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HOT! MiniMax-H3 Fun Controlnet Union dropped by Alibaba! all-in-one control for MiniMaxH3 - Canny, Depth, HED, MLSD, Pose - inpainting - single 7GB checkpoint - guidance-distilled for fast 1-pass inference

21,318 次观看 • 4 天前 •via X (Twitter)

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The Future of AI Filmmaking Starts Here with MiniMax H3 MiniMax Design (H3) #MiniMaxH3 Try here : Model: MiniMax H3 Aspect Ratio: 3:4 Resolution: 2K Prompt: Create a 40-second Hollywood-style cinematic film teaser for a sci-fi thriller titled "THE LAST SIGNAL." Scene 1: A lone astronaut walks across an abandoned lunar research station under a dark sky. Dust floats in slow motion. One red emergency light flashes. Ultra-realistic, IMAX quality, volumetric lighting. Scene 2: Deep underground, scientists discover an ancient metallic object buried beneath ice. Strange glowing symbols slowly activate. Cinematic camera push-in. Scene 3: A mysterious signal begins spreading across satellites surrounding Earth. Massive orbital structures light up simultaneously. Scene 4: Cities worldwide lose power. Giant holographic waves move through skyscrapers. Rain, smoke, emergency vehicles, cinematic chaos. Scene 5: A female scientist stares at thousands of floating holographic equations while whispering, "It's communicating." Scene 6: A colossal alien structure slowly rises from the ocean during sunrise. Enormous scale, cinematic drone shot. Scene 7: Fast montage: • fighter jets • exploding satellites • astronauts floating in zero gravity • underground bunker • frightened child looking at the sky • giant alien silhouette inside storm clouds Final Scene: The screen fades to black. Title appears: THE LAST SIGNAL Tagline: "They were never gone." Coming Soon. Style: Hollywood blockbuster, Denis Villeneuve-inspired cinematic language, ultra-realistic, 8K, anamorphic lenses, dramatic contrast, volumetric fog, realistic skin, global illumination, HDR, dynamic camera movement, cinematic color grading, premium VFX, emotionally intense pacing, realistic physics, Dolby-style atmosphere, epic scale.

Mr Farman Ai

14,175 次观看 • 28 天前

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.

Victor M

54,264 次观看 • 16 天前

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,841,161 次观看 • 4 个月前

THIS GUY JUST REBUILT A $35,000 ANIMATED SITE FOR $12. IF YOU RUN A WEB STUDIO, YOU SHOULD PROBABLY KEEP SCROLLING. Every agency billing $100-149/hr is selling you five departments wearing one invoice. Here’s each one - collapsed into a single agentic session. LAYER 1 - THE CONCEPT ROOM (Claude) Reads the brief, pulls references, and scripts the scroll: what the visitor feels at second 3, second 15, second 40. → Used to be a strategist and a wall of mood boards. Now it’s a conversation. LAYER 2 - THE MOTION STUDIO (Higgsfield) Cinematic clips from 30+ generative models - hero shots, transitions, ambient loops - all matched to the story from Layer 1. → Used to be a motion artist on retainer. Now it’s a prompt. LAYER 3 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. LAYER 4 - THE DESIGN DEPT (baked-in cinematic layer) Six effects, zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. LAYER 5 - THE QA PASS (Claude) Checks load speed, mobile breakpoints, and whether the scroll actually lands - then rewrites whatever doesn’t. → Used to be a client call and a revision cycle. Now it’s one more turn in the same session. Five departments. One operator. One pass. A strategist, a motion artist, a developer, a designer, and a QA lead - weeks of handoffs - now run in a single session. For a Claude subscription and a few dollars of Higgsfield credits. The studio was never selling talent. It was selling overhead. And the overhead just became five layers. Follow me, reply “website” to this post and I will send you the step-by-step Playbook 👇

