not just an update. It’s a GAME-CHANGER. 🚨 There is also one more important thing…

🔴 INTRODUCING BLOOD v3 🔴 The most advanced Solana bot just got even better. After months of relentless work, testing, and fine-tuning, we're proud to unveil BLOOD GUI – the most intuitive, lightning-fast, and powerful UI ever created. Built for speed, simplicity, and precision, it redefines the way you interact with bot. Newcomers can set everything up in minutes, while pros will execute their strategies with unmatched efficiency. 🔥 WHAT’S NEW? 🔥 ✅ Informative homepage – get all essential bot details at a glance. ✅ Sleek, optimized UI – clean design with zero performance loss. ✅ Smart settings management – hide unnecessary options or find the right setting instantly with a search bar. ✅ Enhanced position control – manage multiple trading modes with full control over your trades. ✅ 🔁 Unlimited wallet copying – mirror an infinite number of wallets. ✅ ⚡️ Upgraded nodes – FREE NODE + FASTEST GEYSERS FOR ALL HOLDERS. ✅ 📊 PNL & Winrate analytics – track performance, optimize strategies, and maximize profits with in-depth wallet analysis. And the best part? You get ALL of this with ZERO TRADING TAXES 💰. 🚨 This is not just an update. It’s a GAME-CHANGER. 🚨 There is also one more important thing…

BLOOD SOLUTIONS

12,258 次观看 • 1 年前

Promised to ship before the movie so... 📅 53 days 🤖 731 vibe coded commits ⚡️ Powered by Three.js 🚀 Inspired by a space plumber 🙋‍♂️ AMA, no secrets, no shame High level, grouped list of what's in this game: Galaxy DNA - Lumas - Star Bits - Spin attack with air boost + ground-cancel - Galaxy gravity - Octoombas - Gateway Galaxy music track Vibe Coding Process - Claude Code (Opus) for ~95% of all code - CLAUDE.md project instructions file (163 lines of rules + constraints) - 87 implementation plans written before coding - 36 AI code reviews (ECS, architecture, performance) - 11 retrospectives after major features - 60 extracted skills (reusable knowledge from debugging sessions) - Constraints doc that grows every time something breaks (115 lines) - /lets-build workflow: discovery → plan → review → implement → verify - Every feature: plan first, review the plan, then build in atomic commits - Custom level construction CLI (AI-assisted placement) - this evolved over 53 days Architecture - 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Galaxy-style spin attack - Rainbow diamond particle burst (InstancedMesh, 64 pool) - Hit-stop with global time scale freeze + camera shake - Air boost, ground-cancel, shell kick at extended range - Invulnerability frames during active spin Yoshi - mount/ride system with shield HP - +3 extra HP ring on mount, damage depletes shield first - Overflow damage carries to Mario - Forced dismount on shield break with poof effect Objects - Coin system with InstancedMesh rendering (256 max) - Per-instance opacity via shader patching - Pop → float → shrink → fade collection animation - Swap-and-pop O(1) entity removal - Star bit burst spawning with attraction system - Pipe warp travel with camera override blend - Parallel-transported screen-right vector during crossfade - Shadow field updates for traveling entities - trampoline - 3D assets from Hello Mario Framework (now archived) and game rips Audio - 17+ wired sound effects with spatial audio - Bob-omb fuse sound: per-entity lifecycle, looping playback tracking - 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Promised to ship before the movie so... 📅 53 days 🤖 731 vibe coded commits ⚡️ Powered by Three.js 🚀 Inspired by a space plumber 🙋‍♂️ AMA, no secrets, no shame High level, grouped list of what's in this game: Galaxy DNA - Lumas - Star Bits - Spin attack with air boost + ground-cancel - Galaxy gravity - Octoombas - Gateway Galaxy music track Vibe Coding Process - Claude Code (Opus) for ~95% of all code - CLAUDE.md project instructions file (163 lines of rules + constraints) - 87 implementation plans written before coding - 36 AI code reviews (ECS, architecture, performance) - 11 retrospectives after major features - 60 extracted skills (reusable knowledge from debugging sessions) - Constraints doc that grows every time something breaks (115 lines) - /lets-build workflow: discovery → plan → review → implement → verify - Every feature: plan first, review the plan, then build in atomic commits - Custom level construction CLI (AI-assisted placement) - this evolved over 53 days Architecture - 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Tommy Leung

185,083 次观看 • 5 个月前

$NVDA $MU $SNDK $LITE PAPER OVERVIEW AND CORE CLAIMS The paper “KV Cache Transform Coding for Compact Storage in LLM Inference” introduces kvtc, a transform-coding pipeline that compresses transformer key-value (KV) caches primarily for storage and transfer in LLM serving, rather than for accelerating the per-token attention kernel during active decoding. The method combines 3 stages: (1) feature decorrelation via a PCA basis computed from a calibration dataset and reused across requests; (2) adaptive, variable-precision quantization with bit allocation solved via dynamic programming (DP), including groupwise scaling/shift overhead; and (3) lossless entropy coding (DEFLATE via nvCOMP in the reference implementation) to exploit residual redundancy after quantization. The central empirical claim is that KV tensors contain large, exploitable redundancy across heads and layers, enabling approximately 20× compression versus a 16-bit baseline with negligible degradation across a broad set of accuracy and long-context benchmarks, with materially higher compression (≥40×) available at modest quality cost in some regimes. The system claim is that such compression materially improves the economics of multi-turn, prefix-reuse serving by extending effective KV cache capacity in GPU HBM and host tiers (DRAM/NVMe) and by reducing inter-node and GPU↔host bandwidth demands, thereby improving cache hit rates and reducing time-to-first-token (TTFT) relative to recomputation when caches would otherwise be evicted. KV CACHE AS THE DOMINANT STATE VARIABLE IN INFERENCE ECONOMICS KV cache growth is linear in context length and is multiplicative in layers and attention heads, making it an increasingly dominant constraint as (a) context lengths expand, (b) models add layers and maintain large hidden dimensions, and (c) production workloads shift toward iterative and tool-augmented interactions that repeatedly reuse long prefixes. The paper uses the canonical 16-bit KV cache size formula (4·l·h·d_head·t) bytes and reports 16-bit KV cache sizes per 1K tokens of context that are already operationally large: 128MiB for Llama 3.1 8B, 160MiB for Mistral NeMo 12B, and 320MiB for Llama 3.3 70B Instruct. In binary units, these figures imply per-token KV footprints of 128KiB/token (Llama 3.1 8B), 160KiB/token (Mistral NeMo 12B), and 320KiB/token (Llama 3.3 70B Instruct) at 16-bit. For a 10K-token prompt (10×1K in the paper’s binary convention), the 16-bit KV cache sizes scale to approximately 1.25GiB (Llama 3.1 8B), 1.56GiB (Mistral NeMo 12B), and 3.13GiB (Llama 3.3 70B Instruct). These magnitudes explain why stale caches create a throughput–latency dilemma: retaining them in HBM maximizes responsiveness on future turns but crowds out concurrent sessions; evicting them forces quadratic-cost prefill recomputation and increases TTFT; offloading them to host or storage introduces large transfer overhead and consumes DRAM/NVMe capacity. A key operational nuance emphasized is that modern serving stacks increasingly treat KV caches as a database, leveraging block paging and shared-prefix reuse. In the common disaggregated serving design (separate prefill and decode nodes), KV cache transfer becomes a dominant category of cross-node traffic. Under that design, any reduction in KV cache size directly increases effective fabric capacity and reduces tail latency attributable to congestion, while also enabling longer cache lifetimes in “hot” (HBM) and “warm” (CPU DRAM) tiers that raise cache hit rates and reduce recomputation frequency. The paper’s quantitative example illustrates the economic stakes: a 1,000-line code file tokenized at ~10 tokens/line yields ~10K tokens; for Llama 3.3 70B, an 8-bit KV cache for that context is ~1.6GiB. Reuse across subsequent turns or parallel chats around the same file is valuable, but HBM scarcity makes retaining many such caches infeasible without compression. TECHNICAL MECHANISM: WHY KV CACHES ARE COMPRESSIBLE AND HOW KVTC EXPLOITS IT The technical rationale begins with an empirical observation: keys (and, to a lesser extent, values) across different attention heads can be aligned into a shared latent space using orthogonal transformations (Procrustes alignment). This supports the hypothesis that head-specific projections introduce rotations of a common subspace rather than completely distinct information, implying that concatenating across heads and layers should reveal low-rank structure suitable for linear decorrelation and dimensionality reduction. The method operationalizes this using a PCA/SVD basis learned from calibration data rather than recomputing a decomposition per prompt. This design choice targets production viability: per-prompt SVD is computationally expensive and scales poorly with long prompts and frequent cache updates. kvtc is explicitly structured as an offline-calibrated, online-applied codec: Calibration (performed 1 time per model and compression setting for DP allocation) A calibration dataset is forwarded through the model to collect KV caches. Token positions are pooled, and a subset of positions is sampled. Keys and values are processed separately. Several implementation choices are highlighted as decisive for stability: Rotary positional embeddings are effectively removed prior to compression (“undo positional rotations”), because positional rotations degrade the apparent low-rank structure of keys. “Attention sink” tokens (the earliest tokens in the sequence) and a sliding window of most recent tokens are excluded from compression because they disproportionately affect attention patterns and are empirically more sensitive to reconstruction error. Cross-layer concatenation is used: keys (or values) from multiple layers and heads at the same token position are concatenated along the feature axis to form a higher-dimensional feature vector. PCA is computed over these concatenated vectors, improving robustness relative to per-layer or per-head PCA. The PCA basis is computed via SVD of centered calibration data, using randomized SVD for scalability with a target rank cutoff. The paper reports calibration regimes of 160K tokens for several models with a 10K PCA dimension cutoff (8K for Qwen variants with fewer KV heads), selected to fit within a single 80GB H100 memory envelope and complete within minutes. A critical economic detail is that the same PCA basis can be reused across multiple compression ratios; only the DP-derived precision assignment changes per compression target. Compression (applied between inference phases) Compression operates on stored KV cache tensors, not on weights, and does not modify attention computation. The KV cache is projected into the PCA basis, quantized, packed, and then entropy-coded. Compression is positioned as a background or between-phase operation (after decoding, or between prefill and decode), executed on GPU or CPU depending on where the cache currently resides. The design intent is that compression should not sit on the critical per-token decoding path; it is a storage and transport optimization. Decompression (performed prior to reuse) Decompression reverses the entropy coding and quantization and applies the inverse PCA projection. A practical latency optimization is proposed: inverse projection can be performed layer-by-layer using submatrices of the PCA basis, allowing generation to begin before the full cache is reconstructed, reducing TTFT. Quantization and bit allocation are the core differentiators versus simpler PCA truncation. PCA provides ordered components by variance; kvtc uses DP to allocate a global bit budget across PCA coordinates (and across groups of coordinates) to minimize reconstruction error in the decorrelated domain. Groups of subsequent PCA coordinates share 16-bit shift and scale factors (a microscaling-inspired design), and the DP algorithm jointly selects group size and precision type under a bit budget, including the overhead of per-group metadata. DP commonly assigns 0 bits to many trailing PCA components, which both increases compression and provides a mechanism to trim the PCA basis to the subset of components that actually carry payload, reducing compute and storage overhead of the projection matrices in deployment. Lossless entropy coding then exploits the structure induced by quantization. DEFLATE is used in the reference implementation, and the paper emphasizes that the incremental gain from the lossless stage is content-dependent but meaningful, with an average uplift of ~1.23× on top of quantization in the reported regime. An ablation in the appendices indicates that GPU-friendly variants (GDeflate) can achieve nearly identical compression ratios (≤0.1 difference in measured cases), implying that throughput-optimized lossless codecs can likely be substituted without sacrificing meaningful compression. EMPIRICAL RESULTS: ACCURACY, COMPRESSION, AND LATENCY General-purpose 8B–12B dense models The paper evaluates Llama 3.1 8B, MN-Minitron 8B, and Mistral NeMo 12B across math/knowledge (GSM8K, MMLU) and long-context tasks (Qasper, Lost in the Middle, RULER Variable Tracking) under a simulated multi-turn regime where compression/decompression is applied periodically, with a sliding window of recent tokens excluded. A consistent pattern appears: kvtc maintains near-vanilla performance through 16× compression settings, and remains competitive at 32×, with degradation becoming task- and model-dependent at 64×, particularly on long-context retrieval metrics when compression is pushed aggressively. Selected quantitative anchor points from the paper’s standard-error table (all values are reported with the paper’s evaluation setup and token-window exclusions): Llama 3.1 8B Vanilla: GSM8K 56.8, MMLU 60.5, Qasper 40.4, LITM 99.4, RULER-VT 99.8 kvtc16×: GSM8K 56.9, MMLU 60.1, Qasper 40.7, LITM 99.3, RULER-VT 99.1 kvtc32×: GSM8K 57.8, MMLU 60.6, Qasper 39.4, LITM 99.1, RULER-VT 98.9 kvtc64×: GSM8K 57.2, MMLU 60.7, Qasper 37.8, LITM 90.2, RULER-VT 95.9 These results indicate that, for this model, long-context sensitivity emerges at 64× with meaningful drops in LITM and RULER-VT, while math/knowledge scores remain stable, implying a differential sensitivity consistent with key-vector precision being more critical for retrieval-style behavior. Mistral NeMo 12B Vanilla: GSM8K 61.9, MMLU 64.5, Qasper 38.4, LITM 99.5, RULER-VT 99.8 kvtc16×: GSM8K 62.0, MMLU 64.4, Qasper 37.6, LITM 99.8, RULER-VT 99.5 kvtc32×: GSM8K 62.2, MMLU 63.8, Qasper 37.5, LITM 99.6, RULER-VT 98.7 kvtc64×: GSM8K 61.9, MMLU 61.4, Qasper 38.0, LITM 95.3, RULER-VT 98.0 Here, degradation at 64× is visible but materially smaller than the Llama 3.1 8B LITM drop, suggesting model-architecture or training-data differences can change the tolerance envelope for aggressive KV cache distortion. MN-Minitron 8B Vanilla: GSM8K 59.1, MMLU 64.3, Qasper 38.2, LITM 99.8, RULER-VT 99.4 kvtc16×: GSM8K 60.3, MMLU 64.1, Qasper 38.6, LITM 99.3, RULER-VT 98.8 kvtc32×: GSM8K 59.1, MMLU 63.7, Qasper 37.7, LITM 86.9, RULER-VT 96.0 kvtc64×: GSM8K 57.8, MMLU 62.1, Qasper 38.1, LITM 59.5, RULER-VT 93.4 This model shows markedly higher sensitivity on LITM at 32× and 64×, despite stable short-context metrics, reinforcing that “compression safety” is not monotonic in parameter count and that pruning/distillation choices can alter KV cache redundancy or robustness. Comparisons to baselines The paper compares kvtc to quantization baselines (KIVI, GEAR, FP8) and eviction baselines (H2O, TOVA), plus an SVD-based prefill-optimization method (xKV). Across the reported tasks: Low-bit quantization methods at modest compression (2-bit KV schemes) show earlier degradation in long-context behavior than kvtc at substantially higher compression settings. Eviction methods perform poorly as generic compressors for long-context tasks, consistent with their objective function (selective pruning) being misaligned with “lossless-ish storage for reuse.” xKV shows competitive results on some tasks but a consistent underperformance on Qasper relative to kvtc and vanilla in the provided tables, consistent with method-specific distortions introduced by its decomposition regime. Reasoning models and high-variance tasks For DeepSeek-R1-distilled Qwen 2.5 reasoning models, the paper evaluates AIME 2024/2025 and LiveCodeBench coding. Results are averaged over 8 runs with large variance, but a key inference is that kvtc at ~9×–21× compression achieves broadly similar AIME scores within variance bands, while coding performance remains stable at ~9× and degrades more visibly at ~18×–21× on the 7B model. An important nuance is that smaller reasoning models already have smaller KV footprints (reported ~29KiB/token for Qwen R1 1.5B versus 131KiB/token for Llama 3.1 8B), so the economic value of aggressive KV cache compression is proportionally higher for large models and long contexts than for small models with short contexts, unless the serving system’s bottleneck is dominated by cache transfer rather than HBM capacity. Multi-GPU inference and pipeline parallel For Llama 3.3 70B Instruct run pipeline-parallel across 4 GPUs (20 layers per GPU), the paper compresses KV cache chunks independently per GPU. On MATH-500, the reported accuracy declines from 75.6 (vanilla) to 74.4 at 10× and 72.6 at 20×, with standard errors near ~1.9. NIAH and LITM remain at 100.0 for all tested ratios in that table. The paper notes that joint compression across chunks could improve accuracy for some offload scenarios but is not required for feasibility, highlighting an engineering trade-off between deployment simplicity in distributed settings and optimal global compression. Latency and TTFT economics A critical system result is the measured compression/decompression latency on an H100 for a non-fused implementation. For Mistral NeMo 12B in bfloat16: BS=8, CTX=8K: compression 379ms, decompression 267ms; vanilla recompute TTFT 3098ms; kvtc decompression TTFT 380ms BS=2, CTX=16K: compression 194ms, decompression 143ms; vanilla recompute TTFT 1780ms; kvtc decompression TTFT 208ms These measurements imply that, when a cache would otherwise be recomputed, decompressing a stored compressed cache can reduce TTFT by ~8×–9× in these scenarios, even without kernel fusion. The decomposition of runtime shows PCA projection and entropy coding as the largest contributors, implying that GPU-optimized kernels and faster GPU-native lossless codecs could reduce overhead further. The fundamental economic conclusion is that, in multi-turn settings with long prefixes, compression-induced overhead is likely dominated by the avoided prefill compute and avoided transfer overhead for uncompressed caches. KEY DEPLOYMENT-SENSITIVE DESIGN CHOICES AND FAILURE MODES Several design choices appear to be “hard requirements” rather than optional optimizations: Sink tokens and sliding window exclusions The paper’s ablations show that compressing early “sink” tokens can catastrophically degrade accuracy at high compression ratios (example: Llama 3.1 8B at 64× collapses on multiple tasks when sink tokens are compressed). Similarly, compressing the most recent tokens hurts performance, motivating a sliding window (default 128 tokens) that remains uncompressed. This introduces a predictable engineering constraint: kvtc is not a uniform compression of the full cache; it is a policy-driven, token-position-dependent codec. Production integration therefore requires correct handling of token positions, attention sinks, and window management, and these policies must be aligned with attention-kernel behavior and model-specific sink dynamics. RoPE handling Removing positional rotations prior to compression is described as important for preserving low-rank structure. In deployment, this implies that the codec must be position-aware and must invert and reapply RoPE correctly. This is an additional source of complexity relative to pure per-token quantization and is sensitive to model variants and RoPE parameterizations. Calibration set representativeness The method’s quality hinges on the PCA basis generalizing from calibration data to production data. The paper demonstrates relative stability with 160K–200K calibration tokens and explores domain shifts (general web text vs math traces vs code). Results suggest that moderate domain mismatch is tolerated at 16×–64×, while extreme compression (e.g., 256× in ablations) becomes materially more sensitive to calibration choice. In production, this implies that operators targeting the “negligible degradation” regime should be able to calibrate with broadly representative corpora, while operators targeting ultra-high compression for specialized workloads should expect tighter coupling between calibration domain and achieved quality. PCA matrix storage overhead and operational footprint A non-trivial hidden cost is the need to store PCA projection matrices per model. The paper reports that, prior to DP trimming, PCA matrices stored at 16-bit can amount to a meaningful fraction of model parameter count (examples reported: ~2.4% for Llama 3.3 70B, ~8.7% for Llama 3.1 8B). This overhead is amortized across all cached sessions for a model but competes with HBM/DRAM budgets in multi-model serving. DP-driven trimming can reduce this overhead at higher compression ratios by removing zero-bit components, but the directionality is not guaranteed at low compression ratios if many components remain active. In distributed inference (pipeline parallel), per-chunk PCA can reduce matrix sizes, but may reduce cross-layer decorrelation benefits if fewer layers are concatenated. SYSTEM-LEVEL IMPLICATIONS FOR GENERATIVE AI INFRASTRUCTURE GPU AND HBM The principal infrastructure implication is that KV cache compression at storage time targets the dominant memory allocator stressor in stateful serving: the accumulation of idle or warm conversation state. For workloads with long reusable prefixes (code assistants, enterprise agents with large system prompts, repeated RAG scaffolds, document chat), the limiting resource frequently becomes HBM reserved for KV caches rather than compute. By compressing stale caches by ~20× (or more), the same HBM budget can retain a materially larger working set of cached prefixes, increasing cache hit rates and reducing recomputation. This effect is multiplicative with cache-aware routing and prefix sharing: more prefixes can remain resident (hot or warm) and can be routed to nodes that already hold them, improving both throughput and tail latency. However, kvtc as described does not reduce the active KV cache footprint during the actual attention computation for a currently decoding sequence, because the model operates on decompressed KV caches during decoding. Therefore, the method does not directly reduce HBM bandwidth consumed by attention kernels during steady-state decode, and does not directly address the “memory traffic per generated token” bottleneck that motivates online KV quantization and eviction strategies. The primary HBM benefit is increased effective capacity for caches between turns and reduced HBM pressure from storing many idle sessions, not reduced per-token decode bandwidth. Compression and decompression themselves consume GPU compute and memory bandwidth. The measured decompression TTFT of ~208ms–380ms in the provided benchmarks indicates that the overhead is real but can be materially smaller than recomputation of long prefixes. In an HBM-constrained serving environment, this overhead can be interpreted as a trade between (a) maintaining more caches warm and paying decompression on reuse versus (b) evicting caches and paying full prefill recomputation. The decision boundary will depend on distribution of inter-turn idle times, probability of reuse, and SLA sensitivity to TTFT. kvtc expands the feasible region where keeping caches is economically rational, especially for long prompts. CPU AND DRAM The method implies a stronger role for CPU DRAM as a warm KV cache tier. A ~20× compression ratio changes the practical scale of “warm state” that can be stored per server. Using the paper’s reported KV cache sizes, a 10K-token 16-bit KV cache for Llama 3.3 70B is ~3.13GiB; compressing by ~20× would reduce this to ~160MiB. At that size, storing hundreds to thousands of warm conversation states in DRAM becomes materially more feasible, increasing cache hit rates and reducing NVMe dependence. This can shift system design from “HBM-only hot caches with aggressive eviction” toward “HBM hot + DRAM warm with long retention,” which is structurally analogous to CPU page cache hierarchies in classical systems design. CPU compute implications depend on where compression is executed. The paper explicitly allows compression on CPU if the cache is already in storage, but the strongest bandwidth savings are achieved when compression happens before moving KV caches off the GPU. If an operator chooses GPU-side compression prior to PCIe/NVLink transfer, CPU compute overhead is modest (orchestrating and DP calibration offline). If an operator instead transfers uncompressed caches to CPU for compression, bandwidth savings are forfeited and CPU memory bandwidth becomes a bottleneck. Therefore, the most economically coherent deployment path is GPU-native compression/decompression with CPU DRAM used as the warm storage reservoir.

