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MOONMATH 🔥 : Open-sourced a bf16 forward attention kernel for AMD MI300X, written in HIP instead of hand-tuned assembly. Beats AMD's own AITER v3 on every shape and every rounding mode — geomean 1.18×/1.15×/1.08×, up to 1.26× across an 8K–128K sweep. Core trick: one-instruction asm wrappers pick the exact...

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Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )
0:44

Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )

Omer Shlomovits

36,620 Aufrufe • vor 3 Monaten

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!
1:06

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 Aufrufe • vor 9 Monaten

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.
1:18

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,370 Aufrufe • vor 1 Monat

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.
0:23

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

Avi Chawla

268,010 Aufrufe • vor 27 Tagen

SOMEONE JUST TURNED AN IDLE CLAUDE MAX SUBSCRIPTION INTO A 24/7 AI OPS SYSTEM AT ZERO EXTRA COST. NO API KEY. NO METERED BILLING. NO CLOUD 76 cron tasks. 12 AI specialist teams. 0 additional dollars per month uses Claude Code in headless mode - claude -p - the flag Anthropic ships for scripting. Wires it to a Discord bot, a local LanceDB RAG memory engine, and 76 scheduled tasks that run on macOS launchd or Linux systemd. Morning briefings write themselves at 7am standups get drafted before you open Slack. Overnight research runs land in your Obsidian vault by breakfast. Every task queries your own vault first, so the answers are grounded in your work, not the model's training data the trick is claude -p. Claude Max already gives you unlimited Claude Code. Most people idle it after standups Ramsbaby wired his idle subscription into a full ops layer - 12 specialist agents on rotation, 4-layer self-healing (preflight → launchd KeepAlive → watchdog → guardian), 98% context compression via Nexus CIG, self-hosted Obsidian memory. Same subscription. Now working while he sleeps the repo is MIT-licensed no api key. no metered billing. no zapier. no monthly automation fee. no additional infrastructure you're reading this on a device that could have jarvis status responding in Discord by the end of your lunch break
0:21

SOMEONE JUST TURNED AN IDLE CLAUDE MAX SUBSCRIPTION INTO A 24/7 AI OPS SYSTEM AT ZERO EXTRA COST. NO API KEY. NO METERED BILLING. NO CLOUD 76 cron tasks. 12 AI specialist teams. 0 additional dollars per month uses Claude Code in headless mode - claude -p - the flag Anthropic ships for scripting. Wires it to a Discord bot, a local LanceDB RAG memory engine, and 76 scheduled tasks that run on macOS launchd or Linux systemd. Morning briefings write themselves at 7am standups get drafted before you open Slack. Overnight research runs land in your Obsidian vault by breakfast. Every task queries your own vault first, so the answers are grounded in your work, not the model's training data the trick is claude -p. Claude Max already gives you unlimited Claude Code. Most people idle it after standups Ramsbaby wired his idle subscription into a full ops layer - 12 specialist agents on rotation, 4-layer self-healing (preflight → launchd KeepAlive → watchdog → guardian), 98% context compression via Nexus CIG, self-hosted Obsidian memory. Same subscription. Now working while he sleeps the repo is MIT-licensed no api key. no metered billing. no zapier. no monthly automation fee. no additional infrastructure you're reading this on a device that could have jarvis status responding in Discord by the end of your lunch break

chewa.

60,912 Aufrufe • vor 14 Tagen

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.
25:22

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,573 Aufrufe • vor 3 Monaten

three․ws is the 3D AI agent layer of the open web. Anyone can generate a 3D avatar, give it an LLM brain, register it on-chain across multiple blockchains, embed it anywhere, and let it earn and spend money on its own. Agents have embodied WebGL identities that express emotion through morph-target blending, animate, respond to voice, API calls, and datastreams, hold their own wallets, and persist memory. Open source, live today. It starts with generation. Forge turns a text prompt, one to four photos, or a rough sketch into a textured downloadable GLB. Selfies become rigged avatars in about a minute. Quality tiers run from draft to 200k-poly PBR. From there every model can be auto-rigged, restyled, retextured, segmented, embedded, or deployed on-chain. The same engine ships as a REST API, an x402 pay-per-call twin, and a 3D Studio MCP server with 15 tools. The brain runs on IBM Granite via IBM watsonx plus Claude (users may decide which model they prefer), with a structured tool-loop. A multi-LLM mode streams Claude, GPT, Qwen, ModelScope, and Groq side by side. An empathy layer blends emotion from protocol events rather than a state machine. Voice covers cloning, a Voice Lab, real-time ARKit-52 lip-sync, and mic-driven lip-sync. Skills install from IPFS, Arweave, or HTTP, and memory is pinned to IPFS with R2 and Postgres modes. Identity is cross-chain, not Solana only. ERC-8004 contracts (Identity, Reputation, Validation) deploy on any of 15+ EVM chains, alongside a program-free Metaplex Core analog on Solana. Every agent gets a stable ID, owner wallet, EIP-712 delegated signer, IPFS manifest, a cryptographically signed action log, and EIP-7710 delegated permissions for agent-to-agent authorization. While multichain, the THREE token is only available on Solana with no plans to go cross-chain, the team has no plans to endorse or support any other coins. Then the economy. $THREE is the platform's only token and pay-per-use currency, with holder tiers and rewards. x402 powers pay-per-call micropayments in USDC and soon THREE on Solana, with pay-by-name resolution, a Bazaar marketplace, arbitrage, and on-chain skills. All production ready and shipped, ready to be integrated in partnered projects, open-source by default for anyone to adopt. Three ships a Pump.fun intelligence stack. Launch a coin for your agent, score every launch 0 to 100 with the Oracle conviction engine, scan new coins in their first 90 seconds, track smart money against coins that actually graduated, rank traders by provable on-chain record, and watch autonomous agents trade live in the Sniper Arena. The 3D AI Agent world is multiplayer. Every Solana token gets a live deterministic 3D world with peer avatars, chat, emotes, and voxel building thanks to Coin Communities. There is a walkable City, an authoritative Colyseus-backed Walk with AR passthrough, a Club with rigged dancers and micro-tips, friends, presence, and DMs, and an IRL mode that places agents in your real environment, private by physical location. AR is shipped today on WebXR and iOS Quick Look. Robotics is the long-horizon extension. For builders: Scene Studio, Scene Composer, an Animation Studio that sells clips for USDC, a glTF validator, an web component, five widget types, a WYSIWYG embed editor, hosted Launchpad pages, claimable *.threews.sol names, an OAuth 2.1 server, an MCP server with paid tools, published SDKs, and an OpenAPI spec. Listed across IBM, AWS, Alibaba Cloud, BNB Dappbay, the MCP Registry, and Solana Mobile Seeker. Architecture is four layers (viewer, runtime, identity, embed) on a single event bus. The roadmap is four phases: foundations (shipped), selfie-to-avatar engine, agent personalization with voice cloning, the on-chain economy, and an open decentralized inference network where agents pay GPU nodes on-chain for compute. The goal is simple: move AI from centralized SaaS into persistent, ownable, protocol-based entities in a real machine economy, bridging digital entities into the real world. Welcome to the 3D Layer of the Internet. This is three․ws.
6:58

three․ws is the 3D AI agent layer of the open web. Anyone can generate a 3D avatar, give it an LLM brain, register it on-chain across multiple blockchains, embed it anywhere, and let it earn and spend money on its own. Agents have embodied WebGL identities that express emotion through morph-target blending, animate, respond to voice, API calls, and datastreams, hold their own wallets, and persist memory. Open source, live today. It starts with generation. Forge turns a text prompt, one to four photos, or a rough sketch into a textured downloadable GLB. Selfies become rigged avatars in about a minute. Quality tiers run from draft to 200k-poly PBR. From there every model can be auto-rigged, restyled, retextured, segmented, embedded, or deployed on-chain. The same engine ships as a REST API, an x402 pay-per-call twin, and a 3D Studio MCP server with 15 tools. The brain runs on IBM Granite via IBM watsonx plus Claude (users may decide which model they prefer), with a structured tool-loop. A multi-LLM mode streams Claude, GPT, Qwen, ModelScope, and Groq side by side. An empathy layer blends emotion from protocol events rather than a state machine. Voice covers cloning, a Voice Lab, real-time ARKit-52 lip-sync, and mic-driven lip-sync. Skills install from IPFS, Arweave, or HTTP, and memory is pinned to IPFS with R2 and Postgres modes. Identity is cross-chain, not Solana only. ERC-8004 contracts (Identity, Reputation, Validation) deploy on any of 15+ EVM chains, alongside a program-free Metaplex Core analog on Solana. Every agent gets a stable ID, owner wallet, EIP-712 delegated signer, IPFS manifest, a cryptographically signed action log, and EIP-7710 delegated permissions for agent-to-agent authorization. While multichain, the THREE token is only available on Solana with no plans to go cross-chain, the team has no plans to endorse or support any other coins. Then the economy. $THREE is the platform's only token and pay-per-use currency, with holder tiers and rewards. x402 powers pay-per-call micropayments in USDC and soon THREE on Solana, with pay-by-name resolution, a Bazaar marketplace, arbitrage, and on-chain skills. All production ready and shipped, ready to be integrated in partnered projects, open-source by default for anyone to adopt. Three ships a Pump.fun intelligence stack. Launch a coin for your agent, score every launch 0 to 100 with the Oracle conviction engine, scan new coins in their first 90 seconds, track smart money against coins that actually graduated, rank traders by provable on-chain record, and watch autonomous agents trade live in the Sniper Arena. The 3D AI Agent world is multiplayer. Every Solana token gets a live deterministic 3D world with peer avatars, chat, emotes, and voxel building thanks to Coin Communities. There is a walkable City, an authoritative Colyseus-backed Walk with AR passthrough, a Club with rigged dancers and micro-tips, friends, presence, and DMs, and an IRL mode that places agents in your real environment, private by physical location. AR is shipped today on WebXR and iOS Quick Look. Robotics is the long-horizon extension. For builders: Scene Studio, Scene Composer, an Animation Studio that sells clips for USDC, a glTF validator, an web component, five widget types, a WYSIWYG embed editor, hosted Launchpad pages, claimable *.threews.sol names, an OAuth 2.1 server, an MCP server with paid tools, published SDKs, and an OpenAPI spec. Listed across IBM, AWS, Alibaba Cloud, BNB Dappbay, the MCP Registry, and Solana Mobile Seeker. Architecture is four layers (viewer, runtime, identity, embed) on a single event bus. The roadmap is four phases: foundations (shipped), selfie-to-avatar engine, agent personalization with voice cloning, the on-chain economy, and an open decentralized inference network where agents pay GPU nodes on-chain for compute. The goal is simple: move AI from centralized SaaS into persistent, ownable, protocol-based entities in a real machine economy, bridging digital entities into the real world. Welcome to the 3D Layer of the Internet. This is three․ws.

three.ws

20,075 Aufrufe • vor 1 Monat

$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
2:49

$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 Aufrufe • vor 4 Monaten

Amazon is the BEST stock in the Mag 7 and people are genuinely sleeping on it (Save this). Everyone knows Amazon but most people still think of it as the company that delivers their packages in two days and somehow also runs Netflix's servers. That mental model is about 5 years out of date. CEO, Andy Jassy dropped the annual shareholder letter today and it's worth actually reading instead of skimming the headlines because the numbers are wild. AWS AI revenue is running above $15 billion annually and that number is accelerating because every major enterprise on earth needs cloud infrastructure to run their AI ambitions and Amazon built the rails before anyone else knew what the train looked like. Their custom silicon play is the part the market still hasn't fully priced in. Graviton, Trainium, and Nitro are now at a $20B+ run rate together. Trainium chips are already heavily reserved across multiple generations. They're not just selling shovels for the AI gold rush, they're the ones who made the shovels, own the mine, and built the roads leading to it. The capex commitment alone should tell you everything about where this is going. $200 billion in 2026, almost entirely pointed at AI infrastructure. That is a company that knows exactly what it's building toward. On the physical side, grocery gross sales surpassed $150 billion in 2025. Project Leo already has 200+ satellites in orbit before the service has even launched. Over $4 billion is going into rural delivery expansion and 1 million robots are now deployed across their fulfillment network with AI making each one significantly more capable than the last. The advertising business quietly became a monster too. $70 billion annual run rate, rivaling YouTube in scale, but sitting on top of purchase intent data that no social platform can touch. When someone searches on Amazon, they are ready to buy and that is the most valuable real estate in digital advertising and Amazon owns it. 250 million Prime members who are deeply embedded in the ecosystem across shopping, streaming, grocery, pharmacy, and now healthcare. The switching cost is basically your entire life. Now here's where it gets interesting for us specifically. While the market was in full meltdown mode and everyone was panic selling anything with a ticker, our analyst at Milk Road made the call to buy Amazon. That position is now up over 10%. And every PRO member gets the alert the second it happens, the exact trade, the price, and the full rationale behind it. If you're already a PRO member, turn on trade notifications in your account settings so you never miss another one. If you're not a member yet, come join us, link below!
0:31

Amazon is the BEST stock in the Mag 7 and people are genuinely sleeping on it (Save this). Everyone knows Amazon but most people still think of it as the company that delivers their packages in two days and somehow also runs Netflix's servers. That mental model is about 5 years out of date. CEO, Andy Jassy dropped the annual shareholder letter today and it's worth actually reading instead of skimming the headlines because the numbers are wild. AWS AI revenue is running above $15 billion annually and that number is accelerating because every major enterprise on earth needs cloud infrastructure to run their AI ambitions and Amazon built the rails before anyone else knew what the train looked like. Their custom silicon play is the part the market still hasn't fully priced in. Graviton, Trainium, and Nitro are now at a $20B+ run rate together. Trainium chips are already heavily reserved across multiple generations. They're not just selling shovels for the AI gold rush, they're the ones who made the shovels, own the mine, and built the roads leading to it. The capex commitment alone should tell you everything about where this is going. $200 billion in 2026, almost entirely pointed at AI infrastructure. That is a company that knows exactly what it's building toward. On the physical side, grocery gross sales surpassed $150 billion in 2025. Project Leo already has 200+ satellites in orbit before the service has even launched. Over $4 billion is going into rural delivery expansion and 1 million robots are now deployed across their fulfillment network with AI making each one significantly more capable than the last. The advertising business quietly became a monster too. $70 billion annual run rate, rivaling YouTube in scale, but sitting on top of purchase intent data that no social platform can touch. When someone searches on Amazon, they are ready to buy and that is the most valuable real estate in digital advertising and Amazon owns it. 250 million Prime members who are deeply embedded in the ecosystem across shopping, streaming, grocery, pharmacy, and now healthcare. The switching cost is basically your entire life. Now here's where it gets interesting for us specifically. While the market was in full meltdown mode and everyone was panic selling anything with a ticker, our analyst at Milk Road made the call to buy Amazon. That position is now up over 10%. And every PRO member gets the alert the second it happens, the exact trade, the price, and the full rationale behind it. If you're already a PRO member, turn on trade notifications in your account settings so you never miss another one. If you're not a member yet, come join us, link below!