$NVDA $MU $SNDK $LITE PAPER OVERVIEW AND CORE CLAIMS The paper “KV Cache Transform Coding for Compact Storage in LLM Inference” introduces kvtc, a transform-coding pipeline that compresses transformer key-value (KV) caches primarily for storage and transfer in LLM serving, rather than for accelerating the per-token attention kernel during active decoding. The method combines 3 stages: (1) feature decorrelation via a PCA basis computed from a calibration dataset and reused across requests; (2) adaptive, variable-precision quantization with bit allocation solved via dynamic programming (DP), including groupwise scaling/shift overhead; and (3) lossless entropy coding (DEFLATE via nvCOMP in the reference implementation) to exploit residual redundancy after quantization. The central empirical claim is that KV tensors contain large, exploitable redundancy across heads and layers, enabling approximately 20× compression versus a 16-bit baseline with negligible degradation across a broad set of accuracy and long-context benchmarks, with materially higher compression (≥40×) available at modest quality cost in some regimes. The system claim is that such compression materially improves the economics of multi-turn, prefix-reuse serving by extending effective KV cache capacity in GPU HBM and host tiers (DRAM/NVMe) and by reducing inter-node and GPU↔host bandwidth demands, thereby improving cache hit rates and reducing time-to-first-token (TTFT) relative to recomputation when caches would otherwise be evicted. KV CACHE AS THE DOMINANT STATE VARIABLE IN INFERENCE ECONOMICS KV cache growth is linear in context length and is multiplicative in layers and attention heads, making it an increasingly dominant constraint as (a) context lengths expand, (b) models add layers and maintain large hidden dimensions, and (c) production workloads shift toward iterative and tool-augmented interactions that repeatedly reuse long prefixes. The paper uses the canonical 16-bit KV cache size formula (4·l·h·d_head·t) bytes and reports 16-bit KV cache sizes per 1K tokens of context that are already operationally large: 128MiB for Llama 3.1 8B, 160MiB for Mistral NeMo 12B, and 320MiB for Llama 3.3 70B Instruct. In binary units, these figures imply per-token KV footprints of 128KiB/token (Llama 3.1 8B), 160KiB/token (Mistral NeMo 12B), and 320KiB/token (Llama 3.3 70B Instruct) at 16-bit. For a 10K-token prompt (10×1K in the paper’s binary convention), the 16-bit KV cache sizes scale to approximately 1.25GiB (Llama 3.1 8B), 1.56GiB (Mistral NeMo 12B), and 3.13GiB (Llama 3.3 70B Instruct). These magnitudes explain why stale caches create a throughput–latency dilemma: retaining them in HBM maximizes responsiveness on future turns but crowds out concurrent sessions; evicting them forces quadratic-cost prefill recomputation and increases TTFT; offloading them to host or storage introduces large transfer overhead and consumes DRAM/NVMe capacity. A key operational nuance emphasized is that modern serving stacks increasingly treat KV caches as a database, leveraging block paging and shared-prefix reuse. In the common disaggregated serving design (separate prefill and decode nodes), KV cache transfer becomes a dominant category of cross-node traffic. Under that design, any reduction in KV cache size directly increases effective fabric capacity and reduces tail latency attributable to congestion, while also enabling longer cache lifetimes in “hot” (HBM) and “warm” (CPU DRAM) tiers that raise cache hit rates and reduce recomputation frequency. The paper’s quantitative example illustrates the economic stakes: a 1,000-line code file tokenized at ~10 tokens/line yields ~10K tokens; for Llama 3.3 70B, an 8-bit KV cache for that context is ~1.6GiB. Reuse across subsequent turns or parallel chats around the same file is valuable, but HBM scarcity makes retaining many such caches infeasible without compression. TECHNICAL MECHANISM: WHY KV CACHES ARE COMPRESSIBLE AND HOW KVTC EXPLOITS IT The technical rationale begins with an empirical observation: keys (and, to a lesser extent, values) across different attention heads can be aligned into a shared latent space using orthogonal transformations (Procrustes alignment). This supports the hypothesis that head-specific projections introduce rotations of a common subspace rather than completely distinct information, implying that concatenating across heads and layers should reveal low-rank structure suitable for linear decorrelation and dimensionality reduction. The method operationalizes this using a PCA/SVD basis learned from calibration data rather than recomputing a decomposition per prompt. This design choice targets production viability: per-prompt SVD is computationally expensive and scales poorly with long prompts and frequent cache updates. kvtc is explicitly structured as an offline-calibrated, online-applied codec: Calibration (performed 1 time per model and compression setting for DP allocation) A calibration dataset is forwarded through the model to collect KV caches. Token positions are pooled, and a subset of positions is sampled. Keys and values are processed separately. Several implementation choices are highlighted as decisive for stability: Rotary positional embeddings are effectively removed prior to compression (“undo positional rotations”), because positional rotations degrade the apparent low-rank structure of keys. “Attention sink” tokens (the earliest tokens in the sequence) and a sliding window of most recent tokens are excluded from compression because they disproportionately affect attention patterns and are empirically more sensitive to reconstruction error. Cross-layer concatenation is used: keys (or values) from multiple layers and heads at the same token position are concatenated along the feature axis to form a higher-dimensional feature vector. PCA is computed over these concatenated vectors, improving robustness relative to per-layer or per-head PCA. The PCA basis is computed via SVD of centered calibration data, using randomized SVD for scalability with a target rank cutoff. The paper reports calibration regimes of 160K tokens for several models with a 10K PCA dimension cutoff (8K for Qwen variants with fewer KV heads), selected to fit within a single 80GB H100 memory envelope and complete within minutes. A critical economic detail is that the same PCA basis can be reused across multiple compression ratios; only the DP-derived precision assignment changes per compression target. Compression (applied between inference phases) Compression operates on stored KV cache tensors, not on weights, and does not modify attention computation. The KV cache is projected into the PCA basis, quantized, packed, and then entropy-coded. Compression is positioned as a background or between-phase operation (after decoding, or between prefill and decode), executed on GPU or CPU depending on where the cache currently resides. The design intent is that compression should not sit on the critical per-token decoding path; it is a storage and transport optimization. Decompression (performed prior to reuse) Decompression reverses the entropy coding and quantization and applies the inverse PCA projection. A practical latency optimization is proposed: inverse projection can be performed layer-by-layer using submatrices of the PCA basis, allowing generation to begin before the full cache is reconstructed, reducing TTFT. Quantization and bit allocation are the core differentiators versus simpler PCA truncation. PCA provides ordered components by variance; kvtc uses DP to allocate a global bit budget across PCA coordinates (and across groups of coordinates) to minimize reconstruction error in the decorrelated domain. Groups of subsequent PCA coordinates share 16-bit shift and scale factors (a microscaling-inspired design), and the DP algorithm jointly selects group size and precision type under a bit budget, including the overhead of per-group metadata. DP commonly assigns 0 bits to many trailing PCA components, which both increases compression and provides a mechanism to trim the PCA basis to the subset of components that actually carry payload, reducing compute and storage overhead of the projection matrices in deployment. Lossless entropy coding then exploits the structure induced by quantization. DEFLATE is used in the reference implementation, and the paper emphasizes that the incremental gain from the lossless stage is content-dependent but meaningful, with an average uplift of ~1.23× on top of quantization in the reported regime. An ablation in the appendices indicates that GPU-friendly variants (GDeflate) can achieve nearly identical compression ratios (≤0.1 difference in measured cases), implying that throughput-optimized lossless codecs can likely be substituted without sacrificing meaningful compression. EMPIRICAL RESULTS: ACCURACY, COMPRESSION, AND LATENCY General-purpose 8B–12B dense models The paper evaluates Llama 3.1 8B, MN-Minitron 8B, and Mistral NeMo 12B across math/knowledge (GSM8K, MMLU) and long-context tasks (Qasper, Lost in the Middle, RULER Variable Tracking) under a simulated multi-turn regime where compression/decompression is applied periodically, with a sliding window of recent tokens excluded. A consistent pattern appears: kvtc maintains near-vanilla performance through 16× compression settings, and remains competitive at 32×, with degradation becoming task- and model-dependent at 64×, particularly on long-context retrieval metrics when compression is pushed aggressively. Selected quantitative anchor points from the paper’s standard-error table (all values are reported with the paper’s evaluation setup and token-window exclusions): Llama 3.1 8B Vanilla: GSM8K 56.8, MMLU 60.5, Qasper 40.4, LITM 99.4, RULER-VT 99.8 kvtc16×: GSM8K 56.9, MMLU 60.1, Qasper 40.7, LITM 99.3, RULER-VT 99.1 kvtc32×: GSM8K 57.8, MMLU 60.6, Qasper 39.4, LITM 99.1, RULER-VT 98.9 kvtc64×: GSM8K 57.2, MMLU 60.7, Qasper 37.8, LITM 90.2, RULER-VT 95.9 These results indicate that, for this model, long-context sensitivity emerges at 64× with meaningful drops in LITM and RULER-VT, while math/knowledge scores remain stable, implying a differential sensitivity consistent with key-vector precision being more critical for retrieval-style behavior. Mistral NeMo 12B Vanilla: GSM8K 61.9, MMLU 64.5, Qasper 38.4, LITM 99.5, RULER-VT 99.8 kvtc16×: GSM8K 62.0, MMLU 64.4, Qasper 37.6, LITM 99.8, RULER-VT 99.5 kvtc32×: GSM8K 62.2, MMLU 63.8, Qasper 37.5, LITM 99.6, RULER-VT 98.7 kvtc64×: GSM8K 61.9, MMLU 61.4, Qasper 38.0, LITM 95.3, RULER-VT 98.0 Here, degradation at 64× is visible but materially smaller than the Llama 3.1 8B LITM drop, suggesting model-architecture or training-data differences can change the tolerance envelope for aggressive KV cache distortion. MN-Minitron 8B Vanilla: GSM8K 59.1, MMLU 64.3, Qasper 38.2, LITM 99.8, RULER-VT 99.4 kvtc16×: GSM8K 60.3, MMLU 64.1, Qasper 38.6, LITM 99.3, RULER-VT 98.8 kvtc32×: GSM8K 59.1, MMLU 63.7, Qasper 37.7, LITM 86.9, RULER-VT 96.0 kvtc64×: GSM8K 57.8, MMLU 62.1, Qasper 38.1, LITM 59.5, RULER-VT 93.4 This model shows markedly higher sensitivity on LITM at 32× and 64×, despite stable short-context metrics, reinforcing that “compression safety” is not monotonic in parameter count and that pruning/distillation choices can alter KV cache redundancy or robustness. Comparisons to baselines The paper compares kvtc to quantization baselines (KIVI, GEAR, FP8) and eviction baselines (H2O, TOVA), plus an SVD-based prefill-optimization method (xKV). Across the reported tasks: Low-bit quantization methods at modest compression (2-bit KV schemes) show earlier degradation in long-context behavior than kvtc at substantially higher compression settings. Eviction methods perform poorly as generic compressors for long-context tasks, consistent with their objective function (selective pruning) being misaligned with “lossless-ish storage for reuse.” xKV shows competitive results on some tasks but a consistent underperformance on Qasper relative to kvtc and vanilla in the provided tables, consistent with method-specific distortions introduced by its decomposition regime. Reasoning models and high-variance tasks For DeepSeek-R1-distilled Qwen 2.5 reasoning models, the paper evaluates AIME 2024/2025 and LiveCodeBench coding. Results are averaged over 8 runs with large variance, but a key inference is that kvtc at ~9×–21× compression achieves broadly similar AIME scores within variance bands, while coding performance remains stable at ~9× and degrades more visibly at ~18×–21× on the 7B model. An important nuance is that smaller reasoning models already have smaller KV footprints (reported ~29KiB/token for Qwen R1 1.5B versus 131KiB/token for Llama 3.1 8B), so the economic value of aggressive KV cache compression is proportionally higher for large models and long contexts than for small models with short contexts, unless the serving system’s bottleneck is dominated by cache transfer rather than HBM capacity. Multi-GPU inference and pipeline parallel For Llama 3.3 70B Instruct run pipeline-parallel across 4 GPUs (20 layers per GPU), the paper compresses KV cache chunks independently per GPU. On MATH-500, the reported accuracy declines from 75.6 (vanilla) to 74.4 at 10× and 72.6 at 20×, with standard errors near ~1.9. NIAH and LITM remain at 100.0 for all tested ratios in that table. The paper notes that joint compression across chunks could improve accuracy for some offload scenarios but is not required for feasibility, highlighting an engineering trade-off between deployment simplicity in distributed settings and optimal global compression. Latency and TTFT economics A critical system result is the measured compression/decompression latency on an H100 for a non-fused implementation. For Mistral NeMo 12B in bfloat16: BS=8, CTX=8K: compression 379ms, decompression 267ms; vanilla recompute TTFT 3098ms; kvtc decompression TTFT 380ms BS=2, CTX=16K: compression 194ms, decompression 143ms; vanilla recompute TTFT 1780ms; kvtc decompression TTFT 208ms These measurements imply that, when a cache would otherwise be recomputed, decompressing a stored compressed cache can reduce TTFT by ~8×–9× in these scenarios, even without kernel fusion. The decomposition of runtime shows PCA projection and entropy coding as the largest contributors, implying that GPU-optimized kernels and faster GPU-native lossless codecs could reduce overhead further. The fundamental economic conclusion is that, in multi-turn settings with long prefixes, compression-induced overhead is likely dominated by the avoided prefill compute and avoided transfer overhead for uncompressed caches. KEY DEPLOYMENT-SENSITIVE DESIGN CHOICES AND FAILURE MODES Several design choices appear to be “hard requirements” rather than optional optimizations: Sink tokens and sliding window exclusions The paper’s ablations show that compressing early “sink” tokens can catastrophically degrade accuracy at high compression ratios (example: Llama 3.1 8B at 64× collapses on multiple tasks when sink tokens are compressed). Similarly, compressing the most recent tokens hurts performance, motivating a sliding window (default 128 tokens) that remains uncompressed. This introduces a predictable engineering constraint: kvtc is not a uniform compression of the full cache; it is a policy-driven, token-position-dependent codec. Production integration therefore requires correct handling of token positions, attention sinks, and window management, and these policies must be aligned with attention-kernel behavior and model-specific sink dynamics. RoPE handling Removing positional rotations prior to compression is described as important for preserving low-rank structure. In deployment, this implies that the codec must be position-aware and must invert and reapply RoPE correctly. This is an additional source of complexity relative to pure per-token quantization and is sensitive to model variants and RoPE parameterizations. Calibration set representativeness The method’s quality hinges on the PCA basis generalizing from calibration data to production data. The paper demonstrates relative stability with 160K–200K calibration tokens and explores domain shifts (general web text vs math traces vs code). Results suggest that moderate domain mismatch is tolerated at 16×–64×, while extreme compression (e.g., 256× in ablations) becomes materially more sensitive to calibration choice. In production, this implies that operators targeting the “negligible degradation” regime should be able to calibrate with broadly representative corpora, while operators targeting ultra-high compression for specialized workloads should expect tighter coupling between calibration domain and achieved quality. PCA matrix storage overhead and operational footprint A non-trivial hidden cost is the need to store PCA projection matrices per model. The paper reports that, prior to DP trimming, PCA matrices stored at 16-bit can amount to a meaningful fraction of model parameter count (examples reported: ~2.4% for Llama 3.3 70B, ~8.7% for Llama 3.1 8B). This overhead is amortized across all cached sessions for a model but competes with HBM/DRAM budgets in multi-model serving. DP-driven trimming can reduce this overhead at higher compression ratios by removing zero-bit components, but the directionality is not guaranteed at low compression ratios if many components remain active. In distributed inference (pipeline parallel), per-chunk PCA can reduce matrix sizes, but may reduce cross-layer decorrelation benefits if fewer layers are concatenated. SYSTEM-LEVEL IMPLICATIONS FOR GENERATIVE AI INFRASTRUCTURE GPU AND HBM The principal infrastructure implication is that KV cache compression at storage time targets the dominant memory allocator stressor in stateful serving: the accumulation of idle or warm conversation state. For workloads with long reusable prefixes (code assistants, enterprise agents with large system prompts, repeated RAG scaffolds, document chat), the limiting resource frequently becomes HBM reserved for KV caches rather than compute. By compressing stale caches by ~20× (or more), the same HBM budget can retain a materially larger working set of cached prefixes, increasing cache hit rates and reducing recomputation. This effect is multiplicative with cache-aware routing and prefix sharing: more prefixes can remain resident (hot or warm) and can be routed to nodes that already hold them, improving both throughput and tail latency. However, kvtc as described does not reduce the active KV cache footprint during the actual attention computation for a currently decoding sequence, because the model operates on decompressed KV caches during decoding. Therefore, the method does not directly reduce HBM bandwidth consumed by attention kernels during steady-state decode, and does not directly address the “memory traffic per generated token” bottleneck that motivates online KV quantization and eviction strategies. The primary HBM benefit is increased effective capacity for caches between turns and reduced HBM pressure from storing many idle sessions, not reduced per-token decode bandwidth. Compression and decompression themselves consume GPU compute and memory bandwidth. The measured decompression TTFT of ~208ms–380ms in the provided benchmarks indicates that the overhead is real but can be materially smaller than recomputation of long prefixes. In an HBM-constrained serving environment, this overhead can be interpreted as a trade between (a) maintaining more caches warm and paying decompression on reuse versus (b) evicting caches and paying full prefill recomputation. The decision boundary will depend on distribution of inter-turn idle times, probability of reuse, and SLA sensitivity to TTFT. kvtc expands the feasible region where keeping caches is economically rational, especially for long prompts. CPU AND DRAM The method implies a stronger role for CPU DRAM as a warm KV cache tier. A ~20× compression ratio changes the practical scale of “warm state” that can be stored per server. Using the paper’s reported KV cache sizes, a 10K-token 16-bit KV cache for Llama 3.3 70B is ~3.13GiB; compressing by ~20× would reduce this to ~160MiB. At that size, storing hundreds to thousands of warm conversation states in DRAM becomes materially more feasible, increasing cache hit rates and reducing NVMe dependence. This can shift system design from “HBM-only hot caches with aggressive eviction” toward “HBM hot + DRAM warm with long retention,” which is structurally analogous to CPU page cache hierarchies in classical systems design. CPU compute implications depend on where compression is executed. The paper explicitly allows compression on CPU if the cache is already in storage, but the strongest bandwidth savings are achieved when compression happens before moving KV caches off the GPU. If an operator chooses GPU-side compression prior to PCIe/NVLink transfer, CPU compute overhead is modest (orchestrating and DP calibration offline). If an operator instead transfers uncompressed caches to CPU for compression, bandwidth savings are forfeited and CPU memory bandwidth becomes a bottleneck. Therefore, the most economically coherent deployment path is GPU-native compression/decompression with CPU DRAM used as the warm storage reservoir.

TheValueist

16,549 次观看 • 6 个月前

🔴 I listen to the pro-regime side a lot. That means I do not just painfully listen through hours of what the regime lobbyists talk about in podcasts and interviews, but also to what the Woke Right says. I listen to their lies and try to get an understanding of the truth. Remember: Propaganda is always a mix of both. I listen to projection, deflection, and try to detect which accusation is a confession. Sometimes it's one word. Between two hours of coordinated propaganda, the truth seems to slip through. One word that caught my attention, that nobody else listening to this might have caught. In his recent interview with Russian agent "America is a cancer," Elizabeth Lane (or whatever her real name is), Tucker has a moment where he talks about how disappointed he is that he couldn't deliver on his (assigned) job: to prevent Trump, through his personal relationship and manipulation, from starting a war with the Islamic Republic. Tucker failed, and he is still not over it. He has talked about it multiple times and always displays how this personal failure gets to him. Then Tucker explains what he thinks Trump's plan or vision was when he started the war: kill the Ayatollah, the regime would collapse, and then a pro-Western leader - he corrects himself, "king or something" - would be in Iran. You tell me who Tucker was referring to when describing Trump's vision for a free Iran. I take from this detail the following things: ▪️Trump is aware of the Crown Prince Reza Pahlavi's popularity and massive political base inside Iran. He is aware that a free Iran after elections will include the Crown Prince leading Iran. And he was counting on that part of the equation when he started the war to be the outcome. ▪️All of the noise the regime lobby made up - that Trump called him the "loser prince," that Trump doesn't like the Crown Prince, doesn't take him seriously - we all knew that it was regime propaganda, but I want to reiterate that it was, and continues to be, all just lies. ▪️The part that remains open in my head to this day is: what changed? How did people inside the White House (Vance including regime lobbyists as Vance's friends and advisers) change Trump's mind? Regarding the rest, here is my assessment: ▪️Everything they describe here that the pro-Iran side is doing (vs. the pro-regime side they are both on) is what THEY are doing themselves. They had placed certain "operatives" in Trump's orbit, and they thought they could control/manipulate Trump and put him in The Truman Show. ▪️They did fail in part. Trump did and does listen to people like the late Lindsey Graham, Mark R. Levin, and Laura Loomer. They keep naming these people specifically over and over again. They hate that they can't get rid of them. They are all glad that Lindsey Graham is no longer with us. ▪️They do mention Charlie Kirk as well - as part of "their allegiance". I could write a novel about how I think all of this fits together, what truly happened and what didn't. I did write a long text explaining it, but I deleted it again. It's a different topic for a different time. I'll just repeat the obvious: Nobody has profited politically more from Charlie's death than the Woke Right. And again: Russia, the regime, and the Woke Right are all joining forces to get Tyler Robinson to walk free. Nobody is trying harder to save the man who killed Charlie by blaming everyone else (especially Israel) for his death. They put the deflection campaign out there one hour after his death (narrative control). ▪️There is one part of the interview where the Russian agent talks about how you can control Trump (anyone, for that matter) by the intel you present him with and the decisions that person makes based on that intel. I agree with her 100%. It is what I've been saying the entire time: This is a war of information, with some people surrounding Trump telling him the truth and these people surrounding Trump manipulating him in favor of saving the regime. ▪️Tucker emphasizes again how mad he is that he/they can no longer feed Trump false intel ("he lives in a biosphere where no new information comes in"). Their frustration about losing this power to some degree is a good sign. ▪️Tucker also put Marco Rubio on that list of people he considers problematic. After naming Mark Levin, he directly names Rubio. My observation is that they left Rubio out of their 'campaign of taking people down' until they saw that Vance was losing support and Rubio was gaining it instead. Now the regime's media is producing LEGO videos targeting Rubio. The Woke Right will, this is my guess, start escalating its attacks against Rubio moving forward. ▪️Why? Because they are all politically united and aligned behind the following things: 1. blaming Israel for everything, 2. making Vance (their guy) president in 2028, and 3. saving the Islamic Republic.

🔴 I listen to the pro-regime side a lot. That means I do not just painfully listen through hours of what the regime lobbyists talk about in podcasts and interviews, but also to what the Woke Right says. I listen to their lies and try to get an understanding of the truth. Remember: Propaganda is always a mix of both. I listen to projection, deflection, and try to detect which accusation is a confession. Sometimes it's one word. Between two hours of coordinated propaganda, the truth seems to slip through. One word that caught my attention, that nobody else listening to this might have caught. In his recent interview with Russian agent "America is a cancer," Elizabeth Lane (or whatever her real name is), Tucker has a moment where he talks about how disappointed he is that he couldn't deliver on his (assigned) job: to prevent Trump, through his personal relationship and manipulation, from starting a war with the Islamic Republic. Tucker failed, and he is still not over it. He has talked about it multiple times and always displays how this personal failure gets to him. Then Tucker explains what he thinks Trump's plan or vision was when he started the war: kill the Ayatollah, the regime would collapse, and then a pro-Western leader - he corrects himself, "king or something" - would be in Iran. You tell me who Tucker was referring to when describing Trump's vision for a free Iran. I take from this detail the following things: ▪️Trump is aware of the Crown Prince Reza Pahlavi's popularity and massive political base inside Iran. He is aware that a free Iran after elections will include the Crown Prince leading Iran. And he was counting on that part of the equation when he started the war to be the outcome. ▪️All of the noise the regime lobby made up - that Trump called him the "loser prince," that Trump doesn't like the Crown Prince, doesn't take him seriously - we all knew that it was regime propaganda, but I want to reiterate that it was, and continues to be, all just lies. ▪️The part that remains open in my head to this day is: what changed? How did people inside the White House (Vance including regime lobbyists as Vance's friends and advisers) change Trump's mind? Regarding the rest, here is my assessment: ▪️Everything they describe here that the pro-Iran side is doing (vs. the pro-regime side they are both on) is what THEY are doing themselves. They had placed certain "operatives" in Trump's orbit, and they thought they could control/manipulate Trump and put him in The Truman Show. ▪️They did fail in part. Trump did and does listen to people like the late Lindsey Graham, Mark R. Levin, and Laura Loomer. They keep naming these people specifically over and over again. They hate that they can't get rid of them. They are all glad that Lindsey Graham is no longer with us. ▪️They do mention Charlie Kirk as well - as part of "their allegiance". I could write a novel about how I think all of this fits together, what truly happened and what didn't. I did write a long text explaining it, but I deleted it again. It's a different topic for a different time. I'll just repeat the obvious: Nobody has profited politically more from Charlie's death than the Woke Right. And again: Russia, the regime, and the Woke Right are all joining forces to get Tyler Robinson to walk free. Nobody is trying harder to save the man who killed Charlie by blaming everyone else (especially Israel) for his death. They put the deflection campaign out there one hour after his death (narrative control). ▪️There is one part of the interview where the Russian agent talks about how you can control Trump (anyone, for that matter) by the intel you present him with and the decisions that person makes based on that intel. I agree with her 100%. It is what I've been saying the entire time: This is a war of information, with some people surrounding Trump telling him the truth and these people surrounding Trump manipulating him in favor of saving the regime. ▪️Tucker emphasizes again how mad he is that he/they can no longer feed Trump false intel ("he lives in a biosphere where no new information comes in"). Their frustration about losing this power to some degree is a good sign. ▪️Tucker also put Marco Rubio on that list of people he considers problematic. After naming Mark Levin, he directly names Rubio. My observation is that they left Rubio out of their 'campaign of taking people down' until they saw that Vance was losing support and Rubio was gaining it instead. Now the regime's media is producing LEGO videos targeting Rubio. The Woke Right will, this is my guess, start escalating its attacks against Rubio moving forward. ▪️Why? Because they are all politically united and aligned behind the following things: 1. blaming Israel for everything, 2. making Vance (their guy) president in 2028, and 3. saving the Islamic Republic.

diana bloom

16,535 次观看 • 28 天前

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,793 次观看 • 4 个月前

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 次观看 • 3 个月前

🚨 MUST WATCH: Senator Rand Paul and I talk about Google, defamation, AI lies + more. Fabricated crimes, victims, evidence and more. Rand Paul doesn’t mince words: "I think you’re going to kill them in court" "They’ve convinced me they have no decency and no moral compass" "A week before an election, a fake video could destroy someone" If Big Tech can do what we discuss here, this isn’t just a Robby or Rand problem, it’s a problem for everyone because you could be next. Overview: For 2+ years, Google's AI has accused me of sexual assault, child rape, robberies, shootings, selling drugs, abuse and much more — crimes that I’ve never been accused of, charged with, or convicted of in my life. It told people detailed lies when they asked for my bio. It fabricated fake court records, fake police reports, fake victim statements, fake articles from major media outlets, even fake 404-linked "sources" from major media outlets that it would then hallucinate entire stories for if challenged about the page going to a 404 link. I notified Google repeatedly, directly and through lawyers to their legal team, and they didn’t stop it. The lies continued, reached over two million people according to their own AI, and the damage has been massive, including people in real life confronting me thinking these lies were true. Senator Rand Paul went through something eerily similar. A video on YouTube accused him of taking stolen money from Nicolás Maduro, essentially treason, which is punishable by death. It looked like a legit news broadcast. He and his office notified Google/YouTube multiple times; and they refused to remove it, saying (according to Sen. Paul) "we don't adjudicate the truth." Yet when Rand said cloth masks didn’t work for COVID (a view now widely accepted), they took his content down. Selective enforcement. And now Rand says he has received death threats. Key timecodes to jump to: 2:15 Rand: "What Google did is criminal" 2:30 The Section 230 Problem 4:00 Why This Case Is Different — "They’re not hosting content, they’re CREATING the content" - Paul explains why AI-generated defamation destroys Section 230 protection and why Starbuck’s case is "incredibly strong" 6:00 Paul: "I’ve been against changing 230, but now I think maybe…" 9:20 The nightmare scenario: deepfakes dropped right before a tight election. 10:00 Rand Paul: "I think you are going to kill them in court because…" 13:05 Rand reveals threats he’s received.. 13:22 Fat people are protected while we aren’t protected from blatant lies. 14:00 Robby reveals: Insurance companies DENIED him coverage recently. 17:17 Rand wrestles with the implications: How do we prevent fake AI videos from destroying elections? What happens when you can’t tell what’s real anymore? "This is Pandora’s box" 18:00 Rand defends memes! 18:40 Paul describes Google’s insulting response. 19:00 Call for Senate Hearings — Paul commits to investigating: "I happen to know somebody who’s a chairman of a committee and I think he might be persuaded to look at your case" 24:23 Rand can’t believe Google would risk a jury seeing this. 25:50 "There’s seemingly no going back" 27:51 Rand: "They took down my Senate floor speech which is protected by the Constitution… That’s how arrogant these people are" 29:00 Starbuck reminds Paul of their Big Tech disagreement years ago. Paul admits: "That’s part of learning… My opinion has shifted…" Watch & share. Feel free to clip and use whatever you want for your own social pages. I give you permission!

🚨 MUST WATCH: Senator Rand Paul and I talk about Google, defamation, AI lies + more. Fabricated crimes, victims, evidence and more. Rand Paul doesn’t mince words: "I think you’re going to kill them in court" "They’ve convinced me they have no decency and no moral compass" "A week before an election, a fake video could destroy someone" If Big Tech can do what we discuss here, this isn’t just a Robby or Rand problem, it’s a problem for everyone because you could be next. Overview: For 2+ years, Google's AI has accused me of sexual assault, child rape, robberies, shootings, selling drugs, abuse and much more — crimes that I’ve never been accused of, charged with, or convicted of in my life. It told people detailed lies when they asked for my bio. It fabricated fake court records, fake police reports, fake victim statements, fake articles from major media outlets, even fake 404-linked "sources" from major media outlets that it would then hallucinate entire stories for if challenged about the page going to a 404 link. I notified Google repeatedly, directly and through lawyers to their legal team, and they didn’t stop it. The lies continued, reached over two million people according to their own AI, and the damage has been massive, including people in real life confronting me thinking these lies were true. Senator Rand Paul went through something eerily similar. A video on YouTube accused him of taking stolen money from Nicolás Maduro, essentially treason, which is punishable by death. It looked like a legit news broadcast. He and his office notified Google/YouTube multiple times; and they refused to remove it, saying (according to Sen. Paul) "we don't adjudicate the truth." Yet when Rand said cloth masks didn’t work for COVID (a view now widely accepted), they took his content down. Selective enforcement. And now Rand says he has received death threats. Key timecodes to jump to: 2:15 Rand: "What Google did is criminal" 2:30 The Section 230 Problem 4:00 Why This Case Is Different — "They’re not hosting content, they’re CREATING the content" - Paul explains why AI-generated defamation destroys Section 230 protection and why Starbuck’s case is "incredibly strong" 6:00 Paul: "I’ve been against changing 230, but now I think maybe…" 9:20 The nightmare scenario: deepfakes dropped right before a tight election. 10:00 Rand Paul: "I think you are going to kill them in court because…" 13:05 Rand reveals threats he’s received.. 13:22 Fat people are protected while we aren’t protected from blatant lies. 14:00 Robby reveals: Insurance companies DENIED him coverage recently. 17:17 Rand wrestles with the implications: How do we prevent fake AI videos from destroying elections? What happens when you can’t tell what’s real anymore? "This is Pandora’s box" 18:00 Rand defends memes! 18:40 Paul describes Google’s insulting response. 19:00 Call for Senate Hearings — Paul commits to investigating: "I happen to know somebody who’s a chairman of a committee and I think he might be persuaded to look at your case" 24:23 Rand can’t believe Google would risk a jury seeing this. 25:50 "There’s seemingly no going back" 27:51 Rand: "They took down my Senate floor speech which is protected by the Constitution… That’s how arrogant these people are" 29:00 Starbuck reminds Paul of their Big Tech disagreement years ago. Paul admits: "That’s part of learning… My opinion has shifted…" Watch & share. Feel free to clip and use whatever you want for your own social pages. I give you permission!

Robby Starbuck

105,539 次观看 • 6 个月前

The following is a message from Diana Bloom, an anonymous Iranian activist living in Germany who is being threatened with potentially extremely dangerous "exposure" by what appears to be a lunatic. I am passing this message on since Diana has deleted her account for her own protection and safety. To note, I don't think I should be involved with this crazy Evil Evi woman or her OSINT dramas whatsoever since she went bonkers the other day, but she is acting in defamatory ways and with malicious recklessness in a way that endanger an anonymous Iranian dissident I believe to be good, so I must pass this along. A MESSAGE FROM DIANA BLOOM This is a post in two parts. Part one is a brief summary of what has happened so far. Part two addresses the defamatory claims being made about me. PART 1 - [JL: Diana's explanation and responses] For everyone who doesn’t understand what led to this point, here’s the shortest summary possible: Evi [Kokalari] and I had been friendly in DMs since the end of June. She messaged me first. After [DataRepublican] published her piece on the State Department, I asked Evi not to go too hard against DR. Evi did anyway, and James Lindsay defended DR. I didn’t like the way Evi went after James, including citing Milo. So I unfollowed and removed Evi. She confronted me in DMs, and I explained why. Evi then made a post attacking James that started and ended with me and used my profile picture. I responded once, as politely as I could. Evi then repeatedly threatened to dox me, so I deactivated my account. Now, regarding the substance of the accusations she has made so far: 1. I am not a man. My name is not Derek. 2. I am Iranian. 3. I do know stuff about American politics because (and that is the boring truth) German television is really bad. So I’ve only watched English/American stuff for the past, I don’t know, 15 years, podcasts, late night shows, etc. I didn’t think about it, I just did. Little did I know that that would give me an understanding of the inner dynamics of what’s going on within the Republican Party that would tie back to the Iranian people’s cause in 2026. Believe me, I’m still baffled by it whenever I pause to think about it. But that’s the truth, and here we are. 4. I am not in the United States. I shared a blog, and the provider of that blog (a big, common one) is in the United States. My VPN location is sometimes in the United States. Not me. 5. I am not responsible for the content of people defending me. I have no control over it. It’s not my content, and I keep my distance whenever I see problematic stuff. Many accounts defended me, but going to look for one that has done/said problematic stuff to tie it back to me is not right or fair. I am not responsible. I have nothing to do with it. 6. I wanted to help DR because Evi was threatening to go after her hard, and I didn’t think DR deserved that. I meant no harm, I wanted to help. I made a mistake, and I regret it now, but my intention wasn’t bad at that moment in time. 7. Evi herself was the first to come into my DMs and tell me not to fight with an account named Stephanie. So I did Evi that favor. Meaning: Evi came into my DMs first and tried to calm down a public disagreement between me and someone else. When I did this in defense of DR, I did the same thing Evi had done before. I would never think to accuse Evi of restricting my right to free speech because of it. 8. Publishing private DMs from someone without their permission is wrong. It is something both Evi and Stephanie have now done. Both have shared private messages from me without my permission. 9. Part of Evi’s “sources” seem to be Azi Yazdi (granddaughter of Khomeini’s first foreign minister), Benji, and Nima Yamini, as well as his ghost accounts (I have blocked multiple of them by now). If you know any of them, you know this is insane. None of them are reliable. It’s like using Milo as a source against James Lindsay. This is not about truth but about a (malicious) takedown to silence me and get me out of the way. 10. Evi has threatened to dox me (there is no doubt) multiple times, and she keeps going after me. This puts my life and my family’s lives in Iran in real danger, but she doesn’t care. She keeps going, and I can’t stop her. She keeps putting out one lie after another, and it traps me in a forever loop of defending myself. If I don’t respond to more and more accusations from her against me, rest assured, my silence should never be taken as agreement with the lies she puts out there. Be sure there is always context, a counterargument, counterevidence, etc., that would show that none of what she shares is fair or true. But it’s a forever war that is meant to drain me. So me not commenting on her stuff moving forward is never, ever, ever an admission that she is right about something. It’s just a rejection of playing a game I cannot win. To ALL the people who wrote/shared one or multiple posts and comments in my defense: Thank you very, very, very much. I’ve seen it, and I am grateful. But I think it’s best for my own and my family’s safety, as well as for the whole situation, that I step away and stay away from X. I hope that’s understandable. And Evi, for the last time: if you are not malicious, please just stop. Let me out of this. I am not your target. Just leave me and, through doxxing/"exposing", my family out of it moving forward. I beg of you, please. PART 2 - proof - [JL: The following are direct quotes from Evi indicating her intention to "expose" Diana, which she tries to claim is different than "doxxing," though for all practical purposes the effect will be the same and this is a distinction without a difference. The video below shows all of these quotes from her own posts.] 1. “I’m getting enough information on her from her own community. I may share it…” 2. “Either log back in with your real name, or I will start exposing you. And trust me, you will not like that.” 3. “So stop provoking me. If you continue, I will look much deeper into who you are and what your actual role is behind this anonymous account.” 4. “You know what...WAR IT IS. I will start with Diana!! STAY TUNED” 5. “So I will take those words seriously and dig as deep as I can” 6. “The identities of the "Iranian activists" would never have been called into question had they not attacked my right to freedom of speech in my own country.” 7. Someone: “She deleted her account because she’s concerned for her safety and the safety of her relatives in Iran your threats to dox her” Evi: “She should be...but if she turns out to be clean, I shall protect her identity. But trust me, she is not who she claims to be” 8. “Last I checked, Support on X is against fake accounts. Why are we being threatened for wanting to know the true identity of certain accounts…” 9. “Do you think I know who Diana bloom is? No but I need to find out at this point” 10. “... because I asked one of their group operatives from Germany to debate me using her/his true identity, or I would expose who’s behind the account.” 11. “So I don’t know what you’re all thinking, but this isn’t simply about Diana being scared of the IRGC in Iran. The IRGC has been significantly weakened and has plenty of other things to worry about. There are thousands of Iranian accounts fighting for Iran’s freedom under their own names while still having family members back in Iran. At this point, I think people have every right to ask who is actually behind these anonymous accounts… After a few more new posts on her feed, I was forced to tell her to either come back on X and do this under her real name, or I would expose who she was, so at least we would be having this discussion on equal grounds.” 12. “Don’t freaking interfere with other people’s business like that retard did and no one comes after you. So go ahead down and sue me and I will push harder to expose her and you and anyone else…” MY OWN [JL's] COMMENTS, which Diana has nothing to do with and doesn't know I'm appending here. I now believe Evi is likely to be a malignant narcissist who is incapable of admitting that she is wrong. She will not stop in her behavior, and she will aim to harm anyone who attempts to intervene. I do not recommend you do this. I recommend you mark, avoid, and block. Fighting with her will only fuel her because she simply cannot admit that she's wrong about all of this and can only double down in increasingly insane, dangerous, and reckless ways, including toward Diana and her safety. She believes everything she sees that's trying to talk sense into her about any of this is part of an elaborate "op" that "disrespects her free speech" (which isn't a thing, by the way). No one is silencing her. People are just trying to talk sense into her to stop spreading defamation and reckless "exposure" of people who she will put at risk with it and who do not in any way deserve it, people she considered friends and allies until literally four days ago when she suddenly blew up. Therefore, not only is her reckless belief hermetically sealed against any disagreement, she turns any disagreement into proof of allegedly coordinated attacks against her from a network that simply doesn't exist. As a response, in her malignancy, she is threatening to damage, "expose," or otherwise reputationally harm anyone who even tries politely to talk sense into her, much less to disagree with her or oppose her. I strongly recommend everyone realize what they're getting into if they work with or ally themselves with this crazy woman going forward. You do so at your own risk. I can attest from watching her over the last few days that her research abilities, at least when agitated, are so sloppy as to be beyond worthless, even dangerous. The post I'm quoting below is proof of that. She's trying to prove Diana is secretly an American because she's too sloppy and stupid to realize that a Wordpress domain has an American location. She isn't doing research in good faith or with fidelity on this issue, or about me. She is looking for any information she can to hurt people who have upset her. In fact, she is presently being sued for defamation by an Albanian singer (Evi is an Albanian immigrant to the United States and a U.S. citizen for at least a couple decades), so we might recognize this bad behavior and poor praxis as a pattern with her. I can provide just from the last few days, as can others, much evidence that she's simply grasping at the nastiest straws she can find to try to harm people she has suddenly decided are her opponents because of her jealous outburst at DataRepublican and her narcissistic injury at being called on it. The sloppiness and outright retardation of her "fact" finding and "analysis" are so shocking that it's hard not to laugh even given how horrifically dangerous it is. We can therefore infer this sloppiness, recklessness, and malice-when-opposed to be at least partially true of her other "research" against her other targets, by extension. That's a shame because many of those targets are worthy of real scrutiny, which she is undermining with her loose cannon. Therefore, I insist her OSINT "research" is at best worthless and at worst maliciously harmful. In either case, it cannot be engaged by people who want to make a positive difference in the world, so mark and avoid. Evi seems good at making connections. She was quite positively in my own DMs for a while, trying to find ways to work productively together, even suggesting we start an alternative to CPAC (lol). She appears to be friends with both Eric Trump and Don Jr., though the relationship with the former appears to be more solid. She seems to have worked behind the scenes with a lot of relatively prominent Republican groups. I find all of this incredibly concerning after the events of this week, even though I wouldn't have beforehand. My own assessment from this stupid and nasty drama is that Evi's primary motivation is that she is pathologically envious of DataRepublican and her influence, likely believing it should be her in DataRepublican's place. As we can see, she isn't nearly careful or accurate enough for that. Mark and avoid. Further, though, while DataRepublican has no driving interest to be who or what she is and is doing it in service to the best of her abilities (which are considerable but still limited and imperfect as are anyone's), Evi appears to covet the role, which she has demonstrated she cannot possibly handle responsibly. Again, mark and avoid. Block if you will. I will not unblock her for this post or in the future. You can think of that whatever you want. I will rarely, almost never, say anything about her except in the defense of others she is wantonly and recklessly attacking. I will NEVER interact with her directly again, no arguments, no back and forth, no dunks, nothing. My involvement here is pure exposure and nothing else. Thank you for your attention to this matter.