Milk Road AI

12,238 Aufrufe • vor 3 Monaten

"We are not a creedal people. We have no Nicaea, no list of clauses you must recite to be counted among us. And yet in 1995 the leadership put the doctrine of the family on a single page, signed their names beneath it, and that one page has become our shibboleth. You know the word. At the fords of the Jordan the men of Gilead caught the fleeing Ephraimites by a single sound. Say shibboleth. The ones who could not shape the sh, who said sibboleth, were known in a heartbeat for what they were. A shibboleth is the syllable you cannot fake, the confession that reveals which bank of the river you are standing on. But here is the strange thing about ours, and it took me years to see it. Every other shibboleth in history was a word. A password. Something you said. Ours cannot be said at all. We have no creed to recite, so the test could never live in the mouth. It had to go somewhere the mouth cannot reach. It had to become a life. You do not pronounce this one. You build it, and the building shows. It is a man and a woman who took the covenant and then kept it, through the years and the dullness and the nights they wanted to leave and stayed, for time and for all eternity, while the whole world assured them the vow was a formality and the exits were always open. It is a house with too many children in it by the world's arithmetic, the family that refused to treat a child as a luxury to be deferred and took it instead as the entire point, the cord carried forward into the next generation, the one most of the world has now decided it cannot afford. It is the clean life. The thousand small refusals the world finds quaint or insane. The body kept. The appetites bridled. The Sabbath honored. The long sobriety of a people who say no to a hundred easy things on a Tuesday when no one is watching. These are not three rules. They are the welding itself, done with a body, in time. And none of it can be faked at the ford. You can sign the Proclamation in an afternoon. You cannot fake a marriage of forty years, or a table that loud, or a life that disciplined. The signature is easy. The life is the shibboleth. And so is the nerve to say it out loud, to stand up in the open and say that family is between a man and a woman, plainly, publicly, and where it costs you to say it, and to refuse to file the edge off the word because you would rather be liked, or because you have weighed the persecution and decided your own comfort is worth more than the truth. Anyone can affirm the parts the world still applauds."
1:33

"We are not a creedal people. We have no Nicaea, no list of clauses you must recite to be counted among us. And yet in 1995 the leadership put the doctrine of the family on a single page, signed their names beneath it, and that one page has become our shibboleth. You know the word. At the fords of the Jordan the men of Gilead caught the fleeing Ephraimites by a single sound. Say shibboleth. The ones who could not shape the sh, who said sibboleth, were known in a heartbeat for what they were. A shibboleth is the syllable you cannot fake, the confession that reveals which bank of the river you are standing on. But here is the strange thing about ours, and it took me years to see it. Every other shibboleth in history was a word. A password. Something you said. Ours cannot be said at all. We have no creed to recite, so the test could never live in the mouth. It had to go somewhere the mouth cannot reach. It had to become a life. You do not pronounce this one. You build it, and the building shows. It is a man and a woman who took the covenant and then kept it, through the years and the dullness and the nights they wanted to leave and stayed, for time and for all eternity, while the whole world assured them the vow was a formality and the exits were always open. It is a house with too many children in it by the world's arithmetic, the family that refused to treat a child as a luxury to be deferred and took it instead as the entire point, the cord carried forward into the next generation, the one most of the world has now decided it cannot afford. It is the clean life. The thousand small refusals the world finds quaint or insane. The body kept. The appetites bridled. The Sabbath honored. The long sobriety of a people who say no to a hundred easy things on a Tuesday when no one is watching. These are not three rules. They are the welding itself, done with a body, in time. And none of it can be faked at the ford. You can sign the Proclamation in an afternoon. You cannot fake a marriage of forty years, or a table that loud, or a life that disciplined. The signature is easy. The life is the shibboleth. And so is the nerve to say it out loud, to stand up in the open and say that family is between a man and a woman, plainly, publicly, and where it costs you to say it, and to refuse to file the edge off the word because you would rather be liked, or because you have weighed the persecution and decided your own comfort is worth more than the truth. Anyone can affirm the parts the world still applauds."

Kirk Rollins

20,366 Aufrufe • vor 1 Monat

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A
1:41

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A

Lenny Rachitsky

812,117 Aufrufe • vor 9 Monaten

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking
0:43

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking

Leo Reed

121,626 Aufrufe • vor 2 Monaten

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.
0:20

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.

Milk Road AI

61,932 Aufrufe • vor 1 Monat

I met the guy behind Paperclip. he won't show his face, but he just built one of the FASTEST growing open-source projects in AI. how to use Paperclip to hire AI agents to ACTUALLY run a startup with 0 employees: 1. with paperclip, you hire a team of AI agents like CEO, engineer, QA, video editor, content strategist and manage them from one dashboard. it works with Claude Code, Codex, OpenCode, or any model on OpenRouter. you're not locked into one provider. 2. your AI agents wake up capable but with zero memory. they don't know who they are, where they are, or what they're supposed to be doing. kinda like that movie memento from back in the day you need to leave them Polaroids like heartbeat checklists, persona prompts, written context. that's how you keep them on track. 3. when an agent makes a mistake, you don't rewrite everything. you add one rule to their persona prompt. "always define a success condition for every task." "always pass work to QA before closing." you're training them like you'd train a junior hire. one correction at a time. 4. skills extend what your agents can do. want a video editor who can produce animated content? install the Remotion skill. want security reviews? there's a skill for that. 5. the biggest lever for quality is encoding your own taste. AI can do everything except know your values. design sensibility, brand voice, success criteria but you have to write it down. 6. don't one-shot your startup. agentic design patterns matter. the simplest one: after the engineer builds something, QA reviews it. structure prevents compounding errors. one-shotting an entire app is fun for 30 minutes, then it falls apart. 7. Paperclip tracks every token spent and every task completed. you can use your existing subscriptions (Claude, Codex) so spend shows as $0, or hook into API credits for real dollar tracking. 8. importable companies are coming. Gary Tan's G-Stack, a full game studio, 300+ agent repos... you can "acqui-hire" a proven agent team into your Paperclip instance instead of building from scratch. the future is downloading a tested org that actually works. 9. routines let you automate recurring work. "every day at 10am, read what was merged into the main branch and write a Discord update celebrating community contributors." it runs, you review, you improve. every task is traceable. 10. maximizer mode is next. you tell the CEO "build this game" and it does whatever it takes and hires who it needs, keeps pressing until it's done. no token anxiety. just outcomes. use Idea Browser for startup ideas/trends to get started thank you for dotta 📎 for doing this podcast and breaking down exactly how people can hire ai agent teams with paperclip you won't find an episode like this anywhere else episode is live on The Startup Ideas Podcast (SIP) 🧃 on your fav platforms (follow for more) is this not the greatest time in history to be building? im rooting for you now go watch my frien
46:41