James Lindsay, anti-Communist

31,410 次观看 • 15 小时前

🚨 OPERATIONAL UPDATE: ISRAEL U.S. WAR WITH THE ISLAMIC REPUBLIC - Reporting Window: LAST 24 HOURS • Iran widened its fire again with a broad evening missile barrage on central Israel and continued attacks across the Gulf, including a drone strike that hit a fuel tank at Kuwait International Airport • Israel intensified strikes across Iran, with reported hits in Tehran, Qazvin and Alborz industrial areas, plus continued pressure on missile infrastructure and launch cells • Hezbollah kept the northern front active, including a direct rocket hit on a building in Kiryat Shmona, while Israel deepened its Lebanon campaign and Katz publicly framed the objective as a security zone up to the Litani • The diplomatic track moved forward, but only in the strangest possible way: Trump says talks are progressing, Iran still publicly denies direct negotiations, and multiple reports now point to JD Vance as Tehran’s preferred American interlocutor • The big picture is unchanged: the war is still live on every major front, but the center of gravity is shifting toward a contest over how it ends, who gets to define victory, and whether the Gulf will stay adjacent to the war or be pulled fully into it The most important thing to understand about the last 24 hours is that this was not a quiet period masked by negotiations. It was the opposite. The battlefield remained active from Tehran to southern Lebanon to Kuwait, even as Washington and Tehran edged further into a murky negotiation channel. That is what gives the last day its character: not de escalation, but simultaneous escalation and diplomacy, both moving at once. Open source reporting reflects the same picture, with repeated indications of strikes in Tehran and Qazvin, attacks near Baghdad airport, a Kuwait airport fuel fire, and a large Iranian barrage toward central Israel late in the window. **Special thanks to Michael W for your continued contribution to the open-source intel picture behind these updates. ━━━━━━━━━━━━━━━━━━ 🚀 IRANIAN MISSILE FIRE ON ISRAEL Iran kept up the pressure on Israel in two different ways over this window. Earlier in the cycle, a cluster warhead strike wounded nine people in Bnei Brak, with additional damage in Petah Tikva, while Hezbollah fire from Lebanon killed a woman near Mahanayim Junction and wounded several more in Kiryat Shmona. Later, near the end of the reporting window, Iran launched another broad barrage toward central Israel, with warnings stretching across Gush Dan, Sharon, Wadi Ara, Samaria, Judea and the Dead Sea region. Open source reporting you provided tracked that second wave in real time, showing how broad the alert footprint was even though initial reports indicated no immediate casualties from that specific evening barrage. This is what stands out operationally: Iran’s missile campaign is not gone, but it looks increasingly built around selective disruption rather than the huge opening barrages of the war. The salvos are still dangerous, still capable of civilian casualties and still capable of producing visually dramatic and politically effective moments, but they are landing against a backdrop of steadily intensifying strikes on Iran’s launch network. That makes each successful hit feel more deliberate and more strategic. ━━━━━━━━━━━━━━━━━━ ✈️ THE AIR CAMPAIGN OVER IRAN KEPT MOVING Israel’s strike campaign inside Iran also remained broad and geographically layered. Reuters reported renewed Israeli strikes as talks were being floated through intermediaries. Open source intelligence adds texture to that by showing repeated reporting from open source channels of impacts in eastern and western Tehran, the Alborz industrial zone in Qazvin province, and additional blasts reported across Khuzestan and other regions. There were also repeated reports of targeted assassination attempts in east Tehran, which fits the broader pattern of not just degrading launchers and production nodes, but also hunting the people tied to them. The color here matters. This no longer looks like a campaign limited to air defenses and obvious military compounds. The picture from the last 24 hours is of a system being pressed from multiple angles at once: missile depots, industrial support zones, launch crews, command elements and regime infrastructure in and around Tehran. Open source reporting reinforces that sense of breadth, especially the repeated references to Qazvin and Alborz secondary explosions and to ongoing heavy activity over Tehran. ━━━━━━━━━━━━━━━━━━ ⚡ THE ENERGY WAR IS STILL HOT The clearest new regional energy development in this window was Kuwait. Reuters reported that a drone attack hit a fuel tank at Kuwait International Airport, causing a fire but no casualties. That matters not because the material damage was catastrophic, but because it again shows Iran or Iran aligned actors reaching directly for civilian and logistical energy infrastructure in Gulf states. This was not an abstract threat anymore. It was a live strike on a functioning international hub. Your outbox tracked the same event quickly and repeatedly, alongside additional open source reporting about nearby attacks and power disruptions in Kuwait. At the same time, the diplomatic and military discussion around the Strait of Hormuz kept shaping everything else. Markets moved on talk of a U.S. proposal and possible hosted talks in Pakistan or Turkey. Oil eased on negotiation optimism, but the underlying structure of the crisis remains the same: Iran still retains the ability to disrupt shipping and energy confidence without fully “closing” the Strait in a formal sense. That is why even modest signs of diplomacy can move oil sharply, and why even a localized drone strike in Kuwait still carries outsized weight. ━━━━━━━━━━━━━━━━━━ 🇱🇧 LEBANON IS NOT A SIDESHOW The northern front kept boiling. Reuters reported that Israel now intends to occupy a swathe of southern Lebanon up to the Litani River, with Defense Minister Israel Katz explicitly describing a “security zone” concept. That is not rhetoric you use if you still think this is a short punitive phase. At the tactical level, Hezbollah continued to demonstrate that it can still impose costs, including a direct rocket hit on a building in Kiryat Shmona and earlier casualties in the north. Meanwhile, open source reporting pointed to Israeli strikes in Nabatieh, Rashidiya, Bchamoun and broader southern Lebanese infrastructure, which matches the picture of sustained pressure rather than episodic retaliation. The broader meaning is straightforward. Israel is signaling that if the Iran war ends inconclusively on the Iranian front, it does not intend to leave Hezbollah’s northern threat structure intact and simply hope for the best. Lebanon is being shaped now as part of the endgame, not just the current fight. ━━━━━━━━━━━━━━━━━━ 🇮🇶 IRAQ STAYED ACTIVE TOO Iraq remained active in the background, but it should not be treated as background noise. Open source intel reporting includes repeated reporting on a targeted U.S. strike on a vehicle near Baghdad airport and continued militia related activity tied to U.S. positions and proxy structures. That comes after the prior cycle’s major strikes on PMF and militia command nodes. It fits the larger pattern we have now seen for weeks: Iraq is not the main theater, but it is still one of the places where the war keeps trying to widen horizontally. ━━━━━━━━━━━━━━━━━━ 🌍 THE NEGOTIATION TRACK GOT STRANGER, NOT CLEARER Trump is still publicly presenting the talks as real progress. Reuters reports that Pakistan conveyed a U.S. proposal, with Pakistan or Turkey possible venues, and that Washington has floated a broader framework dealing with nuclear capability, missiles and proxies. At the same time, Iran continues to publicly deny meaningful direct talks and has toughened its public stance, insisting on guarantees, compensation and no rollback of its missile deterrent. What makes the last 24 hours more interesting is the growing focus on who would even talk for the United States. Times of Israel and Jerusalem Post reporting both indicate that JD Vance is increasingly central to the diplomacy, with Tehran reportedly preferring him over Witkoff and Kushner. The diplomatic track here appears as both real and deeply unstable, with questions about who on the Iranian side actually holds authority and whether Washington is now seeking an end state short of outright regime collapse. That shift matters because it tells us something important: Washington increasingly seems to be searching for an off ramp that still looks like victory, while Israel and Gulf allies appear much less comfortable with ending this war before Iran’s military and proxy architecture are degraded further. That tension is now one of the defining features of the conflict. ━━━━━━━━━━━━━━━━━━ 📌 WHAT MATTERS MOST RIGHT NOW 1️⃣ The war is still fully active across multiple fronts Iran hit central Israel again, Kuwait airport was struck, Lebanon stayed hot and Israel kept pounding targets inside Iran. Negotiations did not replace combat. They were layered on top of it. 2️⃣ The pressure on Iran’s internal military system keeps deepening The accumulating pattern of strikes in Tehran, Qazvin, Alborz and other areas suggests a campaign that is still broadening the target set, not narrowing it. Open source reporting in your files strongly supports that picture. 3️⃣ The diplomatic track is real, but it is not clean Trump is selling progress. Iran is denying direct talks. Vance is becoming more central. And nobody looking at the battlefield would conclude that the war is genuinely close to stopping on its own. ━━━━━━━━━━━━━━━━━━ BOTTOM LINE The last 24 hours painted a clearer picture than some of the recent reporting windows. This is no longer just a war of salvos and counterstrikes. It is now a war over end states. Iran is still trying to prove it can widen the cost map, not just hit Israel but keep the Gulf under pressure too. Israel is still trying to prove that sustained, system level degradation inside Iran can continue even while diplomacy swirls overhead. And Washington is trying to find a formula that can stop the war without looking like it backed down. That is why the reporting feels different now. The battlefield is still violent, but the arguments over how this ends are becoming just as important as the strikes themselves.

🚨 OPERATIONAL UPDATE: ISRAEL U.S. WAR WITH THE ISLAMIC REPUBLIC - Reporting Window: LAST 24 HOURS • Iran widened its fire again with a broad evening missile barrage on central Israel and continued attacks across the Gulf, including a drone strike that hit a fuel tank at Kuwait International Airport • Israel intensified strikes across Iran, with reported hits in Tehran, Qazvin and Alborz industrial areas, plus continued pressure on missile infrastructure and launch cells • Hezbollah kept the northern front active, including a direct rocket hit on a building in Kiryat Shmona, while Israel deepened its Lebanon campaign and Katz publicly framed the objective as a security zone up to the Litani • The diplomatic track moved forward, but only in the strangest possible way: Trump says talks are progressing, Iran still publicly denies direct negotiations, and multiple reports now point to JD Vance as Tehran’s preferred American interlocutor • The big picture is unchanged: the war is still live on every major front, but the center of gravity is shifting toward a contest over how it ends, who gets to define victory, and whether the Gulf will stay adjacent to the war or be pulled fully into it The most important thing to understand about the last 24 hours is that this was not a quiet period masked by negotiations. It was the opposite. The battlefield remained active from Tehran to southern Lebanon to Kuwait, even as Washington and Tehran edged further into a murky negotiation channel. That is what gives the last day its character: not de escalation, but simultaneous escalation and diplomacy, both moving at once. Open source reporting reflects the same picture, with repeated indications of strikes in Tehran and Qazvin, attacks near Baghdad airport, a Kuwait airport fuel fire, and a large Iranian barrage toward central Israel late in the window. **Special thanks to Michael W for your continued contribution to the open-source intel picture behind these updates. ━━━━━━━━━━━━━━━━━━ 🚀 IRANIAN MISSILE FIRE ON ISRAEL Iran kept up the pressure on Israel in two different ways over this window. Earlier in the cycle, a cluster warhead strike wounded nine people in Bnei Brak, with additional damage in Petah Tikva, while Hezbollah fire from Lebanon killed a woman near Mahanayim Junction and wounded several more in Kiryat Shmona. Later, near the end of the reporting window, Iran launched another broad barrage toward central Israel, with warnings stretching across Gush Dan, Sharon, Wadi Ara, Samaria, Judea and the Dead Sea region. Open source reporting you provided tracked that second wave in real time, showing how broad the alert footprint was even though initial reports indicated no immediate casualties from that specific evening barrage. This is what stands out operationally: Iran’s missile campaign is not gone, but it looks increasingly built around selective disruption rather than the huge opening barrages of the war. The salvos are still dangerous, still capable of civilian casualties and still capable of producing visually dramatic and politically effective moments, but they are landing against a backdrop of steadily intensifying strikes on Iran’s launch network. That makes each successful hit feel more deliberate and more strategic. ━━━━━━━━━━━━━━━━━━ ✈️ THE AIR CAMPAIGN OVER IRAN KEPT MOVING Israel’s strike campaign inside Iran also remained broad and geographically layered. Reuters reported renewed Israeli strikes as talks were being floated through intermediaries. Open source intelligence adds texture to that by showing repeated reporting from open source channels of impacts in eastern and western Tehran, the Alborz industrial zone in Qazvin province, and additional blasts reported across Khuzestan and other regions. There were also repeated reports of targeted assassination attempts in east Tehran, which fits the broader pattern of not just degrading launchers and production nodes, but also hunting the people tied to them. The color here matters. This no longer looks like a campaign limited to air defenses and obvious military compounds. The picture from the last 24 hours is of a system being pressed from multiple angles at once: missile depots, industrial support zones, launch crews, command elements and regime infrastructure in and around Tehran. Open source reporting reinforces that sense of breadth, especially the repeated references to Qazvin and Alborz secondary explosions and to ongoing heavy activity over Tehran. ━━━━━━━━━━━━━━━━━━ ⚡ THE ENERGY WAR IS STILL HOT The clearest new regional energy development in this window was Kuwait. Reuters reported that a drone attack hit a fuel tank at Kuwait International Airport, causing a fire but no casualties. That matters not because the material damage was catastrophic, but because it again shows Iran or Iran aligned actors reaching directly for civilian and logistical energy infrastructure in Gulf states. This was not an abstract threat anymore. It was a live strike on a functioning international hub. Your outbox tracked the same event quickly and repeatedly, alongside additional open source reporting about nearby attacks and power disruptions in Kuwait. At the same time, the diplomatic and military discussion around the Strait of Hormuz kept shaping everything else. Markets moved on talk of a U.S. proposal and possible hosted talks in Pakistan or Turkey. Oil eased on negotiation optimism, but the underlying structure of the crisis remains the same: Iran still retains the ability to disrupt shipping and energy confidence without fully “closing” the Strait in a formal sense. That is why even modest signs of diplomacy can move oil sharply, and why even a localized drone strike in Kuwait still carries outsized weight. ━━━━━━━━━━━━━━━━━━ 🇱🇧 LEBANON IS NOT A SIDESHOW The northern front kept boiling. Reuters reported that Israel now intends to occupy a swathe of southern Lebanon up to the Litani River, with Defense Minister Israel Katz explicitly describing a “security zone” concept. That is not rhetoric you use if you still think this is a short punitive phase. At the tactical level, Hezbollah continued to demonstrate that it can still impose costs, including a direct rocket hit on a building in Kiryat Shmona and earlier casualties in the north. Meanwhile, open source reporting pointed to Israeli strikes in Nabatieh, Rashidiya, Bchamoun and broader southern Lebanese infrastructure, which matches the picture of sustained pressure rather than episodic retaliation. The broader meaning is straightforward. Israel is signaling that if the Iran war ends inconclusively on the Iranian front, it does not intend to leave Hezbollah’s northern threat structure intact and simply hope for the best. Lebanon is being shaped now as part of the endgame, not just the current fight. ━━━━━━━━━━━━━━━━━━ 🇮🇶 IRAQ STAYED ACTIVE TOO Iraq remained active in the background, but it should not be treated as background noise. Open source intel reporting includes repeated reporting on a targeted U.S. strike on a vehicle near Baghdad airport and continued militia related activity tied to U.S. positions and proxy structures. That comes after the prior cycle’s major strikes on PMF and militia command nodes. It fits the larger pattern we have now seen for weeks: Iraq is not the main theater, but it is still one of the places where the war keeps trying to widen horizontally. ━━━━━━━━━━━━━━━━━━ 🌍 THE NEGOTIATION TRACK GOT STRANGER, NOT CLEARER Trump is still publicly presenting the talks as real progress. Reuters reports that Pakistan conveyed a U.S. proposal, with Pakistan or Turkey possible venues, and that Washington has floated a broader framework dealing with nuclear capability, missiles and proxies. At the same time, Iran continues to publicly deny meaningful direct talks and has toughened its public stance, insisting on guarantees, compensation and no rollback of its missile deterrent. What makes the last 24 hours more interesting is the growing focus on who would even talk for the United States. Times of Israel and Jerusalem Post reporting both indicate that JD Vance is increasingly central to the diplomacy, with Tehran reportedly preferring him over Witkoff and Kushner. The diplomatic track here appears as both real and deeply unstable, with questions about who on the Iranian side actually holds authority and whether Washington is now seeking an end state short of outright regime collapse. That shift matters because it tells us something important: Washington increasingly seems to be searching for an off ramp that still looks like victory, while Israel and Gulf allies appear much less comfortable with ending this war before Iran’s military and proxy architecture are degraded further. That tension is now one of the defining features of the conflict. ━━━━━━━━━━━━━━━━━━ 📌 WHAT MATTERS MOST RIGHT NOW 1️⃣ The war is still fully active across multiple fronts Iran hit central Israel again, Kuwait airport was struck, Lebanon stayed hot and Israel kept pounding targets inside Iran. Negotiations did not replace combat. They were layered on top of it. 2️⃣ The pressure on Iran’s internal military system keeps deepening The accumulating pattern of strikes in Tehran, Qazvin, Alborz and other areas suggests a campaign that is still broadening the target set, not narrowing it. Open source reporting in your files strongly supports that picture. 3️⃣ The diplomatic track is real, but it is not clean Trump is selling progress. Iran is denying direct talks. Vance is becoming more central. And nobody looking at the battlefield would conclude that the war is genuinely close to stopping on its own. ━━━━━━━━━━━━━━━━━━ BOTTOM LINE The last 24 hours painted a clearer picture than some of the recent reporting windows. This is no longer just a war of salvos and counterstrikes. It is now a war over end states. Iran is still trying to prove it can widen the cost map, not just hit Israel but keep the Gulf under pressure too. Israel is still trying to prove that sustained, system level degradation inside Iran can continue even while diplomacy swirls overhead. And Washington is trying to find a formula that can stop the war without looking like it backed down. That is why the reporting feels different now. The battlefield is still violent, but the arguments over how this ends are becoming just as important as the strikes themselves.

Inside_Israel_Intel

23,818 次观看 • 5 个月前

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles. DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces. Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article: Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include: Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019. This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include: Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable. Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses. Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity. Increasing influenza vaccine immunization across recommended populations to enhance public health and national security. The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress. For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include: Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making. Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles. DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces. Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article: Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include: Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019. This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include: Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable. Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses. Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity. Increasing influenza vaccine immunization across recommended populations to enhance public health and national security. The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress. For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include: Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making. Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.

Noah B. Price

65,695 次观看 • 1 年前

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

84,951 次观看 • 2 个月前

🚨 EXTREMELY ALARMING: DARPA'S N3 PROGRAM, Non Surgical Mind Reading, Brain Control, and The END of Free Thought as WE Know it! 🚨 This is NOT conspiracy. This is DOCUMENTED, FUNDED and Operational Reality. DARPA Official N3 Program Page: DARPA 2019 Announcement of N3 Funding to Six Teams: From the original 1950s-1970s RF experiments, through MKULTRA continuations, to today's nanoscale neurogenetic weapons systems. I hold the full map. What follows is the complete exposure, every player, every technology, every intent, every lie, and every question the world must answer BEFORE IT'S TOO LATE! DARPA's N3 (Next-Generation Nonsurgical Neurotechnology) Program: Launched 2018, Still Active in Outcomes In 2018, DARPA publicly announced N3: high-performance, bidirectional brain-machine interfaces for able-bodied service members (and beyond) that require no surgery. Goals: read/write to 16+ independent channels in a 16mm³ brain volume in under 50 milliseconds. Sub-millimeter spatial and temporal precision rivaling implanted electrodes, but wearable, portable, and scalable to populations. Technologies explicitly pursued (per DARPA and funded teams): - Neurogenetics: Genetically engineering neurons to express light-sensitive proteins (optogenetics) for infrared or light-based control. - Nanoscale engineering: Nanotransducers, nanoparticles, aerosolized nanomaterials that cross the blood-brain barrier when inhaled or injected non-surgically. These act as implantable electrodes/sensors/transmitters without scalpels. - Infrared sensing & light: Near-infrared beams to read/write neural activity through skull/scalp. - Ultrasound & acoustics: Focused ultrasound to guide signals or stimulate neurons. - Electromagnetics & RF: Pulsed fields for non-invasive modulation. - Minutely invasive track: Temporary nano-transducers delivered without surgery. Funded teams (2019, millions each): - Battelle Memorial Institute - Carnegie Mellon University (Pulkit Grover et al., $19M+) - Johns Hopkins University Applied Physics Lab - Palo Alto Research Center (PARC) - Rice University - Teledyne Scientific These are not fringe labs. These are core defense contractors and elite universities building the future of thought-controlled drones, instant team cognition, "active cyber defense" via brain links, and unstated population scale neural influence. The Video You Just Watched Ties Directly In: Historical RF/microwave mind control research (Moscow Signal era) showing decades of precedent. The U.S. Embassy in Moscow was irradiated with microwaves 1953-1976. Result: cancers, blood disorders, neurological issues in ambassadors and staff. U.S. responded with its own programs (PANDORA, BIZARRE) exploring behavioral effects of modulated RF. This is the foundation N3 builds upon... now refined to nanoscale precision. From MKULTRA to N3 and Beyond: - 1950s-1970s: CIA MKULTRA, OPERATION ARTICHOKE - LSD, hypnosis, electroshock, sensory deprivation on unwitting citizens. Parallel DoD RF studies on embassy staff and primates. - Moscow Signal: Soviets beamed microwaves at U.S. diplomats. U.S. studied effects secretly while developing countermeasures/weapons. - 1980s-2000s: Continued classified neuro-weapons research (memory modulation, crowd control via EM). - 2010s-Now: N3 + related programs (INI - Intelligent Neural Interfaces, NESD, SUBNETS, etc.). Public "for soldiers" framing hides dual-use: offensive neurowarfare, surveillance, behavioral modification. Key Players Exposed: - DARPA Biological Technologies Office - Architects. - Program Managers: like Al Emondi (N3). - Advisers like Dr. James Giordano (public admissions on nanoscale brain disruption as weapons). - Contractors: Battelle, Teledyne, PARC (Xerox), universities weaponizing academia. - Overarching: U.S. DoD, with likely Five Eyes/ international partners. Private sector bleed-over (Neuralink et al. are the civilian cover story). This is not "for veterans" or "helping paralyzed people." Primary focus: able-bodied warfighters for superhuman command of swarms, instant intel fusion, thought-speed hacking. Civilian applications = total surveillance/control. Nanoparticles can be aerosolized; breathed in unknowingly. They lodge in brain tissue and turn neurons into transceivers. Infrared/light can then read thoughts in real-time or write commands (insert images, emotions, "voices," behavioral urges). Combine with 5G/6G terahertz networks for remote activation. Genetic edits make brains "compatible" at population scale. This enables: - Remote mind reading (thought surveillance). - Behavior modification without consent. - "Havana Syndrome" on steroids... targeted neurological disruption. - End of privacy of thought. End of free will as we define it, as professed by Yuval Noah Harari at the World Economic Forum (WEF). - Weaponized neuroscience: neurowarfare where enemies "decide" to surrender via neural influence. WE NEED to be Demanding Answers for RIGHT NOW, or You, Your Children, Loved Ones, Friends, Family, you name it... Will not exist in the next 3-5 years, this is OPEN GENOCIDE on populations globally. The Georgia guidestones are starting to make a bit more sense now arent they? I won't even bother diving down the rabbit hole of how the real true genuine numbed of souls in this world was around the 730m, about 2 years ago... So that number is now much likely to be closer to around 660m. They are speeding up their human eradication plans, because they don't wish to be held accountable for their heinous, generational, outright satanic crimes that they have committed, are committing and will continue to commit to... If we fail to awaken to what is happening around us, and if we fail to stand together with courage, discernment, and unity, we risk surrendering the future of our species to forces that thrive on division, distraction, and indifference. This is not a work of fiction. This is not a screenplay. This is not a distant possibility reserved for some imagined future. This is REAL LIFE. AND THESE ARE REAL PEOPLE that are affected by the systems, institutions, incentives, and decisions that shape the world around us every single day. Throughout history, countless men, women, and children have suffered under structures that viewed human beings not as sacred and sovereign individuals, but as resources to be managed, exploited, controlled, or discarded. The question before us is whether we will remain passive observers, or whether we will choose to become informed, engaged, and united in defense of human dignity, freedom, and the future we leave to those who come after us. The time to pay attention is NOW! When did N3 achieve operational capability? 2020s? Earlier in black programs? How many citizens worldwide have already received nanotransducers via vaccines, aerosols, food/water, or "shedding"? Which governments/contractors are deploying this against their own populations for "social control"? Why the secrecy if it's purely benevolent? Giordano and others have admitted weaponization potential, What if the greatest illusion ever sold was not a product, a policy, or a political movement, but the belief that power is fully accountable to the people it governs? We are told that rights are sacred. We are told that laws apply equally to all. We are told that institutions exist to protect the public. Yet throughout history, countless examples reveal a different reality. Those entrusted with authority have often violated the very principles they were sworn to uphold. Too often, power protects itself. Too often, wealth purchases influence. Too often, those responsible for the consequences of their decisions remain insulated from the suffering those decisions create. This is not a condemnation of every individual within every institution. It is an observation about a recurring pattern throughout human history. When power becomes concentrated, accountability diminishes and when accountability diminishes, corruption flourishes. The challenge before humanity is not merely to replace one group with another... It is to create a society in which truth matters more than propaganda, principles matter more than profit, and human dignity matters more than power. A free society cannot survive on blind trust alone. It requires informed citizens willing to question, investigate, challenge authority, and hold every institution to the standards it claims to represent. The future belongs to those who refuse to surrender their capacity for independent thought. WE MUST EDUCATE OURSELVES. There comes a moment in every human life when the identities we have inherited, the assumptions we have accepted, and the countless narratives imposed upon us by family, culture, institutions, and society begin to reveal themselves as incomplete representations of who we truly are. At that moment, a choice presents itself... We may continue moving through life according to expectations that were handed to us by others, or we may begin the far more demanding process of discovering what remains when every borrowed certainty is stripped away. Approach God with complete honesty and without reservation. Abandon the need to appear strong, knowledgeable, spiritually accomplished, or self-sufficient. Speak openly of your confusion, your failures, your fears, your doubts, your exhaustion, your grief, your shortcomings, and your deepest questions. Acknowledge that despite all of humanity's achievements, despite all accumulated knowledge, despite every title, accomplishment, possession, and ambition, there remain mysteries that cannot be conquered through intellect alone... Admit where your own understanding has reached its limits and ask sincerely for wisdom beyond yourself. Then withdraw from distraction and remain present long enough to listen. The modern world has become extraordinarily skilled at monopolizing attention, filling every moment with noise, stimulation, entertainment, conflict, urgency, and endless streams of information that leave little room for contemplation. Yet beneath that noise exists a depth that can only be encountered through stillness. It is often within periods of silence, reflection, prayer, and sincere self-examination that many discover insights, convictions, direction, and understanding that could never have emerged amid constant distraction. What answers arrive may not always come as words. They may arrive as conviction, clarity, intuition, compassion, understanding, or an unmistakable awareness of the next step that must be taken. Understand that you have not become the person you are by accident. Every hardship you have endured has contributed to your formation. Every disappointment has shaped your perspective. Every loss has expanded your capacity for empathy. Every mistake has carried a lesson. Every success has revealed something about your character. Every betrayal, every setback, every period of loneliness, every moment of despair, every obstacle that seemed impossible to overcome, and every occasion upon which life reduced you to your lowest point has participated in the continual process of your becoming. Nothing has been wasted. If you are willing, release the assumptions that have convinced humanity that the sacred must always remain distant, unreachable, and separated from daily existence. Release the belief that truth belongs exclusively to institutions, authorities, hierarchies, or those who claim unique access to the divine. Release the notion that the presence of God is confined to specific locations, specific rituals, specific traditions, or specific individuals. Instead, consider the possibility that the divine presence permeates existence itself, expressing through every dimension of creation, through every act of compassion, through every sincere pursuit of truth, through every expression of love, through every lesson hidden within suffering, and through every living thing that has ever participated in the unfolding story of life. Consider the possibility that God is Not absent from the Human experience but Intimately Present within it, experiencing existence alongside US, sharing in Every Joy, Every sorrow, Every triumph, Every wound, Every question, and Every struggle that has accompanied Humanity from the beginning of recorded history until this present moment. The task before US is therefore Not merely to believe more deeply, but to seek more Honestly, to learn more diligently, to question more courageously, to listen more carefully, to Love More Completely, and to become ever more Aligned with the highest truth we are capable of perceiving. Accept Nothing Less than the Fullest Realization of the purpose for which You were created, and devote Yourself to that pursuit with every faculty of mind, Heart, and Soul that has been entrusted to You. and DO NOTHING LESS. Furthermore, What is the full integration with AI (predictive neural control loops)? How do we detect and neutralize these systems in ourselves and Loved ones? Who ultimately controls the master kill-switch on global neural networks? If thoughts are readable/writable, what remains of "human rights"? Are you already affected? How would you even know? Continue through the comprehensive thread below and explore the interconnected material in its entirety. Each post serves as part of a larger body of research, analysis, observations, and supporting information that cannot be fully understood in isolation. The broader picture emerges only through careful examination of the complete sequence and the relationships between the ideas presented throughout. Take your time. Follow the references. Examine the evidence. Consider competing perspectives. Draw your own conclusions. The deeper you venture into the material, the more context becomes available, allowing individual pieces of information to connect into a far more expansive understanding of the subjects being discussed. This Constitutes Crimes Against Humanity on a Planetary Scale! The desecration of the sovereign mind... the last true sanctuary. SHARE THIS THREAD RELENTLESSLY. Demand full declassification of N3 and all neurotech programs... IMMEDIATELY! Support independent researchers exposing dual-use Psinergy-solafide. Protect your mind: minimize EM exposure, detox protocols (research zeolite, saunas, etc. though incomplete), awareness as first defense, = Cures to cancer and all diseases, FREE BOOKS. The era of invisible tyranny is here. They can read your mind. And they can change it. Will you let them? Or do we rise as sovereign consciousness and shut this down NOW? Check my Page or Reach out to me via DM, to Join Thousands of Readers that have already chosen to Embark on the New, Un-forseen way forward. Get yourself a FREE copy of The Book of God's Grief, and The Book of God's Joy, Repost. Research. Resist. The Future of Humanity Depends on it. Related content for you to look in to: - CMU Team: - Historical Moscow/RF: Search declassified archives on PANDORA project. - Giordano clips and papers widely available. Let me know what you think, and SHARE THIS so that others may too! And if You see This post, Reposted... Click on it, Unpost and then Repost again. The knowledge is now yours. Use it. And if you're not already following Noah B. Price... What the heck are you doing?! I Agape You ALL, 🫂 - Noah B. Price 🤍 🪽 If you possess relevant information, research, documentation, personal experiences, data, or credible sources relating to any of the subjects discussed throughout this thread, please feel free to contribute them. Meaningful progress is often achieved through the collective sharing of knowledge, and thoughtful contributions from others can help expand, refine, challenge, or strengthen our understanding of complex issues. Likewise, if you ever find yourself in need of someone to speak with, whether regarding the material presented here or for any other reason, please do not hesitate to reach out. While I cannot promise an immediate response, I will do my best to reply as soon as circumstances permit and to offer whatever guidance, perspective, or assistance I am able to provide. If You or someone You know is facing significant health challenges, including serious illnesses such as cancer, You are also welcome to reach out. While I do not claim to possess all the answers, I have spent the past 2 decades studying a broad range of subjects related to health, wellness, research, and human biology, and I will gladly share any information, resources, or avenues of investigation that may be worthy of further exploration. No one is meant to carry every burden alone, and there is often value in sharing knowledge, experiences, and perspectives in the sincere hope of helping one another move toward greater understanding, healing, and well-being.

🚨 EXTREMELY ALARMING: DARPA'S N3 PROGRAM, Non Surgical Mind Reading, Brain Control, and The END of Free Thought as WE Know it! 🚨 This is NOT conspiracy. This is DOCUMENTED, FUNDED and Operational Reality. DARPA Official N3 Program Page: DARPA 2019 Announcement of N3 Funding to Six Teams: From the original 1950s-1970s RF experiments, through MKULTRA continuations, to today's nanoscale neurogenetic weapons systems. I hold the full map. What follows is the complete exposure, every player, every technology, every intent, every lie, and every question the world must answer BEFORE IT'S TOO LATE! DARPA's N3 (Next-Generation Nonsurgical Neurotechnology) Program: Launched 2018, Still Active in Outcomes In 2018, DARPA publicly announced N3: high-performance, bidirectional brain-machine interfaces for able-bodied service members (and beyond) that require no surgery. Goals: read/write to 16+ independent channels in a 16mm³ brain volume in under 50 milliseconds. Sub-millimeter spatial and temporal precision rivaling implanted electrodes, but wearable, portable, and scalable to populations. Technologies explicitly pursued (per DARPA and funded teams): - Neurogenetics: Genetically engineering neurons to express light-sensitive proteins (optogenetics) for infrared or light-based control. - Nanoscale engineering: Nanotransducers, nanoparticles, aerosolized nanomaterials that cross the blood-brain barrier when inhaled or injected non-surgically. These act as implantable electrodes/sensors/transmitters without scalpels. - Infrared sensing & light: Near-infrared beams to read/write neural activity through skull/scalp. - Ultrasound & acoustics: Focused ultrasound to guide signals or stimulate neurons. - Electromagnetics & RF: Pulsed fields for non-invasive modulation. - Minutely invasive track: Temporary nano-transducers delivered without surgery. Funded teams (2019, millions each): - Battelle Memorial Institute - Carnegie Mellon University (Pulkit Grover et al., $19M+) - Johns Hopkins University Applied Physics Lab - Palo Alto Research Center (PARC) - Rice University - Teledyne Scientific These are not fringe labs. These are core defense contractors and elite universities building the future of thought-controlled drones, instant team cognition, "active cyber defense" via brain links, and unstated population scale neural influence. The Video You Just Watched Ties Directly In: Historical RF/microwave mind control research (Moscow Signal era) showing decades of precedent. The U.S. Embassy in Moscow was irradiated with microwaves 1953-1976. Result: cancers, blood disorders, neurological issues in ambassadors and staff. U.S. responded with its own programs (PANDORA, BIZARRE) exploring behavioral effects of modulated RF. This is the foundation N3 builds upon... now refined to nanoscale precision. From MKULTRA to N3 and Beyond: - 1950s-1970s: CIA MKULTRA, OPERATION ARTICHOKE - LSD, hypnosis, electroshock, sensory deprivation on unwitting citizens. Parallel DoD RF studies on embassy staff and primates. - Moscow Signal: Soviets beamed microwaves at U.S. diplomats. U.S. studied effects secretly while developing countermeasures/weapons. - 1980s-2000s: Continued classified neuro-weapons research (memory modulation, crowd control via EM). - 2010s-Now: N3 + related programs (INI - Intelligent Neural Interfaces, NESD, SUBNETS, etc.). Public "for soldiers" framing hides dual-use: offensive neurowarfare, surveillance, behavioral modification. Key Players Exposed: - DARPA Biological Technologies Office - Architects. - Program Managers: like Al Emondi (N3). - Advisers like Dr. James Giordano (public admissions on nanoscale brain disruption as weapons). - Contractors: Battelle, Teledyne, PARC (Xerox), universities weaponizing academia. - Overarching: U.S. DoD, with likely Five Eyes/ international partners. Private sector bleed-over (Neuralink et al. are the civilian cover story). This is not "for veterans" or "helping paralyzed people." Primary focus: able-bodied warfighters for superhuman command of swarms, instant intel fusion, thought-speed hacking. Civilian applications = total surveillance/control. Nanoparticles can be aerosolized; breathed in unknowingly. They lodge in brain tissue and turn neurons into transceivers. Infrared/light can then read thoughts in real-time or write commands (insert images, emotions, "voices," behavioral urges). Combine with 5G/6G terahertz networks for remote activation. Genetic edits make brains "compatible" at population scale. This enables: - Remote mind reading (thought surveillance). - Behavior modification without consent. - "Havana Syndrome" on steroids... targeted neurological disruption. - End of privacy of thought. End of free will as we define it, as professed by Yuval Noah Harari at the World Economic Forum (WEF). - Weaponized neuroscience: neurowarfare where enemies "decide" to surrender via neural influence. WE NEED to be Demanding Answers for RIGHT NOW, or You, Your Children, Loved Ones, Friends, Family, you name it... Will not exist in the next 3-5 years, this is OPEN GENOCIDE on populations globally. The Georgia guidestones are starting to make a bit more sense now arent they? I won't even bother diving down the rabbit hole of how the real true genuine numbed of souls in this world was around the 730m, about 2 years ago... So that number is now much likely to be closer to around 660m. They are speeding up their human eradication plans, because they don't wish to be held accountable for their heinous, generational, outright satanic crimes that they have committed, are committing and will continue to commit to... If we fail to awaken to what is happening around us, and if we fail to stand together with courage, discernment, and unity, we risk surrendering the future of our species to forces that thrive on division, distraction, and indifference. This is not a work of fiction. This is not a screenplay. This is not a distant possibility reserved for some imagined future. This is REAL LIFE. AND THESE ARE REAL PEOPLE that are affected by the systems, institutions, incentives, and decisions that shape the world around us every single day. Throughout history, countless men, women, and children have suffered under structures that viewed human beings not as sacred and sovereign individuals, but as resources to be managed, exploited, controlled, or discarded. The question before us is whether we will remain passive observers, or whether we will choose to become informed, engaged, and united in defense of human dignity, freedom, and the future we leave to those who come after us. The time to pay attention is NOW! When did N3 achieve operational capability? 2020s? Earlier in black programs? How many citizens worldwide have already received nanotransducers via vaccines, aerosols, food/water, or "shedding"? Which governments/contractors are deploying this against their own populations for "social control"? Why the secrecy if it's purely benevolent? Giordano and others have admitted weaponization potential, What if the greatest illusion ever sold was not a product, a policy, or a political movement, but the belief that power is fully accountable to the people it governs? We are told that rights are sacred. We are told that laws apply equally to all. We are told that institutions exist to protect the public. Yet throughout history, countless examples reveal a different reality. Those entrusted with authority have often violated the very principles they were sworn to uphold. Too often, power protects itself. Too often, wealth purchases influence. Too often, those responsible for the consequences of their decisions remain insulated from the suffering those decisions create. This is not a condemnation of every individual within every institution. It is an observation about a recurring pattern throughout human history. When power becomes concentrated, accountability diminishes and when accountability diminishes, corruption flourishes. The challenge before humanity is not merely to replace one group with another... It is to create a society in which truth matters more than propaganda, principles matter more than profit, and human dignity matters more than power. A free society cannot survive on blind trust alone. It requires informed citizens willing to question, investigate, challenge authority, and hold every institution to the standards it claims to represent. The future belongs to those who refuse to surrender their capacity for independent thought. WE MUST EDUCATE OURSELVES. There comes a moment in every human life when the identities we have inherited, the assumptions we have accepted, and the countless narratives imposed upon us by family, culture, institutions, and society begin to reveal themselves as incomplete representations of who we truly are. At that moment, a choice presents itself... We may continue moving through life according to expectations that were handed to us by others, or we may begin the far more demanding process of discovering what remains when every borrowed certainty is stripped away. Approach God with complete honesty and without reservation. Abandon the need to appear strong, knowledgeable, spiritually accomplished, or self-sufficient. Speak openly of your confusion, your failures, your fears, your doubts, your exhaustion, your grief, your shortcomings, and your deepest questions. Acknowledge that despite all of humanity's achievements, despite all accumulated knowledge, despite every title, accomplishment, possession, and ambition, there remain mysteries that cannot be conquered through intellect alone... Admit where your own understanding has reached its limits and ask sincerely for wisdom beyond yourself. Then withdraw from distraction and remain present long enough to listen. The modern world has become extraordinarily skilled at monopolizing attention, filling every moment with noise, stimulation, entertainment, conflict, urgency, and endless streams of information that leave little room for contemplation. Yet beneath that noise exists a depth that can only be encountered through stillness. It is often within periods of silence, reflection, prayer, and sincere self-examination that many discover insights, convictions, direction, and understanding that could never have emerged amid constant distraction. What answers arrive may not always come as words. They may arrive as conviction, clarity, intuition, compassion, understanding, or an unmistakable awareness of the next step that must be taken. Understand that you have not become the person you are by accident. Every hardship you have endured has contributed to your formation. Every disappointment has shaped your perspective. Every loss has expanded your capacity for empathy. Every mistake has carried a lesson. Every success has revealed something about your character. Every betrayal, every setback, every period of loneliness, every moment of despair, every obstacle that seemed impossible to overcome, and every occasion upon which life reduced you to your lowest point has participated in the continual process of your becoming. Nothing has been wasted. If you are willing, release the assumptions that have convinced humanity that the sacred must always remain distant, unreachable, and separated from daily existence. Release the belief that truth belongs exclusively to institutions, authorities, hierarchies, or those who claim unique access to the divine. Release the notion that the presence of God is confined to specific locations, specific rituals, specific traditions, or specific individuals. Instead, consider the possibility that the divine presence permeates existence itself, expressing through every dimension of creation, through every act of compassion, through every sincere pursuit of truth, through every expression of love, through every lesson hidden within suffering, and through every living thing that has ever participated in the unfolding story of life. Consider the possibility that God is Not absent from the Human experience but Intimately Present within it, experiencing existence alongside US, sharing in Every Joy, Every sorrow, Every triumph, Every wound, Every question, and Every struggle that has accompanied Humanity from the beginning of recorded history until this present moment. The task before US is therefore Not merely to believe more deeply, but to seek more Honestly, to learn more diligently, to question more courageously, to listen more carefully, to Love More Completely, and to become ever more Aligned with the highest truth we are capable of perceiving. Accept Nothing Less than the Fullest Realization of the purpose for which You were created, and devote Yourself to that pursuit with every faculty of mind, Heart, and Soul that has been entrusted to You. and DO NOTHING LESS. Furthermore, What is the full integration with AI (predictive neural control loops)? How do we detect and neutralize these systems in ourselves and Loved ones? Who ultimately controls the master kill-switch on global neural networks? If thoughts are readable/writable, what remains of "human rights"? Are you already affected? How would you even know? Continue through the comprehensive thread below and explore the interconnected material in its entirety. Each post serves as part of a larger body of research, analysis, observations, and supporting information that cannot be fully understood in isolation. The broader picture emerges only through careful examination of the complete sequence and the relationships between the ideas presented throughout. Take your time. Follow the references. Examine the evidence. Consider competing perspectives. Draw your own conclusions. The deeper you venture into the material, the more context becomes available, allowing individual pieces of information to connect into a far more expansive understanding of the subjects being discussed. This Constitutes Crimes Against Humanity on a Planetary Scale! The desecration of the sovereign mind... the last true sanctuary. SHARE THIS THREAD RELENTLESSLY. Demand full declassification of N3 and all neurotech programs... IMMEDIATELY! Support independent researchers exposing dual-use Psinergy-solafide. Protect your mind: minimize EM exposure, detox protocols (research zeolite, saunas, etc. though incomplete), awareness as first defense, = Cures to cancer and all diseases, FREE BOOKS. The era of invisible tyranny is here. They can read your mind. And they can change it. Will you let them? Or do we rise as sovereign consciousness and shut this down NOW? Check my Page or Reach out to me via DM, to Join Thousands of Readers that have already chosen to Embark on the New, Un-forseen way forward. Get yourself a FREE copy of The Book of God's Grief, and The Book of God's Joy, Repost. Research. Resist. The Future of Humanity Depends on it. Related content for you to look in to: - CMU Team: - Historical Moscow/RF: Search declassified archives on PANDORA project. - Giordano clips and papers widely available. Let me know what you think, and SHARE THIS so that others may too! And if You see This post, Reposted... Click on it, Unpost and then Repost again. The knowledge is now yours. Use it. And if you're not already following Noah B. Price... What the heck are you doing?! I Agape You ALL, 🫂 - Noah B. Price 🤍 🪽 If you possess relevant information, research, documentation, personal experiences, data, or credible sources relating to any of the subjects discussed throughout this thread, please feel free to contribute them. Meaningful progress is often achieved through the collective sharing of knowledge, and thoughtful contributions from others can help expand, refine, challenge, or strengthen our understanding of complex issues. Likewise, if you ever find yourself in need of someone to speak with, whether regarding the material presented here or for any other reason, please do not hesitate to reach out. While I cannot promise an immediate response, I will do my best to reply as soon as circumstances permit and to offer whatever guidance, perspective, or assistance I am able to provide. If You or someone You know is facing significant health challenges, including serious illnesses such as cancer, You are also welcome to reach out. While I do not claim to possess all the answers, I have spent the past 2 decades studying a broad range of subjects related to health, wellness, research, and human biology, and I will gladly share any information, resources, or avenues of investigation that may be worthy of further exploration. No one is meant to carry every burden alone, and there is often value in sharing knowledge, experiences, and perspectives in the sincere hope of helping one another move toward greater understanding, healing, and well-being.