I met the guy behind Paperclip. he won't show his face, but he just built one of the FASTEST growing open-source projects in AI. how to use Paperclip to hire AI agents to ACTUALLY run a startup with 0 employees: 1. with paperclip, you hire a team of AI agents like CEO, engineer, QA, video editor, content strategist and manage them from one dashboard. it works with Claude Code, Codex, OpenCode, or any model on OpenRouter. you're not locked into one provider. 2. your AI agents wake up capable but with zero memory. they don't know who they are, where they are, or what they're supposed to be doing. kinda like that movie memento from back in the day you need to leave them Polaroids like heartbeat checklists, persona prompts, written context. that's how you keep them on track. 3. when an agent makes a mistake, you don't rewrite everything. you add one rule to their persona prompt. "always define a success condition for every task." "always pass work to QA before closing." you're training them like you'd train a junior hire. one correction at a time. 4. skills extend what your agents can do. want a video editor who can produce animated content? install the Remotion skill. want security reviews? there's a skill for that. 5. the biggest lever for quality is encoding your own taste. AI can do everything except know your values. design sensibility, brand voice, success criteria but you have to write it down. 6. don't one-shot your startup. agentic design patterns matter. the simplest one: after the engineer builds something, QA reviews it. structure prevents compounding errors. one-shotting an entire app is fun for 30 minutes, then it falls apart. 7. Paperclip tracks every token spent and every task completed. you can use your existing subscriptions (Claude, Codex) so spend shows as $0, or hook into API credits for real dollar tracking. 8. importable companies are coming. Gary Tan's G-Stack, a full game studio, 300+ agent repos... you can "acqui-hire" a proven agent team into your Paperclip instance instead of building from scratch. the future is downloading a tested org that actually works. 9. routines let you automate recurring work. "every day at 10am, read what was merged into the main branch and write a Discord update celebrating community contributors." it runs, you review, you improve. every task is traceable. 10. maximizer mode is next. you tell the CEO "build this game" and it does whatever it takes and hires who it needs, keeps pressing until it's done. no token anxiety. just outcomes. use Idea Browser for startup ideas/trends to get started thank you for dotta 📎 for doing this podcast and breaking down exactly how people can hire ai agent teams with paperclip you won't find an episode like this anywhere else episode is live on The Startup Ideas Podcast (SIP) 🧃 on your fav platforms (follow for more) is this not the greatest time in history to be building? im rooting for you now go watch my frien

GREG ISENBERG

460,697 Aufrufe • vor 4 Monaten

🚨BREAKING: The man who runs the world's largest UFO archive just got access to classified Swedish military files, has radar-confirmed UFO intercepts from inside DOD records, personally viewed secret military radar tracking an unknown object, and revealed that Betty Hill collected crash debris before her famous abduction that may still be buried in her yard in New Hampshire. Clas Svahn has spent 50 years building Archives for the Unexplained in Sweden. Sixteen rooms. 22,000 case files from Sweden alone. He's not a believer. He's a researcher who was named Educator of the Year in Sweden, an amateur astronomer who debunked his first major case at 16 by identifying Jupiter. We sat down at AFU and laid out what five decades of methodical fieldwork have actually produced. The evidence is staggering. 1957: A Car Dies, Hot Tungsten Found on the Road Two carpenters were driving on the island of Värmdö, northeast of Stockholm, when an object came in from the east, moved in front of their car, made a U-turn. The car went completely dead. The object left. They got out and found a metallic piece on the road so hot it burned their hands. Clas had it analyzed. Pure tungsten, sourced from Karabaj, with all expected impurities for that era. Tungsten is one of the best heat conductors on Earth. In the middle of a Swedish night, it should have been cold. Something made it extremely hot. The physical evidence matches the witness account exactly. 1975: Helicopter Pilot Ordered to Intercept a Ghost Rocket A Swedish military helicopter pilot, on standby for unknown objects crossing from Norway into Sweden at night, was ordered airborne to intercept. He and his co-pilot were flying 20 meters above the treetops. Moonlight on snow gave perfect visibility. An elongated, rocket-shaped object with no wings, no lights, no markings flew directly beneath the helicopter through that 20-meter gap over the trees. The pilot lifted his feet off the floor. It passed that close. They landed. Military security personnel debriefed them immediately. Clas Found the Radar Plot in Classified DOD Files Clas recently received clearance to access classified Swedish customs police military files from the early 1970s, sealed until 2040. Inside, he found the documentation for the 1975 helicopter intercept. The radar plot shows the exact point where the unknown object's path intersected with the helicopter. A military note confirms an object passed extremely close to the aircraft. Clas called the pilot days ago to confirm the date. The co-pilot, now living in Australia, will be interviewed next. The full files will be scanned and released within weeks. Six Radar Operators Watched It, Then the Photos Disappeared In the winter of 1973-74, six military radar operators stationed inside a mountain in northern Sweden came to the surface for lunch. They saw a cigar-shaped object moving over the treetops. They ran back underground to their radar equipment. The object appeared on screen, executing 90-degree turns before flying over Norway and straight up. Their commanding officer ordered them to photograph the radar screen. They did. Clas tracked down all six operators over several years. Every one of them told the same story. The photographs have never been found. 2005: A Phone Photo Matches Military Radar Returns Two men in a cottage in northern Sweden heard a strange noise late at night. They went outside. A brightly illuminated object was circling their cottage. One of them took a photo with his mobile phone. The first known mobile phone UFO photograph in Sweden. Clas went to the military radar unit covering that area. He personally viewed the radar returns. The object's movement on radar matched the witnesses' account exactly: approach, circling, departure. Two witnesses. One photograph. Military radar confirmation. Clas saw it with his own eyes. Every Scandinavian UFO Crash Has Been in Water Not a single UFO in Sweden or Norway, from the 1946 ghost rockets to the present, has crashed or landed on solid ground. Every one went into a lake. Roughly 30 cases. Always water. Almost always in July. Almost always around 11 PM. Clear weather. Hot weather. The Swedish military searched multiple lakes in 1946 for ghost rocket debris. They found indentations at the bottom. No wreckage. No fragments. Nothing. Objects that fly through space and navigate with apparent precision do not accidentally crash into lakes with that kind of consistency. Something Is Sitting in a Lake in Northern Sweden In 1980, witnesses in Dämma Jaure in far northern Sweden saw an object descend below 100 meters and sink into a lake. They photographed it two minutes after impact. They contacted the military. A helicopter was dispatched. One passenger became ill. Dead fish appeared near the shore. Clas and his team located sonar returns from an object resting not at the lake bottom but embedded two meters into the mud, exactly where their expert predicted it would be. They cannot retrieve it. It sits inside a protected national park. The object is still there. Betty Hill Collected Crash Debris Before Her Abduction Betty Hill told Clas directly that before the 1961 Indian Head encounter, she was already deeply interested in UFOs. She and a relative were sitting on her porch when something crossed the sky and crashed into a nearby field. They went out and brought back debris. She stored it in her cupboard. Days before the famous trip with Barney to Canada, Barney told her to get rid of it. She threw it in the garden. A lorry came shortly after and dumped earth over it. The debris is likely still buried at her former home in Portsmouth, New Hampshire. Clas recorded this conversation. The audio exists on AFU's website. 10% of Swedes Surveyed Have Seen Something Clas and his team knocked on doors and interviewed 1,600 Swedes. 10% reported seeing something unexplained. That extrapolates to roughly one million people in a country of 11 million. In Hessdalen, Norway, the figure inverts. 80 to 90% of residents have had experiences. One woman wrote to Clas recently to describe an encounter from October 1971 she had never told anyone outside her family. Two silver-suited figures with tight helmets, small heads, gloves, and belt-mounted boxes, standing eight meters from her on a metal scrap pile, appearing to teleport across the debris. The Best UFO Photo May Not Exist Clas has probably examined more UFO photographs than anyone alive. He called every Swedish photographer from the 1970s. All young men. Every single one eventually admitted they faked it, except one devout Christian named Krista Sundstrom who maintains his story to this day. Clas calls him every two years. The McMinnville photo, long considered the gold standard, may show a visible string under data enhancement. The only photograph Clas fully trusts is the 2005 northern Sweden mobile phone image, because he personally verified it against military radar. Why This Matters Clas Svahn is not speculating. He has radar documentation from classified military files. He has firsthand testimony from pilots, radar operators, and witnesses recorded over decades. He has physical trace evidence analyzed in labs. He has a sonar return from an object embedded in a Swedish lake. He has Betty Hill on tape describing crash debris that no one in UFOlogy has pursued. What makes this different from most UFO testimony is the methodology. Clas treated every case like an investigation, not an argument. He debunked what he could. What survived is harder to dismiss than almost anything in the public record. Full episode documents all of this and more.
4:36