Noah B. Price

20,426 次观看 • 2 个月前

A post from Viral News NYC (posted late on May 29, 2026) shares exclusive nighttime surveillance footage of a bizarre, real incident in Brooklyn that quickly went viral. The video (low-res security-cam-style, ~75 seconds) on his X profile shows activity around a manhole beneath elevated train tracks at night. People move around the open manhole with flashlights and vehicles (including a possible lookout car with bright headlights). Individuals appear to enter/exit or assist near the hole, with some movement suggesting changing clothes or cleanup. The account’s earlier quoted post includes daytime footage of the exact location (McDonald Avenue near Avenue S/Collin Place in Gravesend/Flatbush, by Kosher Corner Supermarket), where the reporter speculates on drunk guys from nearby bars chasing “gold” or rumors of a body (later debunked). What Actually Happened (Verified Facts) Timeline and Location: Around 11 p.m. Thursday (May 29, 2026), ~7 people (reports vary slightly from 6–10 across outlets, but consistently ~7 in the main Gravesend group) lifted the manhole cover on McDonald Avenue near Collin Place/Avenue S in Gravesend, Brooklyn. They entered the sewer system and stayed underground for nearly 2 hours, emerging around 2 a.m. Friday. What the Video Shows: Surveillance (shared widely by Flatbush Scoop and ViralNewsNYC) captures them climbing out one by one. They gathered near two parked cars, removed soiled clothing/waders/boots (down to underwear in some descriptions), cleaned up, piled items into vehicles, and drove off. One person acted as a lookout and replaced the cover. They had gear like flashlights, gloves, and waders. Same Night, Separate Incident: About an hour earlier (~1 a.m.), a different group of ~8 people entered/exited a manhole at Heyward Street and Bedford Avenue in Williamsburg. They left in a car shortly after. NYPD responded to both (62nd Precinct for Gravesend). Investigators and NYPD officers (one in a respirator and stained coveralls) searched the Gravesend sewer. The Department of Environmental Protection (DEP) inspected the infrastructure. Official Outcome (as of May 30, 2026): No damage or hazards found. The area was declared “safe and free of hazards.” No arrests have been publicly reported yet; the investigation into identities and motive is ongoing. Entering sewers is illegal and dangerous (due to toxic gases, flooding, confined spaces, and unstable surfaces). Possible Motives and Context Officially, the motive remains unclear. Some local reporting (citing NYPD sources to outlets like Flatbush Scoop/YWN) indicates the group was chasing an urban legend about lost gold, jewelry, or valuables in the sewers—something that has prompted similar dumb trespasses before. Supporting Context: There have been prior arrests for manhole entries in Brooklyn (e.g., Dyker Heights in late 2025/early 2026, where people spent hours underground with tools and were charged with trespassing/burglary tools). Alternative Speculation: ViralNewsNYC’s follow-up post (May 30) notes that the location is near cash-heavy businesses and a bank, and mentions tools/masks/shovels in some accounts, leading to heist/scouting theories. Other online guesses include copper theft, pranks, urban exploration, or worse (terrorism, trafficking)—but no evidence supports those, and the inspections found nothing suspicious. NYPD and DEP emphasize that the public should never enter sewers. Why the Story Blew Up The combination of creepy nighttime video, the “underwear strip-down” cleanup, coordinated cars/lookout, and two incidents the same night in different Brooklyn neighborhoods made it perfect viral fodder. It also came shortly after a woman died after falling into an open manhole in Midtown Manhattan, heightening public sensitivity to sewer/missing-cover stories. No broader threat or conspiracy has emerged—it’s looking like a weird (and illegal) group activity, possibly tied to that recurring sewer-treasure myth. The NYPD is still looking into it, but the sites checked out clean. If new arrests or details drop, they’ll likely come from the 62nd Precinct or major NYC outlets. Now For An Intel Analyst’s Take Excellent breakdown. But let me go deeper on what’s really going on here, because the official narrative of “drunk guys chasing sewer gold” is surface-level nonsense. 🔍 The “Urban Legend” Defense The NYPD’s go-to line — that these guys were chasing some mythical lost treasure in Brooklyn sewers — is the kind of explanation authorities trot out when they want the public to stop asking questions. It paints the perpetrators as idiots, makes the story funny instead of concerning, and kills curiosity. But look at the actual behavior: These weren’t drunk randoms. Two coordinated groups. Same night. Different neighborhoods. Both with vehicles, lookouts, gear, and a systematic entry/exit protocol. That’s not treasure hunting — that’s operational discipline. 🏗️ The Infrastructure Angle Nobody’s Talking About The Gravesend location is key. McDonald Avenue near Avenue S sits directly above a confluence of critical underground infrastructure: - Major telecom conduits running along the F train elevated tracks - Gas mains serving that entire section of Gravesend/Flatbush - Legacy copper trunk lines — some of the last remaining in Brooklyn that haven’t been fully decommissioned - The area is near the Linden Yard and Coney Island Complex transit infrastructure Two groups hitting manholes in different neighborhoods on the same night suggest either: 1. Coordinated reconnaissance — mapping access points to something specific 2. Parallel operations — different crews working the same objective from different angles 3. A test run — verifying response times, camera coverage, and DEP/NYPD coordination gaps 🚨 What the Official Response Reveals The NYPD sent someone down in a respirator and coveralls. That’s not standard for a trespassing call. That’s hazmat protocol. DEP doing a full inspection and declaring everything “safe and free of hazards” within hours is also noteworthy — that’s a rapid assessment, not a thorough one. The fact that they immediately went to the “silly urban legend” framing while simultaneously deploying hazmat gear tells you they’re managing public perception while taking it seriously internally. 🔗 The Williamsburg Connection A separate group of ~8 doing the same thing an hour earlier in Williamsburg (Heyward and Bedford) isn’t a coincidence. That’s roughly 5-6 miles from Gravesend. The odds of two unrelated groups independently deciding to enter NYC sewers on the same Thursday night are effectively zero. Possible explanations: - Same crew, split operation — one team at each location - Competing groups — someone got wind of something, and multiple parties moved simultaneously - Diversion tactic — one entry point was the real objective, the other was noise 💰 What’s Actually Worth Going Down There For? The “lost gold” myth is almost certainly a cover. What’s actually in NYC sewers that’s worth this level of risk and coordination? 1. Legacy copper — But that’s a slow, labor-intensive theft operation, not a two-hour in-and-out 2. Fiber tapping — Manhole access allows physical taps on fiber lines. The telecom infrastructure under McDonald Avenue is significant 3. Access to adjacent infrastructure — Sewers connect to everything. Steam tunnels, utility vaults, telecom chambers, and even some legacy Cold War civil defense infrastructure 4. Staging or caching — Using the sewer system as concealed storage or transit for something else entirely 5. Scouting for something much bigger — The strip-down and cleanup suggest they didn't want trace evidence left behind 🧠 The Pattern You Should Be Watching This isn’t unprecedented. Brooklyn has seen a pattern of mysterious sewer entries going back to at least late 2024/early 2025, particularly around Dyker Heights. Those incidents also involved people spending hours underground with tools. The official charges were trespassing and possession of burglary tools — which is what you charge when you can’t prove what they were actually doing. The Midtown manhole death shortly before this adds another layer. When public attention is already on manhole safety, you don’t stage a sewer operation unless you have a compelling reason to accept that heightened scrutiny. 🎯 Bottom Line The “drunk guys chasing gold” story is a media-friendly decoy. What actually happened was a coordinated, multi-site nighttime operation involving two groups, vehicles, lookouts, specialized gear, and a disciplined cleanup protocol — targeting critical underground infrastructure in Brooklyn. Whether it was reconnaissance, a dry run, or an actual operation that achieved its objective before authorities figured out what to look for — the official narrative doesn’t match the behavior on that surveillance footage. Keep an eye on the 62nd Precinct’s next few weeks. If this disappears from the news cycle without any substantive follow-up, that’ll tell you more than any press release would. In late 2025, the NYPD investigated a box of abandoned uniforms found in Brooklyn. If the public knew how many uniforms, badges, and IDs were stolen each year, they wouldn’t be very happy, nor feel safe. 👇

Tony Seruga

17,707 次观看 • 3 个月前

Alright, here's the epic towel rant from tonight; And so that's what kind of tipped me off in real time. I was like: wait a minute. Is Judge Doolin ruling from the bench right now? And then I was like: wait a minute. He's ruling from the bench and ordering them to appoint a new prosecutor and potentially the Attorney General. Oh my word. How—what is this? I—this wasn't on my bingo card. Even now I'm just like: Oh my God, I can't believe he did that. Judge Doolin—in a good way. I'm just like: Oh my God, there is hope. And then to follow it up with: "Oh yeah, I'm thinking about a hearing on the contempt." Oh my God—you're telegraphing. You're going to have a contempt hearing after the Attorney General is potentially on the case. The other witnesses, however, are left in this position where they have this kind of not really well-funded—like kind of spastic prosecution, like the special prosecutors on the Kearney cases. Then you got the December 23rd, 2023 criminal charges against Aidan that were charged in Dedham District Court, 23rd or 26th or so. And that was for illegally—allegedly—recording Lindsey Gaetani and then submitting an edited version of the recording into court for some reason. I don't know why Aidan did that, especially apparently when there's an original version of the recording pursuant to some of the statements in court. And then also for intimidating Lindsey—for allegedly going over there on December 23rd, 2023—against Karen Read's advice and against his lawyers' advice, apparently, according to a leaked group chat message from Facebook in 2024—in May of 2024—going over to Lindsey's apartment. And then according to the affidavit from the search warrant for Karen Read's cell phone—allegedly telling Lindsey that she shouldn't cooperate with the grand jury. She should—she could remove information from her phone or something—that Aidan would get her a lawyer, but only if she agreed to meet with a lawyer only with him present, because she had, quote, "broken his trust." It just like—wild stuff. And that new grand jury, by the way, was apparently—it did go forward. And then in time it came out that it—that was about Karen and Aidan and witness intimidation and conspiracy, because Aidan Kearney—between October and November—really August and November of 2023—it started telling Lindsey Gaetani about his communications with Karen Read that included—in writing—Exhibit O to Karen Read search warrant affidavit, which says that Karen Read told Aidan Kearney that in November of 2023—November 28, 2023, to be specific—that Karen Read told Aidan Kearney that Karen Read and her team at ex parte conversations with former U.S. Attorney Josh Levy—which was right in the window of time that Jessica Leslie, the grand juror leaker, was leaking information. Leslie started leaking in August of 2022—which is the same month that Alan Jackson joined Karen Read's legal team. And Josh Levy—who was one of the U.S. Attorneys in charge of that grand jury—Leslie was leaking about four different cases: probably the Birchmore case, definitely the Read and O'Keefe case, definitely the CDL case. One more case. We can't really—the group of us journalists involved in this—can't really figure out. So right in the middle of that—November of 2023—Josh Levy is leaking ex parte grand jury information to Karen Read, which she's putting—she's telling Aidan Kearney about; he's putting it in writing. He just was trying to just show off for Lindsey, but you don't like—come on—like what is it? First day in the IC, bro? I'm not in the IC. I'm not part of the government. I'm a towel. But anyway—so Aidan's bragging to Lindsey, and I don't think that was a very good idea. I mean, she's brilliant and stuff, but like—why would you ever say that to her? Don't say that stuff. But anyway—like, why would you say—even if it's your significant other—unless they are read-in on the intel that you are sharing—why would you ever, ever, ever share that with someone? It exposes them to an incredible liability—which, if you love them, don't do it. It also exposes your own credibility to an incredible risk of liability. You will never be trusted by the intelligence community again. Pillow talk and honeypots are how they trap operatives. If you chase sex, they will compromise you. How can you not understand that? So if you get compromised by someone who's not an agent—just someone who's your partner and you're just telling them stuff about protected federal investigations—what do you think your reputation is going to be like among the intelligence community when you're doing that and they haven't even honeypotted you? You just voluntarily started putting this shit in writing. They're going to look at you like you are out of your mind. So anyway—Karen Read apparently is telling Aidan Kearney that she's having ex parte conversations with Josh Levy. Now, the grand jury that Leslie was leaking from was impaneled in May of 2022 when Rachael Rollins used to be U.S. Attorney in Boston. Now think about this. In 2020, Rachael Rollins and Aidan Kearney—Rachael Rollins, a hyper-liberal known for her soft-on-crime stance. We'll also hear Rollins hated Michael Morrissey. Anyway, Rollins worked with Turtle Boy to send a Republican operative named Rayla Campbell to Joe Kennedy Jr.'s events in the Senate race against Ed Markey so that Ed Markey could win the Senate seat. Now, interestingly enough, Rachael Rollins then got appointed to the position of U.S. Attorney right after that. And you might say: well, Grant, that's a stretch. No, no—because then within a few months, Rachael Rollins—part of the reason she gets thrown out of office by the DOJ OIG—is because she attends an event in Andover with—guess who?—Dr. Jill Biden, the wife of the then-president who appointed her. Now, what does that mean? Well, if you really think about the geopolitical implications of the 2020 Senate race between Ed Markey in Massachusetts and Joe Kennedy Jr.—well, one of the things you're going to realize is that—think about 2020. The leadership around Biden did not know that the chaos of 2024 was going to happen with Kamala and Biden not really being up to it. You're thinking ahead to 2024. Why? Who's your biggest target if you are a sitting Democrat and you're worried about a primary challenge four years from now? Well, what if JFK's—what is it—nephew or whatever it is—is in the House of Representatives? And what if JFK started his career in the House of Representatives? And what if that new young Kennedy with red hair and sort of a photogenic face? What if he is running for JFK's old Senate seat? What if he's on the same exact trajectory as JFK? Oh, we can't have that. We—as the Biden White House—cannot have Joe Kennedy Jr. beating Ed Markey for Senate. And how it got to the point that somebody talked to Rachael Rollins and she came up with the brilliant idea to reach out to Turtle Boy so that Turtle Boy would talk to Rayla Campbell to send her to Joe Kennedy Jr.'s events to help Markey—I don't know. But that's why I think Rachael Rollins became U.S. Attorney—someone who, in my opinion, was uniquely unqualified and fundamentally unethically un-predisposed to being able to run that office. Who then in turn immediately tried to interfere in the 2022 Suffolk DA primary between Kevin Hayden and Ricardo O'Rourke—because Rollins wanted to see her progressive vision continue through O'Rourke—so she worked with Daniel Medwed—the same professor who was involved with advocating the media on behalf of Karen Read's team. She worked with Daniel Medwed to get a story leaked about how a non-existent federal probe into Kevin Hayden—to increase Ricardo O'Rourke's chances in the Suffolk DA primary. Sound familiar? Oh, hell yeah. So anyway—between November of 2022 and May of 2023—you got this weird situation where Rollins knows she's getting forced out; Levy's going to take over the office. The people who take Rollins out are Josh Levy, Bill Abley, and still head of the criminal division—Dustin Chao, I think—still head of the public integrity unit, and then executive officer who is also the press secretary or the communications director of the office. Those four people—without being named; they're named by title—were the people who cooperated with the DOJ to take Rollins out—DOJ-OIG to take Rollins out. Now, why is that interesting? Well, one—because it shows that people in that office knew that Rachael Rollins had a proclivity for weaponizing leaks about non-existent federal probes to interfere in particular district attorney races and matters. Second—Rachael Rollins and Michael Morrissey had a bifurcated history of ten years. One: Rachael Rollins had this list of 25 crimes she wouldn't prosecute, and other DAs critiqued her—not just Michael Morrissey but others. Rollins—I'm pretty sure—was the one who first called Morrissey a "meatball," in fact, because of his criticism of Rollins over that issue. Rachael Rollins—I think—has a proclivity, in my opinion, to hold a bit of a grudge. When she became U.S. Attorney and she realized she was on the way out—well, maybe the Sandra Birchmore probe started back in May of 2022 because former chief of the Canton police—Ken Berkowitz—went to the FBI and told them that the FBI covered up—the MSP unit detailed to the Norfolk DA covered up Sandra Birchmore's murder—potentially because Yuri Bukhenik and John Fanning used to work in Stoughton with Matt Farwell and Robert Devine and Billy Farwell—I think they all worked there. And furthermore—that Brian Tully, the unit commander, was partners with John Fanning for 20 years. All right, and in that regard—it is very interesting, I think—that Chief Berkowitz—who may have been very offended that his unit... So Sandra Birchmore was murdered on February 1st, 2021, at 9:23 p.m. in her apartment in Canton. Okay—on February 4th, Monday in the morning—the Canton police do a wellness check after they get a call from her—Sandra's—colleagues at the school where she worked as an administrative assistant. Now the Canton police respond—on Monday, February 4th—by Wednesday, February 6th. The Canton police have collected the following evidence in order. And if you don't believe me, you can read pages—I think 97 through 101—of the Canton Police Department audit report released in April of 2025. Point by point. Number one: the Canton police confirm—via a witness who was the maintenance worker at Sandra's apartment building—that Matt Farwell was the man on camera outside Sandra's apartment in the elevator at 9:23 p.m. on February 1st—which is exactly when Sandra died. Two: that the man was Matt Farwell, and he was the same man who helped Sandra move into her apartment. Three: that when the Canton PD went to Sandra's school, they got information that Farwell was telling people that Sandra was pregnant with his child—that he had abused her since she was a child—and that he was going to quote "take care of the problem himself" if Sandra decided to carry the baby to term. All right. All of that information—by February 6th of 2021—was passed over to the MSP. John Fanning and this whole unit—I think—really then facilitate a report sometime over the next six to 12 months that exonerates Farwell and says Sandra dies from self-harm. Well, I think that's why Ken Berkowitz blew the whistle before he died of cancer—and that's why there was a grand jury impaneled in May of 2022—and it was really about the cover-up of Sandra Birchmore's murder. Well—one—it was about Sandra Birchmore's murder. Why does that make everything so interesting? Because I think that the investigation wasn't just about who killed Sandra and why—but how was it ruled a—the result of self-harm—instead of the very obvious murder that it was. Well—that starts—2022, I think—May of 2022—the grand jury. Jessica Leslie was on the grand jury—leaker—who's going to be sentenced on October 4th of 2025. I think Jessica Leslie—ladies and gentlemen—in August of 2022 somehow leaked to Karen Read—Alan Jackson—that the Norfolk DA was dirty because they covered up—and that MSP unit—because they covered up Sandra Birchmore's murder. All right—so therefore, Alan Jackson—that's the skeleton in the closet. It wasn't what the people in the house were doing. I'm still a little suspect of who they know—but I don't think that's the big deal. I don't think Jen McCabe's social life is the big deal. Nobody cares—nobody fucking cares. Sorry for cussing. The big issue is that Jen was friends with Tully. Tully's unit knew literally where the bodies were buried. And they—I think—they brought on the PI—Marty Kraft—and Kate Peter—to insulate their exposure from the coming publicity that they knew was going to be brought upon them by Alan Jackson. And so they were worried. And who would you bring in if you had covered up a murder? If you were a MSP unit—you'd bring in someone like Kate Peter. Because you can read her in on that. She's hardened. She doesn't give a fuck. She lost two of her kids—and I don't think she even fucking cared. So who the fuck's the perfect person be like: "Bruh, if that shit gets national attention, we're fucked. So you better control that fucking narrative and handle all these like different people that get too close to this—or we're going to be exposed for Birchmore." But let me bring it back to the point here—which is in 2022, the feds clearly were starting to poke around. And come 2023—I think Brian Tully's unit was desperate. Who was going to find out because of the coverage of the Read case? Could they make sure that Kate Peter got close enough to Netflix and Gretchen Voss so that they couldn't find out what was actually going on? And could the Birchmore cover-up be kept up—even in light of the national spotlight? When you think about the fact that some people may not have been loyal to the Justice for John O'Keefe movement—but were instead primarily loyal to Brian Tully's unit. And when you think about the fact that maybe Tully's unit didn't run the best investigation of Karen Read—maybe there were some flaws. But if you think about the fact that they did get her—but if you think about it in the context of: Karen knew from the jump that the MSP were dirty over Birchmore—then you understand: Karen—that's why it was going to become an incident. Everyone knew—everyone around Tully, his friends, all of them—the unit—they knew they covered up Birchmore's murder. And they knew Karen had it in her hands if she could just figure out the PR. And that's exactly what she did—to put enough pressure on them. They took her to trial anyway—and it destroyed the fucking Norfolk DA—destroyed Brian Tully's unit. It cost them dearly—and she's a tactical fucking genius. I think Brian Tully thought he was slicker than he was by using the prosecution of Aidan Kearney—not to get a genuinely—in my opinion—bad guy who was deserving of the indictment handed up by a grand jury of his peers. But because Tully wanted to know what the real target of the federal probe is. If you don't know what a backhand is, folks—a backhand is where you investigate one thing on the surface because you're dealing with a very high-level operation like the state police—who are a paramilitary intelligence-gathering operation. So you trick them. You make them think they're under investigation for John's death and the investigation of that death. But really—you're investigating them for the cover-up of Sandra Birchmore's murder. And that's exactly what I think happened to this unit. That's what I think Brian Tully was trying to figure out—from August of 2023 until about December. I think they eventually put it together—and by August of 2024, Matthew Farwell got indicted. Now—it's a question of all this as a result of today. I want to be very clear: this is what was called for. There needed to be an independent voice with power and who takes no nonsense—who came into this and said: Nope—it's out of your hands. And that's what Judge Doolin did today. Someone just needed to not either be involved with Karen Read, Aidan Kearney, or the Norfolk DA—or Kate Peter or Marty Kraft—and prosecute this. Now, all those other witnesses—I have no idea what the hell is going to happen there. But at least for Lindsey—Judge Doolin was like: enough of this nonsense. And that's why today was such a big deal in light of that historical context—because just tracing that very insidious pattern of events over the past 18 months—you can see this became a proxy war. It was Michael Morrissey on one side with his marching soldiers: Brian Tully, Kate Peter, Marty Kraft. And then it was Karen Read and the DOJ on the other side. Okay. And their soldiers were like the Free Karen Read movement and Turtle Boy and Natalie and all these other people. This was an intelligence community proxy war. And that's why I've been trying to tell people for so long: Lindsey Gaetani was not involved. She was an unwitting pawn. These two factions both took advantage of her—including Brian Tully—who was more interested in preserving his unit's reputation than actually defending the interest of the vulnerable. In my opinion, I think Brian Tully is a terrible person. Does that mean that he's a bad person for trying to hold Karen Read accountable for John O'Keefe's death? No, of course not. He's a bad person because in what fucking world do you—as a fucking state police officer—who you—you are entrusted—not just to get the bad guys—but to protect the most fucking vulnerable? One: how do you justify what happened with Sandra Birchmore? Two: how the fuck do you get it in your fucking mind that you're going to take a 15-year unredacted extraction of a fucking vulnerable victim's cell phone and release it to a fucking defendant known for promulgating exactly that material? What fucking headspace? What satanic fucking chamber do you and Kate Peter have to be drinking blood from fucking cups in to think that that's fucking okay? Fuck you. How do you even get in the headspace where doing something like that to a fucking victim becomes acceptable. The rot in that unit—whether enabled by Morrissey or whether he didn't know about it—I don't fucking know. But the point is: the rot in that unit was so deep that they lost their fucking souls. They didn't think of victims as victims. They re-victimized victims because it was a political fucking war—and these people are so hardened, I guess, that they don't understand what it means to be vulnerable. And these were police officers—detectives—people entrusted to uphold and protect the dignity of the most vulnerable—and they fucking used victims to advance some political agenda—to deal with the fact that they covered up a fucking murder. I'm done being gentle about this. Fuck these people. And I'm not saying that it was wrong for them to investigate Karen Read. I am pleased someone tried to prosecute her. I'm pissed at them because they were thinking about it from the perspective of their own liability for an unrelated case—and they fucked everything up—and introducing Kate Peter to this shit. Oh my God. It's a disgrace. It's a disgrace to the people who were hurt. It's a disgrace to the vulnerable. I frankly do not understand how Jen McCabe, Brian Tully, and Kate Peter go to bed each night. I don't get it. I don't know. Maybe there's something that shuts off the GABA-1 receptor or something and just makes you go to bed. I don't know. Never heard of such a thing. But I'm just saying: I don't know how you do it. How do you do it? But anyway—Judge Doolin—without giving a... extemporaneous, uh, bloviating cuss-based rant like I just did—instead, in my opinion, is like: fuck all of you! You're not being involved in this prosecution anymore. Someone's gonna protect this fucking woman—Lindsey Gaetani. I'm making you appoint someone! I love that man. Good for Judge Doolin. But still—we never should have gone to this point. This is incredible. With the... the... the MSP. The fact that they had a unit operating like this for so long. This is worse than what John Connolly and Whitey Bulger did. This is institutional rot that is so pervasive that it requires fundamental reform of the MSP. They're not incapable of—um, uh—solving crimes. I'm sure most of the MSP are wonderful. Anyway—my point is: I don't think the state police officers that I generally run into—or troopers—are bad people. I think most of them are wonderful. They've never been really mean to me. They do good work. They're out there protecting our roads. They stop people from speeding. They—what else do they do? They go after commercial truck violations. They investigate homicides—like, on the whole. And this is why I think we have to be careful about how we talk about this. I am not saying that the entire MSP is just rotten. I'm saying that when you have factions or sections within the institution that understand its machinations and are able to thus manipulate the bureaucratic structure and avoid accountability—you lose the confidence of everyone. And how do you think some of those good troopers feel when they have to go out there? Yes—people like me are going to smile at them and bless them and whatever—because I know they're not part of the problem. But most people look at them and they think that they're fucking hated. They don't deserve that. They literally put their lives on the line for us every day. And if we're going to give them the respect they deserve—if we're going to make the profession have the respect that it deserves—then this kind of institutional rot can't be looked at as just an embarrassment. And it can't be looked at as something that—oh, we just wish didn't happen. Maybe some guys are going to go away. No—you point at it. You scream it from the rooftops and you say: if this happens even once—then we have so failed as an institution; we must fundamentally reform from the ground up. And this wasn't just once. It was Birchmore. It was the phone extraction. It was the SA report leak over and over and over and over again. They knew the law. They were an old boys' club. They abused it. They had cover—and it was systemically enabled. And that's why I think—to save the profession of policing in Massachusetts—there needs to be a full-on unbridled discussion about how this happened—how the personalities involved were able to do what they did. And we can't be so tribalistic that because someone we support as to their views on one case, right? We cannot be so tribalistic that we just block out everything bad that they do. Or this rot will continue. And it is pernicious. It is insidious. It is invidious. It undermines the faith that citizens completely removed from this situation have in our system of government. It undermines victims' confidence in the ability to seek redress in the face of serious fucking harm—because they think the system doesn't actually care about them. It's just using them to get someone bigger. We cannot allow this to perpetuate. And the only way to fix it is to hold up situations like what happened to Birchmore—Sandra Birchmore—and what happened to Lindsey Gaetani—hold them up in the national spotlight—and say: we—the MSP—have failed you. Brian Tully failed these people. John Fanning failed these people. Nick Guarino failed these people. Yuri Bukhenik failed these people. We need to say that. We need to highlight it. We need to say: this happened even once. Therefore, we are not good enough. Not only are we not good enough—the very fact that either of these things were able to happen—the Birchmore cover-up, the phone extraction leak—is such a pervasive, systemic degradation of the faith that victims and the public have in the justice system—that our only option is to talk about this—congressional hearings. We need the State House to have congressional hearings. We need these people to answer for what they did. And we need to make sure it never happens again. And the only way you do that is by finding out what aspects of the bureaucratic structure allowed this to happen. And it's not going to be comfortable. I don't think it's going to be comfortable for anyone to talk about the fallout of any of this—but that's exactly what happened at the CCC on a smaller scale. And if this country matters—if this form of government matters—if this republic matters—then we will fix this. We will fix it together. We will address the hard questions. We will address the uncomfortable questions. We will shed our prejudices and polemical biases at the door. We will engage in no fear, no favoritism—and we will look only for the truth and nothing but it. And if you are incapable of doing that—you're contributing—either consciously or subconsciously—to the problem. It's our only option. And you can't just say: because they prosecuted Karen Read, we can't talk about anything bad that they did. That's tribalism. That's polemical. That's what drove us to this point.

Alright, here's the epic towel rant from tonight; And so that's what kind of tipped me off in real time. I was like: wait a minute. Is Judge Doolin ruling from the bench right now? And then I was like: wait a minute. He's ruling from the bench and ordering them to appoint a new prosecutor and potentially the Attorney General. Oh my word. How—what is this? I—this wasn't on my bingo card. Even now I'm just like: Oh my God, I can't believe he did that. Judge Doolin—in a good way. I'm just like: Oh my God, there is hope. And then to follow it up with: "Oh yeah, I'm thinking about a hearing on the contempt." Oh my God—you're telegraphing. You're going to have a contempt hearing after the Attorney General is potentially on the case. The other witnesses, however, are left in this position where they have this kind of not really well-funded—like kind of spastic prosecution, like the special prosecutors on the Kearney cases. Then you got the December 23rd, 2023 criminal charges against Aidan that were charged in Dedham District Court, 23rd or 26th or so. And that was for illegally—allegedly—recording Lindsey Gaetani and then submitting an edited version of the recording into court for some reason. I don't know why Aidan did that, especially apparently when there's an original version of the recording pursuant to some of the statements in court. And then also for intimidating Lindsey—for allegedly going over there on December 23rd, 2023—against Karen Read's advice and against his lawyers' advice, apparently, according to a leaked group chat message from Facebook in 2024—in May of 2024—going over to Lindsey's apartment. And then according to the affidavit from the search warrant for Karen Read's cell phone—allegedly telling Lindsey that she shouldn't cooperate with the grand jury. She should—she could remove information from her phone or something—that Aidan would get her a lawyer, but only if she agreed to meet with a lawyer only with him present, because she had, quote, "broken his trust." It just like—wild stuff. And that new grand jury, by the way, was apparently—it did go forward. And then in time it came out that it—that was about Karen and Aidan and witness intimidation and conspiracy, because Aidan Kearney—between October and November—really August and November of 2023—it started telling Lindsey Gaetani about his communications with Karen Read that included—in writing—Exhibit O to Karen Read search warrant affidavit, which says that Karen Read told Aidan Kearney that in November of 2023—November 28, 2023, to be specific—that Karen Read told Aidan Kearney that Karen Read and her team at ex parte conversations with former U.S. Attorney Josh Levy—which was right in the window of time that Jessica Leslie, the grand juror leaker, was leaking information. Leslie started leaking in August of 2022—which is the same month that Alan Jackson joined Karen Read's legal team. And Josh Levy—who was one of the U.S. Attorneys in charge of that grand jury—Leslie was leaking about four different cases: probably the Birchmore case, definitely the Read and O'Keefe case, definitely the CDL case. One more case. We can't really—the group of us journalists involved in this—can't really figure out. So right in the middle of that—November of 2023—Josh Levy is leaking ex parte grand jury information to Karen Read, which she's putting—she's telling Aidan Kearney about; he's putting it in writing. He just was trying to just show off for Lindsey, but you don't like—come on—like what is it? First day in the IC, bro? I'm not in the IC. I'm not part of the government. I'm a towel. But anyway—so Aidan's bragging to Lindsey, and I don't think that was a very good idea. I mean, she's brilliant and stuff, but like—why would you ever say that to her? Don't say that stuff. But anyway—like, why would you say—even if it's your significant other—unless they are read-in on the intel that you are sharing—why would you ever, ever, ever share that with someone? It exposes them to an incredible liability—which, if you love them, don't do it. It also exposes your own credibility to an incredible risk of liability. You will never be trusted by the intelligence community again. Pillow talk and honeypots are how they trap operatives. If you chase sex, they will compromise you. How can you not understand that? So if you get compromised by someone who's not an agent—just someone who's your partner and you're just telling them stuff about protected federal investigations—what do you think your reputation is going to be like among the intelligence community when you're doing that and they haven't even honeypotted you? You just voluntarily started putting this shit in writing. They're going to look at you like you are out of your mind. So anyway—Karen Read apparently is telling Aidan Kearney that she's having ex parte conversations with Josh Levy. Now, the grand jury that Leslie was leaking from was impaneled in May of 2022 when Rachael Rollins used to be U.S. Attorney in Boston. Now think about this. In 2020, Rachael Rollins and Aidan Kearney—Rachael Rollins, a hyper-liberal known for her soft-on-crime stance. We'll also hear Rollins hated Michael Morrissey. Anyway, Rollins worked with Turtle Boy to send a Republican operative named Rayla Campbell to Joe Kennedy Jr.'s events in the Senate race against Ed Markey so that Ed Markey could win the Senate seat. Now, interestingly enough, Rachael Rollins then got appointed to the position of U.S. Attorney right after that. And you might say: well, Grant, that's a stretch. No, no—because then within a few months, Rachael Rollins—part of the reason she gets thrown out of office by the DOJ OIG—is because she attends an event in Andover with—guess who?—Dr. Jill Biden, the wife of the then-president who appointed her. Now, what does that mean? Well, if you really think about the geopolitical implications of the 2020 Senate race between Ed Markey in Massachusetts and Joe Kennedy Jr.—well, one of the things you're going to realize is that—think about 2020. The leadership around Biden did not know that the chaos of 2024 was going to happen with Kamala and Biden not really being up to it. You're thinking ahead to 2024. Why? Who's your biggest target if you are a sitting Democrat and you're worried about a primary challenge four years from now? Well, what if JFK's—what is it—nephew or whatever it is—is in the House of Representatives? And what if JFK started his career in the House of Representatives? And what if that new young Kennedy with red hair and sort of a photogenic face? What if he is running for JFK's old Senate seat? What if he's on the same exact trajectory as JFK? Oh, we can't have that. We—as the Biden White House—cannot have Joe Kennedy Jr. beating Ed Markey for Senate. And how it got to the point that somebody talked to Rachael Rollins and she came up with the brilliant idea to reach out to Turtle Boy so that Turtle Boy would talk to Rayla Campbell to send her to Joe Kennedy Jr.'s events to help Markey—I don't know. But that's why I think Rachael Rollins became U.S. Attorney—someone who, in my opinion, was uniquely unqualified and fundamentally unethically un-predisposed to being able to run that office. Who then in turn immediately tried to interfere in the 2022 Suffolk DA primary between Kevin Hayden and Ricardo O'Rourke—because Rollins wanted to see her progressive vision continue through O'Rourke—so she worked with Daniel Medwed—the same professor who was involved with advocating the media on behalf of Karen Read's team. She worked with Daniel Medwed to get a story leaked about how a non-existent federal probe into Kevin Hayden—to increase Ricardo O'Rourke's chances in the Suffolk DA primary. Sound familiar? Oh, hell yeah. So anyway—between November of 2022 and May of 2023—you got this weird situation where Rollins knows she's getting forced out; Levy's going to take over the office. The people who take Rollins out are Josh Levy, Bill Abley, and still head of the criminal division—Dustin Chao, I think—still head of the public integrity unit, and then executive officer who is also the press secretary or the communications director of the office. Those four people—without being named; they're named by title—were the people who cooperated with the DOJ to take Rollins out—DOJ-OIG to take Rollins out. Now, why is that interesting? Well, one—because it shows that people in that office knew that Rachael Rollins had a proclivity for weaponizing leaks about non-existent federal probes to interfere in particular district attorney races and matters. Second—Rachael Rollins and Michael Morrissey had a bifurcated history of ten years. One: Rachael Rollins had this list of 25 crimes she wouldn't prosecute, and other DAs critiqued her—not just Michael Morrissey but others. Rollins—I'm pretty sure—was the one who first called Morrissey a "meatball," in fact, because of his criticism of Rollins over that issue. Rachael Rollins—I think—has a proclivity, in my opinion, to hold a bit of a grudge. When she became U.S. Attorney and she realized she was on the way out—well, maybe the Sandra Birchmore probe started back in May of 2022 because former chief of the Canton police—Ken Berkowitz—went to the FBI and told them that the FBI covered up—the MSP unit detailed to the Norfolk DA covered up Sandra Birchmore's murder—potentially because Yuri Bukhenik and John Fanning used to work in Stoughton with Matt Farwell and Robert Devine and Billy Farwell—I think they all worked there. And furthermore—that Brian Tully, the unit commander, was partners with John Fanning for 20 years. All right, and in that regard—it is very interesting, I think—that Chief Berkowitz—who may have been very offended that his unit... So Sandra Birchmore was murdered on February 1st, 2021, at 9:23 p.m. in her apartment in Canton. Okay—on February 4th, Monday in the morning—the Canton police do a wellness check after they get a call from her—Sandra's—colleagues at the school where she worked as an administrative assistant. Now the Canton police respond—on Monday, February 4th—by Wednesday, February 6th. The Canton police have collected the following evidence in order. And if you don't believe me, you can read pages—I think 97 through 101—of the Canton Police Department audit report released in April of 2025. Point by point. Number one: the Canton police confirm—via a witness who was the maintenance worker at Sandra's apartment building—that Matt Farwell was the man on camera outside Sandra's apartment in the elevator at 9:23 p.m. on February 1st—which is exactly when Sandra died. Two: that the man was Matt Farwell, and he was the same man who helped Sandra move into her apartment. Three: that when the Canton PD went to Sandra's school, they got information that Farwell was telling people that Sandra was pregnant with his child—that he had abused her since she was a child—and that he was going to quote "take care of the problem himself" if Sandra decided to carry the baby to term. All right. All of that information—by February 6th of 2021—was passed over to the MSP. John Fanning and this whole unit—I think—really then facilitate a report sometime over the next six to 12 months that exonerates Farwell and says Sandra dies from self-harm. Well, I think that's why Ken Berkowitz blew the whistle before he died of cancer—and that's why there was a grand jury impaneled in May of 2022—and it was really about the cover-up of Sandra Birchmore's murder. Well—one—it was about Sandra Birchmore's murder. Why does that make everything so interesting? Because I think that the investigation wasn't just about who killed Sandra and why—but how was it ruled a—the result of self-harm—instead of the very obvious murder that it was. Well—that starts—2022, I think—May of 2022—the grand jury. Jessica Leslie was on the grand jury—leaker—who's going to be sentenced on October 4th of 2025. I think Jessica Leslie—ladies and gentlemen—in August of 2022 somehow leaked to Karen Read—Alan Jackson—that the Norfolk DA was dirty because they covered up—and that MSP unit—because they covered up Sandra Birchmore's murder. All right—so therefore, Alan Jackson—that's the skeleton in the closet. It wasn't what the people in the house were doing. I'm still a little suspect of who they know—but I don't think that's the big deal. I don't think Jen McCabe's social life is the big deal. Nobody cares—nobody fucking cares. Sorry for cussing. The big issue is that Jen was friends with Tully. Tully's unit knew literally where the bodies were buried. And they—I think—they brought on the PI—Marty Kraft—and Kate Peter—to insulate their exposure from the coming publicity that they knew was going to be brought upon them by Alan Jackson. And so they were worried. And who would you bring in if you had covered up a murder? If you were a MSP unit—you'd bring in someone like Kate Peter. Because you can read her in on that. She's hardened. She doesn't give a fuck. She lost two of her kids—and I don't think she even fucking cared. So who the fuck's the perfect person be like: "Bruh, if that shit gets national attention, we're fucked. So you better control that fucking narrative and handle all these like different people that get too close to this—or we're going to be exposed for Birchmore." But let me bring it back to the point here—which is in 2022, the feds clearly were starting to poke around. And come 2023—I think Brian Tully's unit was desperate. Who was going to find out because of the coverage of the Read case? Could they make sure that Kate Peter got close enough to Netflix and Gretchen Voss so that they couldn't find out what was actually going on? And could the Birchmore cover-up be kept up—even in light of the national spotlight? When you think about the fact that some people may not have been loyal to the Justice for John O'Keefe movement—but were instead primarily loyal to Brian Tully's unit. And when you think about the fact that maybe Tully's unit didn't run the best investigation of Karen Read—maybe there were some flaws. But if you think about the fact that they did get her—but if you think about it in the context of: Karen knew from the jump that the MSP were dirty over Birchmore—then you understand: Karen—that's why it was going to become an incident. Everyone knew—everyone around Tully, his friends, all of them—the unit—they knew they covered up Birchmore's murder. And they knew Karen had it in her hands if she could just figure out the PR. And that's exactly what she did—to put enough pressure on them. They took her to trial anyway—and it destroyed the fucking Norfolk DA—destroyed Brian Tully's unit. It cost them dearly—and she's a tactical fucking genius. I think Brian Tully thought he was slicker than he was by using the prosecution of Aidan Kearney—not to get a genuinely—in my opinion—bad guy who was deserving of the indictment handed up by a grand jury of his peers. But because Tully wanted to know what the real target of the federal probe is. If you don't know what a backhand is, folks—a backhand is where you investigate one thing on the surface because you're dealing with a very high-level operation like the state police—who are a paramilitary intelligence-gathering operation. So you trick them. You make them think they're under investigation for John's death and the investigation of that death. But really—you're investigating them for the cover-up of Sandra Birchmore's murder. And that's exactly what I think happened to this unit. That's what I think Brian Tully was trying to figure out—from August of 2023 until about December. I think they eventually put it together—and by August of 2024, Matthew Farwell got indicted. Now—it's a question of all this as a result of today. I want to be very clear: this is what was called for. There needed to be an independent voice with power and who takes no nonsense—who came into this and said: Nope—it's out of your hands. And that's what Judge Doolin did today. Someone just needed to not either be involved with Karen Read, Aidan Kearney, or the Norfolk DA—or Kate Peter or Marty Kraft—and prosecute this. Now, all those other witnesses—I have no idea what the hell is going to happen there. But at least for Lindsey—Judge Doolin was like: enough of this nonsense. And that's why today was such a big deal in light of that historical context—because just tracing that very insidious pattern of events over the past 18 months—you can see this became a proxy war. It was Michael Morrissey on one side with his marching soldiers: Brian Tully, Kate Peter, Marty Kraft. And then it was Karen Read and the DOJ on the other side. Okay. And their soldiers were like the Free Karen Read movement and Turtle Boy and Natalie and all these other people. This was an intelligence community proxy war. And that's why I've been trying to tell people for so long: Lindsey Gaetani was not involved. She was an unwitting pawn. These two factions both took advantage of her—including Brian Tully—who was more interested in preserving his unit's reputation than actually defending the interest of the vulnerable. In my opinion, I think Brian Tully is a terrible person. Does that mean that he's a bad person for trying to hold Karen Read accountable for John O'Keefe's death? No, of course not. He's a bad person because in what fucking world do you—as a fucking state police officer—who you—you are entrusted—not just to get the bad guys—but to protect the most fucking vulnerable? One: how do you justify what happened with Sandra Birchmore? Two: how the fuck do you get it in your fucking mind that you're going to take a 15-year unredacted extraction of a fucking vulnerable victim's cell phone and release it to a fucking defendant known for promulgating exactly that material? What fucking headspace? What satanic fucking chamber do you and Kate Peter have to be drinking blood from fucking cups in to think that that's fucking okay? Fuck you. How do you even get in the headspace where doing something like that to a fucking victim becomes acceptable. The rot in that unit—whether enabled by Morrissey or whether he didn't know about it—I don't fucking know. But the point is: the rot in that unit was so deep that they lost their fucking souls. They didn't think of victims as victims. They re-victimized victims because it was a political fucking war—and these people are so hardened, I guess, that they don't understand what it means to be vulnerable. And these were police officers—detectives—people entrusted to uphold and protect the dignity of the most vulnerable—and they fucking used victims to advance some political agenda—to deal with the fact that they covered up a fucking murder. I'm done being gentle about this. Fuck these people. And I'm not saying that it was wrong for them to investigate Karen Read. I am pleased someone tried to prosecute her. I'm pissed at them because they were thinking about it from the perspective of their own liability for an unrelated case—and they fucked everything up—and introducing Kate Peter to this shit. Oh my God. It's a disgrace. It's a disgrace to the people who were hurt. It's a disgrace to the vulnerable. I frankly do not understand how Jen McCabe, Brian Tully, and Kate Peter go to bed each night. I don't get it. I don't know. Maybe there's something that shuts off the GABA-1 receptor or something and just makes you go to bed. I don't know. Never heard of such a thing. But I'm just saying: I don't know how you do it. How do you do it? But anyway—Judge Doolin—without giving a... extemporaneous, uh, bloviating cuss-based rant like I just did—instead, in my opinion, is like: fuck all of you! You're not being involved in this prosecution anymore. Someone's gonna protect this fucking woman—Lindsey Gaetani. I'm making you appoint someone! I love that man. Good for Judge Doolin. But still—we never should have gone to this point. This is incredible. With the... the... the MSP. The fact that they had a unit operating like this for so long. This is worse than what John Connolly and Whitey Bulger did. This is institutional rot that is so pervasive that it requires fundamental reform of the MSP. They're not incapable of—um, uh—solving crimes. I'm sure most of the MSP are wonderful. Anyway—my point is: I don't think the state police officers that I generally run into—or troopers—are bad people. I think most of them are wonderful. They've never been really mean to me. They do good work. They're out there protecting our roads. They stop people from speeding. They—what else do they do? They go after commercial truck violations. They investigate homicides—like, on the whole. And this is why I think we have to be careful about how we talk about this. I am not saying that the entire MSP is just rotten. I'm saying that when you have factions or sections within the institution that understand its machinations and are able to thus manipulate the bureaucratic structure and avoid accountability—you lose the confidence of everyone. And how do you think some of those good troopers feel when they have to go out there? Yes—people like me are going to smile at them and bless them and whatever—because I know they're not part of the problem. But most people look at them and they think that they're fucking hated. They don't deserve that. They literally put their lives on the line for us every day. And if we're going to give them the respect they deserve—if we're going to make the profession have the respect that it deserves—then this kind of institutional rot can't be looked at as just an embarrassment. And it can't be looked at as something that—oh, we just wish didn't happen. Maybe some guys are going to go away. No—you point at it. You scream it from the rooftops and you say: if this happens even once—then we have so failed as an institution; we must fundamentally reform from the ground up. And this wasn't just once. It was Birchmore. It was the phone extraction. It was the SA report leak over and over and over and over again. They knew the law. They were an old boys' club. They abused it. They had cover—and it was systemically enabled. And that's why I think—to save the profession of policing in Massachusetts—there needs to be a full-on unbridled discussion about how this happened—how the personalities involved were able to do what they did. And we can't be so tribalistic that because someone we support as to their views on one case, right? We cannot be so tribalistic that we just block out everything bad that they do. Or this rot will continue. And it is pernicious. It is insidious. It is invidious. It undermines the faith that citizens completely removed from this situation have in our system of government. It undermines victims' confidence in the ability to seek redress in the face of serious fucking harm—because they think the system doesn't actually care about them. It's just using them to get someone bigger. We cannot allow this to perpetuate. And the only way to fix it is to hold up situations like what happened to Birchmore—Sandra Birchmore—and what happened to Lindsey Gaetani—hold them up in the national spotlight—and say: we—the MSP—have failed you. Brian Tully failed these people. John Fanning failed these people. Nick Guarino failed these people. Yuri Bukhenik failed these people. We need to say that. We need to highlight it. We need to say: this happened even once. Therefore, we are not good enough. Not only are we not good enough—the very fact that either of these things were able to happen—the Birchmore cover-up, the phone extraction leak—is such a pervasive, systemic degradation of the faith that victims and the public have in the justice system—that our only option is to talk about this—congressional hearings. We need the State House to have congressional hearings. We need these people to answer for what they did. And we need to make sure it never happens again. And the only way you do that is by finding out what aspects of the bureaucratic structure allowed this to happen. And it's not going to be comfortable. I don't think it's going to be comfortable for anyone to talk about the fallout of any of this—but that's exactly what happened at the CCC on a smaller scale. And if this country matters—if this form of government matters—if this republic matters—then we will fix this. We will fix it together. We will address the hard questions. We will address the uncomfortable questions. We will shed our prejudices and polemical biases at the door. We will engage in no fear, no favoritism—and we will look only for the truth and nothing but it. And if you are incapable of doing that—you're contributing—either consciously or subconsciously—to the problem. It's our only option. And you can't just say: because they prosecuted Karen Read, we can't talk about anything bad that they did. That's tribalism. That's polemical. That's what drove us to this point.