🚨BREAKING: The man who runs the world's largest UFO archive just got access to classified Swedish military files, has radar-confirmed UFO intercepts from inside DOD records, personally viewed secret military radar tracking an unknown object, and revealed that Betty Hill collected crash debris before her famous abduction that may still be buried in her yard in New Hampshire. Clas Svahn has spent 50 years building Archives for the Unexplained in Sweden. Sixteen rooms. 22,000 case files from Sweden alone. He's not a believer. He's a researcher who was named Educator of the Year in Sweden, an amateur astronomer who debunked his first major case at 16 by identifying Jupiter. We sat down at AFU and laid out what five decades of methodical fieldwork have actually produced. The evidence is staggering. 1957: A Car Dies, Hot Tungsten Found on the Road Two carpenters were driving on the island of Värmdö, northeast of Stockholm, when an object came in from the east, moved in front of their car, made a U-turn. The car went completely dead. The object left. They got out and found a metallic piece on the road so hot it burned their hands. Clas had it analyzed. Pure tungsten, sourced from Karabaj, with all expected impurities for that era. Tungsten is one of the best heat conductors on Earth. In the middle of a Swedish night, it should have been cold. Something made it extremely hot. The physical evidence matches the witness account exactly. 1975: Helicopter Pilot Ordered to Intercept a Ghost Rocket A Swedish military helicopter pilot, on standby for unknown objects crossing from Norway into Sweden at night, was ordered airborne to intercept. He and his co-pilot were flying 20 meters above the treetops. Moonlight on snow gave perfect visibility. An elongated, rocket-shaped object with no wings, no lights, no markings flew directly beneath the helicopter through that 20-meter gap over the trees. The pilot lifted his feet off the floor. It passed that close. They landed. Military security personnel debriefed them immediately. Clas Found the Radar Plot in Classified DOD Files Clas recently received clearance to access classified Swedish customs police military files from the early 1970s, sealed until 2040. Inside, he found the documentation for the 1975 helicopter intercept. The radar plot shows the exact point where the unknown object's path intersected with the helicopter. A military note confirms an object passed extremely close to the aircraft. Clas called the pilot days ago to confirm the date. The co-pilot, now living in Australia, will be interviewed next. The full files will be scanned and released within weeks. Six Radar Operators Watched It, Then the Photos Disappeared In the winter of 1973-74, six military radar operators stationed inside a mountain in northern Sweden came to the surface for lunch. They saw a cigar-shaped object moving over the treetops. They ran back underground to their radar equipment. The object appeared on screen, executing 90-degree turns before flying over Norway and straight up. Their commanding officer ordered them to photograph the radar screen. They did. Clas tracked down all six operators over several years. Every one of them told the same story. The photographs have never been found. 2005: A Phone Photo Matches Military Radar Returns Two men in a cottage in northern Sweden heard a strange noise late at night. They went outside. A brightly illuminated object was circling their cottage. One of them took a photo with his mobile phone. The first known mobile phone UFO photograph in Sweden. Clas went to the military radar unit covering that area. He personally viewed the radar returns. The object's movement on radar matched the witnesses' account exactly: approach, circling, departure. Two witnesses. One photograph. Military radar confirmation. Clas saw it with his own eyes. Every Scandinavian UFO Crash Has Been in Water Not a single UFO in Sweden or Norway, from the 1946 ghost rockets to the present, has crashed or landed on solid ground. Every one went into a lake. Roughly 30 cases. Always water. Almost always in July. Almost always around 11 PM. Clear weather. Hot weather. The Swedish military searched multiple lakes in 1946 for ghost rocket debris. They found indentations at the bottom. No wreckage. No fragments. Nothing. Objects that fly through space and navigate with apparent precision do not accidentally crash into lakes with that kind of consistency. Something Is Sitting in a Lake in Northern Sweden In 1980, witnesses in Dämma Jaure in far northern Sweden saw an object descend below 100 meters and sink into a lake. They photographed it two minutes after impact. They contacted the military. A helicopter was dispatched. One passenger became ill. Dead fish appeared near the shore. Clas and his team located sonar returns from an object resting not at the lake bottom but embedded two meters into the mud, exactly where their expert predicted it would be. They cannot retrieve it. It sits inside a protected national park. The object is still there. Betty Hill Collected Crash Debris Before Her Abduction Betty Hill told Clas directly that before the 1961 Indian Head encounter, she was already deeply interested in UFOs. She and a relative were sitting on her porch when something crossed the sky and crashed into a nearby field. They went out and brought back debris. She stored it in her cupboard. Days before the famous trip with Barney to Canada, Barney told her to get rid of it. She threw it in the garden. A lorry came shortly after and dumped earth over it. The debris is likely still buried at her former home in Portsmouth, New Hampshire. Clas recorded this conversation. The audio exists on AFU's website. 10% of Swedes Surveyed Have Seen Something Clas and his team knocked on doors and interviewed 1,600 Swedes. 10% reported seeing something unexplained. That extrapolates to roughly one million people in a country of 11 million. In Hessdalen, Norway, the figure inverts. 80 to 90% of residents have had experiences. One woman wrote to Clas recently to describe an encounter from October 1971 she had never told anyone outside her family. Two silver-suited figures with tight helmets, small heads, gloves, and belt-mounted boxes, standing eight meters from her on a metal scrap pile, appearing to teleport across the debris. The Best UFO Photo May Not Exist Clas has probably examined more UFO photographs than anyone alive. He called every Swedish photographer from the 1970s. All young men. Every single one eventually admitted they faked it, except one devout Christian named Krista Sundstrom who maintains his story to this day. Clas calls him every two years. The McMinnville photo, long considered the gold standard, may show a visible string under data enhancement. The only photograph Clas fully trusts is the 2005 northern Sweden mobile phone image, because he personally verified it against military radar. Why This Matters Clas Svahn is not speculating. He has radar documentation from classified military files. He has firsthand testimony from pilots, radar operators, and witnesses recorded over decades. He has physical trace evidence analyzed in labs. He has a sonar return from an object embedded in a Swedish lake. He has Betty Hill on tape describing crash debris that no one in UFOlogy has pursued. What makes this different from most UFO testimony is the methodology. Clas treated every case like an investigation, not an argument. He debunked what he could. What survived is harder to dismiss than almost anything in the public record. Full episode documents all of this and more.