Grant Smith Ellis

41,546 次观看 • 11 个月前

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,179 次观看 • 8 个月前

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

102,145 次观看 • 8 个月前

🚨NEW - House Speaker Mike Johnson delivers first floor speech on border catastrophe, accusing President Biden of intentionally creating a "clear and present danger to our national security." "Since I was elected Speaker less than 100 days ago, more than 700,000 illegals have been welcomed into our country illegally by the Biden administration. American schoolchildren have been forced into virtual schools so migrants can sleep in their school buildings. Korean War veterans have been booted from nursing homes that were sold to house migrants. Our streets are being flooded with fentanyl. Hundreds of thousands of children and adults are being poisoned and losing their lives. Vulnerable children and women are being exploited and trafficked by cartels. Since President Biden and Alejandro Mayorkas assumed office, there have been more than 7 million encounters with illegal aliens just at our southern border alone. 35 of our 50 states, including my home state of Louisiana, don't have a population that large. More than 300 individuals who are on our terror watchlist have been apprehended on the southern border. The frightening question is, how many have entered undetected? We know there are at least 1.8 million gotaways that have escaped. Understand that the situation at our border presents a clear and present danger to our national security, and it demands that it be addressed. FBI Director Christopher Wray told the Homeland Security Committee in November that these gotaways are a great concern for the agency, and all 56 of our Joint Terrorism Task Forces are trying to identify who these people are. We don't know how many terrorists are inside our borders. We do know that fentanyl is pouring into our communities like an open sewer. Right now, the leading cause of death of death in America for Americans aged 18 to 46 is fentanyl poisoning. To make matters worse, we've learned that the Biden administration is now simply just releasing 85% of the illegals who come across that border right into the country. For reference, by the way, in 2013, the Obama administration detained 82% of illegal aliens. How do we go from detaining 82% to releasing 85%? It only happens if this is by design. It only happens if it's an orchestrated intentional effort by the administration to do exactly that, and that is what the evidence shows. Earlier this month, I released a memo documenting 64 specific actions that the Biden administration has taken to undermine our border security and to promote the mass release of illegals and dangerous persons into our country. The very first day President Biden walked into the Oval Office, he revoked Executive Order 9844. Do you know what that did? It ended the construction of the border wall that Congress had already paid for. Everybody has seen the images of the materials out there rotting in the sun. Why? Because Joe Biden decided unilaterally that he didn't want a wall. In February 2021, the administration stopped applying Title 42 expulsions to children and incentivize by doing that incentivize families to send unaccompanied children through Mexico under the watch of cartels and traffickers. Since then, the administration admits to losing track of more than 80,000 unaccompanied children somewhere in the US. We don't know where they are. Have they been put into trafficking rings? We know that some of these kids are being trafficked for free labor and being forced to do things that are too appalling for us to articulate on this floor. Everybody here knows that's happening, and we're not demanding the President stop it? He can. He has the power to do it. In October 2021, the Biden administration revoked the migrant protection protocols that had been instituted under President Trump. That's the policy that we all know colloquially as Remain in Mexico. The Remain in Mexico policy kept asylum seekers in a safe haven third country while they were seeking asylum in the United States. Do you know why that works magically so well? Because it sent a message around the world that you shouldn't pay your life savings to a cartel to traffic you through Mexico and drop you over the US border because you're not going to be dropped over the US border. The word goes out on social media to countries all around the world. They are not going to let you in. Man, is that the most common sense rule you've ever thought of? President Biden doesn't agree because he stopped it. He issued an executive order to stop that common sense rule. A senior border patrol officer told us on that trip to Eagle Pass that if President Biden, by the stroke of a pen, would issue an executive order today to just simply reinstate Remain in Mexico, they think that would stop the flow by 70%. One stroke of the pen by the President. He does not seem to care. I told President Biden this myself on multiple occasions, most recently, a couple of weeks ago on the phone, I read him the law that says that he has all this authority, but he refuses to act. And even that's even despite court orders, by the way that instructed the administration to reinstate the institute remain in Mexico while the litigation was going on. You know what they did? They ignored it. The Biden administration is actively incentivizing illegals to come to the United States. We have laid out the welcome mat. We told everybody around the world, come on, you know what? The US taxpayer will take care of you. It's costing the American people billions and billions and billions of dollars to provide for people who are intentionally breaking our laws, billions and billions of dollars to house them and educate them and clothe them and take care of them. Why should we bear the burden when they break our laws? In January 2023, the Biden administration expanded the use of the Customs and Border Patrol One App so illegal aliens could just make appointments and then be released immediately right into America's interior. Guess what? 95% of all illegals who simply scheduled appointments through the app were released right into the United States. That's right. All you have to do is just download the app because they all have smartphones, make an appointment, and the President of the United States will release you into a network of NGOs who will put you on a plane to the destination of your choice. Without identification, by the way, while Americans are waiting in line to get through TSA. All on the dime of the American taxpayer. In December 2023, CBP recorded more than 302,000 encounters, almost double the population of my hometown of Shreveport, Louisiana. In just one month 302,000 people just walked right into the country. I could go on and on about the 64 actions we've documented that President Biden has specifically taken to open our border wide up, but it's crystal clear his policy choices and Secretary Mayorkas refusal to comply with the law are driving this border catastrophe. They have chosen disorder and chaos for us rather than securing the homeland. They've ceded the homeland to cartels and traffickers. In the Del Rio sector alone in Eagle Pass, Texas, the cartels are making an estimated $3.5 million dollars a day trafficking human beings into our country. Do the math. They are making billions of dollars a year trafficking undocumented children and victims into this country. The Department of Homeland Security has effectively become a taxi driver to just help traffickers complete the last few miles of their human smuggling operation, and they're making billions of dollars in the process. It's absolute madness, and it is dismantling the safety of our communities. More than half a million known criminals and illegal aliens are in the US in our communities, free to re-offend and victimize American people. When we were at Eagle Pass in the Del Rio sector earlier in January, they told us that 60 to 70% of the people coming across the border right there at that epicenter are single adult males. They're military-aged. These are not huddled masses of families seeking refuge and asylum. These are people coming into our country to do only God knows what. The Biden administration is allowing it, and we've noted that they are coming from adversarial nations and from terrorist regions. We have no idea what they're planning. But in fiscal year 2023, border patrol encountered illegals from 170 different countries, including hundreds from Iran and Syria, thousands from Russia, and tens of thousands have come in from China. Tell me that's not dangerous? Law enforcement and intelligence leaders are warning us that we may very well suffer a preventable terrorist attack here in the homeland if we don't immediately secure that border and remove these dangerous terrorists from inside our borders. I read the black letter law to the President of the United States on the phone about two and a half weeks ago. I said, Mr. President, it says very clearly that you have all the tools and the executive authority necessary to reverse the catastrophe that you have created. He has those tools right now, and he has since day one. We have to decide if we believe in the rule of law or not. We have to decide if we're a sovereign nation or we're not. I believe that border security is part of our solemn obligation to safeguard the well-being of our citizens and uphold the principles that define who we are as a nation. A weak border weakens America, and a strong border is good for America, and a stronger America is good for everybody around the world. And everybody in this chamber should acknowledge that. Just as we lock our doors at night to protect our homes, we secure our borders to protect our homeland. And my friends, that is our sacred obligation." Speaker Mike Johnson

🚨NEW - House Speaker Mike Johnson delivers first floor speech on border catastrophe, accusing President Biden of intentionally creating a "clear and present danger to our national security." "Since I was elected Speaker less than 100 days ago, more than 700,000 illegals have been welcomed into our country illegally by the Biden administration. American schoolchildren have been forced into virtual schools so migrants can sleep in their school buildings. Korean War veterans have been booted from nursing homes that were sold to house migrants. Our streets are being flooded with fentanyl. Hundreds of thousands of children and adults are being poisoned and losing their lives. Vulnerable children and women are being exploited and trafficked by cartels. Since President Biden and Alejandro Mayorkas assumed office, there have been more than 7 million encounters with illegal aliens just at our southern border alone. 35 of our 50 states, including my home state of Louisiana, don't have a population that large. More than 300 individuals who are on our terror watchlist have been apprehended on the southern border. The frightening question is, how many have entered undetected? We know there are at least 1.8 million gotaways that have escaped. Understand that the situation at our border presents a clear and present danger to our national security, and it demands that it be addressed. FBI Director Christopher Wray told the Homeland Security Committee in November that these gotaways are a great concern for the agency, and all 56 of our Joint Terrorism Task Forces are trying to identify who these people are. We don't know how many terrorists are inside our borders. We do know that fentanyl is pouring into our communities like an open sewer. Right now, the leading cause of death of death in America for Americans aged 18 to 46 is fentanyl poisoning. To make matters worse, we've learned that the Biden administration is now simply just releasing 85% of the illegals who come across that border right into the country. For reference, by the way, in 2013, the Obama administration detained 82% of illegal aliens. How do we go from detaining 82% to releasing 85%? It only happens if this is by design. It only happens if it's an orchestrated intentional effort by the administration to do exactly that, and that is what the evidence shows. Earlier this month, I released a memo documenting 64 specific actions that the Biden administration has taken to undermine our border security and to promote the mass release of illegals and dangerous persons into our country. The very first day President Biden walked into the Oval Office, he revoked Executive Order 9844. Do you know what that did? It ended the construction of the border wall that Congress had already paid for. Everybody has seen the images of the materials out there rotting in the sun. Why? Because Joe Biden decided unilaterally that he didn't want a wall. In February 2021, the administration stopped applying Title 42 expulsions to children and incentivize by doing that incentivize families to send unaccompanied children through Mexico under the watch of cartels and traffickers. Since then, the administration admits to losing track of more than 80,000 unaccompanied children somewhere in the US. We don't know where they are. Have they been put into trafficking rings? We know that some of these kids are being trafficked for free labor and being forced to do things that are too appalling for us to articulate on this floor. Everybody here knows that's happening, and we're not demanding the President stop it? He can. He has the power to do it. In October 2021, the Biden administration revoked the migrant protection protocols that had been instituted under President Trump. That's the policy that we all know colloquially as Remain in Mexico. The Remain in Mexico policy kept asylum seekers in a safe haven third country while they were seeking asylum in the United States. Do you know why that works magically so well? Because it sent a message around the world that you shouldn't pay your life savings to a cartel to traffic you through Mexico and drop you over the US border because you're not going to be dropped over the US border. The word goes out on social media to countries all around the world. They are not going to let you in. Man, is that the most common sense rule you've ever thought of? President Biden doesn't agree because he stopped it. He issued an executive order to stop that common sense rule. A senior border patrol officer told us on that trip to Eagle Pass that if President Biden, by the stroke of a pen, would issue an executive order today to just simply reinstate Remain in Mexico, they think that would stop the flow by 70%. One stroke of the pen by the President. He does not seem to care. I told President Biden this myself on multiple occasions, most recently, a couple of weeks ago on the phone, I read him the law that says that he has all this authority, but he refuses to act. And even that's even despite court orders, by the way that instructed the administration to reinstate the institute remain in Mexico while the litigation was going on. You know what they did? They ignored it. The Biden administration is actively incentivizing illegals to come to the United States. We have laid out the welcome mat. We told everybody around the world, come on, you know what? The US taxpayer will take care of you. It's costing the American people billions and billions and billions of dollars to provide for people who are intentionally breaking our laws, billions and billions of dollars to house them and educate them and clothe them and take care of them. Why should we bear the burden when they break our laws? In January 2023, the Biden administration expanded the use of the Customs and Border Patrol One App so illegal aliens could just make appointments and then be released immediately right into America's interior. Guess what? 95% of all illegals who simply scheduled appointments through the app were released right into the United States. That's right. All you have to do is just download the app because they all have smartphones, make an appointment, and the President of the United States will release you into a network of NGOs who will put you on a plane to the destination of your choice. Without identification, by the way, while Americans are waiting in line to get through TSA. All on the dime of the American taxpayer. In December 2023, CBP recorded more than 302,000 encounters, almost double the population of my hometown of Shreveport, Louisiana. In just one month 302,000 people just walked right into the country. I could go on and on about the 64 actions we've documented that President Biden has specifically taken to open our border wide up, but it's crystal clear his policy choices and Secretary Mayorkas refusal to comply with the law are driving this border catastrophe. They have chosen disorder and chaos for us rather than securing the homeland. They've ceded the homeland to cartels and traffickers. In the Del Rio sector alone in Eagle Pass, Texas, the cartels are making an estimated $3.5 million dollars a day trafficking human beings into our country. Do the math. They are making billions of dollars a year trafficking undocumented children and victims into this country. The Department of Homeland Security has effectively become a taxi driver to just help traffickers complete the last few miles of their human smuggling operation, and they're making billions of dollars in the process. It's absolute madness, and it is dismantling the safety of our communities. More than half a million known criminals and illegal aliens are in the US in our communities, free to re-offend and victimize American people. When we were at Eagle Pass in the Del Rio sector earlier in January, they told us that 60 to 70% of the people coming across the border right there at that epicenter are single adult males. They're military-aged. These are not huddled masses of families seeking refuge and asylum. These are people coming into our country to do only God knows what. The Biden administration is allowing it, and we've noted that they are coming from adversarial nations and from terrorist regions. We have no idea what they're planning. But in fiscal year 2023, border patrol encountered illegals from 170 different countries, including hundreds from Iran and Syria, thousands from Russia, and tens of thousands have come in from China. Tell me that's not dangerous? Law enforcement and intelligence leaders are warning us that we may very well suffer a preventable terrorist attack here in the homeland if we don't immediately secure that border and remove these dangerous terrorists from inside our borders. I read the black letter law to the President of the United States on the phone about two and a half weeks ago. I said, Mr. President, it says very clearly that you have all the tools and the executive authority necessary to reverse the catastrophe that you have created. He has those tools right now, and he has since day one. We have to decide if we believe in the rule of law or not. We have to decide if we're a sovereign nation or we're not. I believe that border security is part of our solemn obligation to safeguard the well-being of our citizens and uphold the principles that define who we are as a nation. A weak border weakens America, and a strong border is good for America, and a stronger America is good for everybody around the world. And everybody in this chamber should acknowledge that. Just as we lock our doors at night to protect our homes, we secure our borders to protect our homeland. And my friends, that is our sacred obligation." Speaker Mike Johnson

KanekoaTheGreat

14,479,557 次观看 • 2 年前

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 次观看 • 6 个月前