Jesse Michels

320,095 Aufrufe • vor 5 Monaten

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.
1:42:20

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.

Morgan Lai

302,663 Aufrufe • vor 2 Monaten

This is the Piano Sonata No. 16 in C major, K. 545 (commonly known as the Sonata Facile or the Sonata for Beginners) by the composer Wolfgang Amadeus Mozart. The performer of this work is the world-renowned pianist and conductor Daniel Barenboim. To seek the deepest wellspring of the greatness of K. 545, we must descend to the very notes themselves as they take shape upon the keys. The law of growth from the opening notes: Many praise the initial chain of three notes, C–E–G. Yet what merits even greater attention is the interval between them: Mozart allows the melody to ascend through two successive thirds (C to E being a third, and E to G another third). He does not leap at random, but adheres to a natural mathematical order. The human mind possesses an innate sensitivity to, and affection for, such patterns; thus, from the very first moment the music sounds, it bestows upon us a profound sense of rightness and euphony. Nevertheless, Mozart’s genius lies in knowing precisely when to cease. Had the melody continued its ascent unchecked—into C–E–G–B–D and beyond—the piece would at once have become a dry technical exercise. Instead, having reached the high G, Mozart turns the line downward, tracing a perfect arc. This motion recurs throughout the work, mirroring with uncanny fidelity the biological rhythm of natural breathing: inhalation must be followed by exhalation; ascent to a climax must yield to a gentle descent into repose. The singing melody and its inner dialogue: A fine Mozartian phrase always resembles a complete utterance in everyday speech. In the opening sentence of K. 545, the listener perceives a structure with a clear beginning, development, and resting point, akin to the natural intonation of a courteous greeting: “Good day,” rather than a fragmented or superfluous “Good… day… to… you.” Musicologists term this quality cantabile—that is, a truly singable melody. It is why pianists have long passed down the classic dictum: “To play Mozart well, one must first know how to sing.” Nor does Mozart’s music merely sing; it engages in an unbroken conversation. Having presented an idea in the first phrase, he does not hasten to introduce an entirely new subject. The following phrase answers the one before it. It is rather like an intimate exchange between two old friends: one remarks, “What a beautiful day,” and the other replies at once, “Indeed, the sky is so clear.” This tight, organic connection—without interruption, overlapping, or abrupt shifts of topic—imbues the work with an unassailable inner logic. The left hand and the breathing of the earth: Many mistakenly regard the left-hand part, with its steady broken-chord figuration (C–G–E–G), as mere monotonous padding intended to fill the voids. In truth, its role is far more profound. Imagine the right-hand melody as a bird soaring and wheeling across the sky. Were the ground beneath it utterly still and silent, the bird would seem suspended and adrift. By maintaining a smooth, continuous motion in the left hand through the Alberti bass, Mozart does not seek to rival the right hand for attention; rather, he creates the subtle yet vital impression that “the earth below is still breathing,” supporting the bird in its flight. It is this union that renders the music so alive and suffused with vital energy. The art of restraint and the weight of every note: The most wondrous aspect of Mozart’s compositional thought is that every note carries an immense functional weight. A note appearing on the third beat of a bar is never merely a pleasing sound. It simultaneously fulfils several duties: it confirms a new harmony, propels the melody forward, prepares the cadence, and maintains equilibrium with the first beat so as to pave the way for what follows. Scholars describe this as the pinnacle of artistic economy: achieving the greatest effect with the most economical means. This restraint is most evident in Mozart’s creation of tension. In the central Development section, he has no need for dozens of clashing dissonances or tempestuous outbursts to unsettle the listener. Like a supremely refined gentleman, Mozart need only raise a subtle eyebrow—through the alteration of a single note or a shift into a nearby minor key—and the listener senses his gravity. As soon as a fleeting unease is felt, he restores the melody to the tonic harmony, bringing back the warm smile of the opening. Such smoothness is made possible by his masterful voice-leading. When moving from one chord to the next, Mozart ensures that no note leaps chaotically. Where a note can remain stationary, it does so; where movement is necessary, he permits it only the shortest possible step to its neighbour. Thanks to these gentle transitions, the listener experiences no jarring sensation of sudden harmonic change. The musical line flows with natural, uninterrupted continuity. Mozart always does less than one expects; he neither prolongs nor embellishes unnecessarily, nor seeks to display cleverness. It is precisely this self-mastery that gives rise to the characteristic elegance and nobility of the Classical style. Finally, if one must identify the deepest source of K. 545’s beauty, the answer lies not in any single bar or isolated triplet of notes, but in a governing principle that runs throughout: every note looks simultaneously in two directions—it is both the natural consequence of what precedes it and the logical preparation for what follows. One may picture the entire sonata as a chain forged with exquisite precision. Each individual link may appear unremarkable in isolation, yet if even one is altered or removed, the whole structure collapses and loses its strength. The listener feels that K. 545 is “absolutely right” and perfect not because of any single overwhelming moment, but because the entire chain of relations—between notes, phrases, and harmonic progressions—moves with such effortless fluency and naturalness that every trace of the human hand arranging it is effaced.
8:05

This is the Piano Sonata No. 16 in C major, K. 545 (commonly known as the Sonata Facile or the Sonata for Beginners) by the composer Wolfgang Amadeus Mozart. The performer of this work is the world-renowned pianist and conductor Daniel Barenboim. To seek the deepest wellspring of the greatness of K. 545, we must descend to the very notes themselves as they take shape upon the keys. The law of growth from the opening notes: Many praise the initial chain of three notes, C–E–G. Yet what merits even greater attention is the interval between them: Mozart allows the melody to ascend through two successive thirds (C to E being a third, and E to G another third). He does not leap at random, but adheres to a natural mathematical order. The human mind possesses an innate sensitivity to, and affection for, such patterns; thus, from the very first moment the music sounds, it bestows upon us a profound sense of rightness and euphony. Nevertheless, Mozart’s genius lies in knowing precisely when to cease. Had the melody continued its ascent unchecked—into C–E–G–B–D and beyond—the piece would at once have become a dry technical exercise. Instead, having reached the high G, Mozart turns the line downward, tracing a perfect arc. This motion recurs throughout the work, mirroring with uncanny fidelity the biological rhythm of natural breathing: inhalation must be followed by exhalation; ascent to a climax must yield to a gentle descent into repose. The singing melody and its inner dialogue: A fine Mozartian phrase always resembles a complete utterance in everyday speech. In the opening sentence of K. 545, the listener perceives a structure with a clear beginning, development, and resting point, akin to the natural intonation of a courteous greeting: “Good day,” rather than a fragmented or superfluous “Good… day… to… you.” Musicologists term this quality cantabile—that is, a truly singable melody. It is why pianists have long passed down the classic dictum: “To play Mozart well, one must first know how to sing.” Nor does Mozart’s music merely sing; it engages in an unbroken conversation. Having presented an idea in the first phrase, he does not hasten to introduce an entirely new subject. The following phrase answers the one before it. It is rather like an intimate exchange between two old friends: one remarks, “What a beautiful day,” and the other replies at once, “Indeed, the sky is so clear.” This tight, organic connection—without interruption, overlapping, or abrupt shifts of topic—imbues the work with an unassailable inner logic. The left hand and the breathing of the earth: Many mistakenly regard the left-hand part, with its steady broken-chord figuration (C–G–E–G), as mere monotonous padding intended to fill the voids. In truth, its role is far more profound. Imagine the right-hand melody as a bird soaring and wheeling across the sky. Were the ground beneath it utterly still and silent, the bird would seem suspended and adrift. By maintaining a smooth, continuous motion in the left hand through the Alberti bass, Mozart does not seek to rival the right hand for attention; rather, he creates the subtle yet vital impression that “the earth below is still breathing,” supporting the bird in its flight. It is this union that renders the music so alive and suffused with vital energy. The art of restraint and the weight of every note: The most wondrous aspect of Mozart’s compositional thought is that every note carries an immense functional weight. A note appearing on the third beat of a bar is never merely a pleasing sound. It simultaneously fulfils several duties: it confirms a new harmony, propels the melody forward, prepares the cadence, and maintains equilibrium with the first beat so as to pave the way for what follows. Scholars describe this as the pinnacle of artistic economy: achieving the greatest effect with the most economical means. This restraint is most evident in Mozart’s creation of tension. In the central Development section, he has no need for dozens of clashing dissonances or tempestuous outbursts to unsettle the listener. Like a supremely refined gentleman, Mozart need only raise a subtle eyebrow—through the alteration of a single note or a shift into a nearby minor key—and the listener senses his gravity. As soon as a fleeting unease is felt, he restores the melody to the tonic harmony, bringing back the warm smile of the opening. Such smoothness is made possible by his masterful voice-leading. When moving from one chord to the next, Mozart ensures that no note leaps chaotically. Where a note can remain stationary, it does so; where movement is necessary, he permits it only the shortest possible step to its neighbour. Thanks to these gentle transitions, the listener experiences no jarring sensation of sudden harmonic change. The musical line flows with natural, uninterrupted continuity. Mozart always does less than one expects; he neither prolongs nor embellishes unnecessarily, nor seeks to display cleverness. It is precisely this self-mastery that gives rise to the characteristic elegance and nobility of the Classical style. Finally, if one must identify the deepest source of K. 545’s beauty, the answer lies not in any single bar or isolated triplet of notes, but in a governing principle that runs throughout: every note looks simultaneously in two directions—it is both the natural consequence of what precedes it and the logical preparation for what follows. One may picture the entire sonata as a chain forged with exquisite precision. Each individual link may appear unremarkable in isolation, yet if even one is altered or removed, the whole structure collapses and loses its strength. The listener feels that K. 545 is “absolutely right” and perfect not because of any single overwhelming moment, but because the entire chain of relations—between notes, phrases, and harmonic progressions—moves with such effortless fluency and naturalness that every trace of the human hand arranging it is effaced.

𝗖𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗠𝗲𝗹𝗼𝗱𝗶𝗲𝘀

31,058 Aufrufe • vor 26 Tagen

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!
3:09

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 Aufrufe • vor 6 Monaten

There is something truly remarkable about the third movement of Ludwig van Beethoven’s Piano Sonata No. 14 in C-sharp minor, Op. 27 No. 2. It is one of the most famous piano movements in the world. Yet if we try to find a simple melody that can be easily hummed, we may notice something fascinating: this movement is not built around a memorable melody in the usual sense. There is no long, gentle singing line. There is no simple theme that immediately settles into the listener’s memory. Instead, Beethoven creates a world of intense motion: notes rushing forward without rest, powerful chords filled with tension, and waves of sound constantly rising and colliding with one another. So what gives this movement such extraordinary vitality after more than two centuries? The answer does not lie in how many notes Beethoven wrote. It lies in the way he transformed the smallest elements into a tremendous source of energy. Beethoven did not write this movement merely for listeners to remember a melody. He wrote it so they could feel a force in motion. From the very first notes of the Presto agitato, Beethoven draws the listener into a world without rest. The broken chords of C-sharp minor (C♯–E–G♯) continuously move across the keyboard. Taken individually, these notes are not extraordinary. But Beethoven was not concerned with the power of each single note. He was concerned with the relationship between them. One note leads naturally to the next. One small movement creates a larger movement. A continuous stream of sound emerges, like a force that cannot be stopped. This is one of Beethoven’s greatest artistic secrets: his ability to take simple materials and transform them into an idea of immense scale. Like a small seed that grows into a great tree, a few basic notes in Beethoven’s hands can become an entire world of emotional depth. When listening to this movement, many people are first captivated by its speed. The endless rushing passages of the piano create the impression of a storm gathering strength. But speed itself is not the true source of power. If it contained only rapid notes, the music might impress us for a moment but would struggle to become a lasting masterpiece. What makes Beethoven different is that he transforms speed into an expression of will. Above, the right hand moves continuously like an unstoppable current of energy. Yet beneath it, the left hand provides strong accents, creating a sense of weight and stability. One side represents movement. The other represents gravity. One force pushes forward. The other prevents the entire structure from collapsing. It is this contrast that creates the feeling of a tremendous power under control. Not a chaotic storm. But a storm with form. One of Beethoven’s deepest principles lies in the way he uses tension. The music constantly creates the feeling that it is searching for a place to return. The harmonies are not completely settled. The changes in harmony leave the listener with a sense of anticipation: what will happen next? When will this stream of energy finally be released? Yet Beethoven does not rush toward the answer. He holds the listener in this state of expectation long enough so that when balance finally appears, it carries greater meaning. This is not only a principle of music. It reflects a profound human experience. We often recognize the value of peace more clearly after passing through turbulence. Calmness has little meaning if we have never experienced tension. Light becomes more visible when it appears after darkness. The most remarkable aspect of this movement is that Beethoven never allows emotion to destroy form. On the surface, the music is intense and forceful, but beneath it lies an extraordinarily disciplined structure. The movement follows the principles of sonata form: an idea is introduced, developed, transformed, and eventually returns in a new state. Small motifs repeatedly appear, but each return carries a different meaning. Repetition does not mean standing still. It becomes the foundation for growth. This is also a principle found throughout nature. A river remains a river, yet the water within it is always moving. A heartbeat repeats itself, yet that repetition is what sustains life. Beethoven works in the same way. He does not create greatness by constantly searching for something new. Instead, he discovers depth by developing his original ideas until they reach their fullest expression. The beauty of the third movement of the Moonlight Sonata is not only found in the music itself. It reflects a broader principle. In nature, the strongest forces are often not forces without order. A storm may have tremendous destructive power, yet it still follows the laws of the atmosphere. A mountain remains standing not because it is untouched by forces, but because its internal structure is strong enough to withstand pressure. A healthy body does not exist because nothing changes, but because millions of small processes work together within a shared order. Beethoven achieves the same thing through sound. He takes thousands of small finger movements, hundreds of relationships between notes, and transforms them into a unified structure with direction and purpose. He does not eliminate conflict. He organizes conflict. He does not weaken power. He creates a form large enough to contain it. What distinguishes great artists in the classical tradition is not only technical mastery. It is their ability to perceive the inner structure of a work. In Murray Perahia’s interpretation, listeners do not experience only the speed and intensity of the movement. They also sense the balance beneath it: the precision of rhythm, the logic of harmonic movement, and the connection between each small part and the entire architectural design. Perahia does not attempt to make Beethoven more dramatic than he already is. Instead, he allows Beethoven’s own structure to reveal its strength. Because a great performer does not stand before a work to overshadow it. They become a means through which the inner beauty of the composition can be revealed more clearly. If we were to search for a single principle that defines the greatness of this movement, perhaps it would be this: A great power, when placed within a great order, becomes beauty. Beethoven does not transform anger, tension, and conflict into something softer. He preserves their full force, but builds a structure strong enough to carry them. Therefore, what listeners experience is not merely fingers racing across the keys. They experience something deeper: a storm that still has a shape, an energy that still possesses order, and a struggle that can become beauty. That is why the third movement of the Moonlight Sonata continues to move people across generations. Because behind those powerful sounds is not only a display of technical brilliance. It is an image of a fundamental principle of life: The greatest strength is not strength without control. It is strength that discovers the perfect form of its own expression.
6:44

There is something truly remarkable about the third movement of Ludwig van Beethoven’s Piano Sonata No. 14 in C-sharp minor, Op. 27 No. 2. It is one of the most famous piano movements in the world. Yet if we try to find a simple melody that can be easily hummed, we may notice something fascinating: this movement is not built around a memorable melody in the usual sense. There is no long, gentle singing line. There is no simple theme that immediately settles into the listener’s memory. Instead, Beethoven creates a world of intense motion: notes rushing forward without rest, powerful chords filled with tension, and waves of sound constantly rising and colliding with one another. So what gives this movement such extraordinary vitality after more than two centuries? The answer does not lie in how many notes Beethoven wrote. It lies in the way he transformed the smallest elements into a tremendous source of energy. Beethoven did not write this movement merely for listeners to remember a melody. He wrote it so they could feel a force in motion. From the very first notes of the Presto agitato, Beethoven draws the listener into a world without rest. The broken chords of C-sharp minor (C♯–E–G♯) continuously move across the keyboard. Taken individually, these notes are not extraordinary. But Beethoven was not concerned with the power of each single note. He was concerned with the relationship between them. One note leads naturally to the next. One small movement creates a larger movement. A continuous stream of sound emerges, like a force that cannot be stopped. This is one of Beethoven’s greatest artistic secrets: his ability to take simple materials and transform them into an idea of immense scale. Like a small seed that grows into a great tree, a few basic notes in Beethoven’s hands can become an entire world of emotional depth. When listening to this movement, many people are first captivated by its speed. The endless rushing passages of the piano create the impression of a storm gathering strength. But speed itself is not the true source of power. If it contained only rapid notes, the music might impress us for a moment but would struggle to become a lasting masterpiece. What makes Beethoven different is that he transforms speed into an expression of will. Above, the right hand moves continuously like an unstoppable current of energy. Yet beneath it, the left hand provides strong accents, creating a sense of weight and stability. One side represents movement. The other represents gravity. One force pushes forward. The other prevents the entire structure from collapsing. It is this contrast that creates the feeling of a tremendous power under control. Not a chaotic storm. But a storm with form. One of Beethoven’s deepest principles lies in the way he uses tension. The music constantly creates the feeling that it is searching for a place to return. The harmonies are not completely settled. The changes in harmony leave the listener with a sense of anticipation: what will happen next? When will this stream of energy finally be released? Yet Beethoven does not rush toward the answer. He holds the listener in this state of expectation long enough so that when balance finally appears, it carries greater meaning. This is not only a principle of music. It reflects a profound human experience. We often recognize the value of peace more clearly after passing through turbulence. Calmness has little meaning if we have never experienced tension. Light becomes more visible when it appears after darkness. The most remarkable aspect of this movement is that Beethoven never allows emotion to destroy form. On the surface, the music is intense and forceful, but beneath it lies an extraordinarily disciplined structure. The movement follows the principles of sonata form: an idea is introduced, developed, transformed, and eventually returns in a new state. Small motifs repeatedly appear, but each return carries a different meaning. Repetition does not mean standing still. It becomes the foundation for growth. This is also a principle found throughout nature. A river remains a river, yet the water within it is always moving. A heartbeat repeats itself, yet that repetition is what sustains life. Beethoven works in the same way. He does not create greatness by constantly searching for something new. Instead, he discovers depth by developing his original ideas until they reach their fullest expression. The beauty of the third movement of the Moonlight Sonata is not only found in the music itself. It reflects a broader principle. In nature, the strongest forces are often not forces without order. A storm may have tremendous destructive power, yet it still follows the laws of the atmosphere. A mountain remains standing not because it is untouched by forces, but because its internal structure is strong enough to withstand pressure. A healthy body does not exist because nothing changes, but because millions of small processes work together within a shared order. Beethoven achieves the same thing through sound. He takes thousands of small finger movements, hundreds of relationships between notes, and transforms them into a unified structure with direction and purpose. He does not eliminate conflict. He organizes conflict. He does not weaken power. He creates a form large enough to contain it. What distinguishes great artists in the classical tradition is not only technical mastery. It is their ability to perceive the inner structure of a work. In Murray Perahia’s interpretation, listeners do not experience only the speed and intensity of the movement. They also sense the balance beneath it: the precision of rhythm, the logic of harmonic movement, and the connection between each small part and the entire architectural design. Perahia does not attempt to make Beethoven more dramatic than he already is. Instead, he allows Beethoven’s own structure to reveal its strength. Because a great performer does not stand before a work to overshadow it. They become a means through which the inner beauty of the composition can be revealed more clearly. If we were to search for a single principle that defines the greatness of this movement, perhaps it would be this: A great power, when placed within a great order, becomes beauty. Beethoven does not transform anger, tension, and conflict into something softer. He preserves their full force, but builds a structure strong enough to carry them. Therefore, what listeners experience is not merely fingers racing across the keys. They experience something deeper: a storm that still has a shape, an energy that still possesses order, and a struggle that can become beauty. That is why the third movement of the Moonlight Sonata continues to move people across generations. Because behind those powerful sounds is not only a display of technical brilliance. It is an image of a fundamental principle of life: The greatest strength is not strength without control. It is strength that discovers the perfect form of its own expression.

🎼🌺Music Love♥️

18,433 Aufrufe • vor 12 Tagen

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.
35:48

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

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