ZEUS⚡️

141,226 次观看 • 1 个月前

50% more context unlocked for Qwen 3.8 27b Q4_K_XL dflash 2 on a single RTX 4090 (24 GB VRAM) I found a hidden VRAM tax in llama.cpp. By combining my custom 2 bit DFlash 2 drafter with one overlooked server flag, I just unlocked another +80,000 tokens of context. Qwen3.8-27B is now running a massive 250,000 context at 75 tokens/s on a single RTX 4090. Here is the secret: By default, `llama-server` reserves massive chunks of your VRAM to handle multiple concurrent users (batching). If you are running a single user session, you are bleeding memory for features you aren't using. By passing the `--parallel 1` flag, you force the engine to dedicate 100% of your 24GB VRAM buffer to a single user. When we combine the VRAM saved by our Q2_K 2-bit drafter with the VRAM saved by `--parallel 1`, the context ceilings absolutely explode: Note: all benchmarks carried out with a massive 28k prompt. Ubuntu 22. ### THE NEW 24GB PHYSICAL LIMITS (Single RTX 4090): # 1. The "Repo Swallower" (Q4 KV Cache): - Context: 250,000 tokens (Up from 170k!) - Speed: 73.66 t/s decode | 1,608 t/s prefill - Peak VRAM: 23.8 GB # 2. The "High-Precision SWE" (Q8 KV Cache): - Context: 150,000 tokens (Up from 100k!) - Speed: 75.01 t/s decode | 1,667 t/s prefill - Peak VRAM: 23.9 GB # 3. The "Pristine Attention" (Unquantized FP16 KV): - Context: 90,000 tokens - Speed: 80.58 t/s decode | 1,699 t/s prefill - Peak VRAM: 23.92 GB ### HOW TO RUN THE 250K GOD STACK TODAY: (Requires PR #27342 + my Q2_K Hugging Face drafter) llama.cpp flags: ./build/bin/llama-server -m Qwen3.8-27B-UD-Q4_K_XL.gguf -md Qwen3.8-27B-DFlash2-Q2_K.gguf --spec-type draft-dflash --spec-draft-n-max 3 -c 250000 -ngl 99 --parallel 1 --port 8080 -ctv q4_0 -ctk q4_0 We are pushing a quarter million tokens of context with speculative DFlash 2 decoding at 73 tokens/second on a single consumer gaming GPU. I dropped my custom 2 bit Hugging Face GGUF links, visual performance graphs, and the PR #27342 build instructions in the replies below. If you own a single RTX 3090 or 4090, it is officially time to cancel your API subscriptions and let local silicon eat the cloud. how much monthly API spend does an optimized 4090 rig like this actually replace for you?

Alok

39,189 次观看 • 7 天前

I genuinely want our 🇮🇳 economy to reach $5 trillion by 2027. But with GDP growth at 6.4% in Q2 FY26 & FII's pulling out ₹1.75 lakh crore in 2024-25, #Budget2026 on February 1st must deliver bold reforms ! 📈 👉Sharing my 7 critical expectations from our FM Nirmala Sitharaman ji 👇.. 📊 Cut LTCG tax back to 10% & double exemption to ₹2.5 lakh 12.5% tax is killing long term returns for every equity investor. Before July 2024 we paid only 10% above ₹1 lakh & people stayed invested happily. Government hiked it suddenly & broke confidence. Roll back to 10% now. Raise exemption from ₹1.25 lakh to ₹2.5 lakh so small SIP guys with ₹10k-20k monthly build wealth without tax on modest gains. Foreigners pulled ₹1.6 lakh crore in 2024-25 partly due to this. Want them back? Stop squeezing returns & protect investors. 📊 Bring STCG tax down from 20% to 15% Jump from 15% to 20% in July 2024 was brutal. Short term traders pay 33% more tax now. Volumes crashed & retail participation dropped hard in late 2024. Less trading means poor liquidity & bad prices for everyone. Drop to 15% now. Markets will wake up, volumes jump, exchanges compete globally. More action means better valuations & wealth for all. 📊 Abolish STT completely or cut by 50%, end double tax nonsense STT on every buy & sell plus capital gains tax on profit is pure double robbery. No major country does this. Government took ₹78000 crore from STT in FY26. Remove STT fully or slash rates half. Make trading cheap & retail will flood back huge. 📊 Massive infra push, commit ₹15-18 lakh crore capex & finish fast ₹11.2 lakh crore capex is too small for $5 trillion by 2027 or beating China on infra. Announce ₹15-18 lakh crore for roads, ports, airports, metros, defence & digital. But projects stuck years in clearances & land fights. Force single window clearance in 30 days. Set fast track courts for disputes in 6 months max. Give infra status to real estate & affordable housing for cheap loans. Every ₹1 spent creates ₹4-5 in cement steel construction. This pushes infra & capital goods stocks 100-150% in 3 years & millions jobs. 📊 Manufacturing boom, 10 year tax holiday plus 50% first year depreciation Make India better than Vietnam Bangladesh Mexico for factories. Give new units 10 years zero tax, especially Tier 2-3 cities. Allow 50% depreciation on machinery first year for cash flow boost. Extend PLI to 25 sectors like toys footwear auto parts chemicals. Cut customs slabs from 8 to 4 simple. One time amnesty to clear old stuck money. This means export boom, jobs, profits & engineering chemical stocks fly. 📊 Put ₹50k-75k extra cash in middle class pockets yearly Raise standard deduction to ₹1.5 lakh from ₹75000, saves ₹15k-22k tax. Increase 80D to ₹1 lakh from ₹25000 as medical costs exploded post COVID. Boost home loan deduction to ₹3 lakh from ₹2 lakh. These put ₹50k-75k extra in 10+ crore salaried hands yearly. Money goes to cars travel education. Consumption is 55% GDP & drives FMCG auto retail stocks up fast. 📊 GST 2.0, fix ITC delays & make business easy ITC refunds take 3-6 months & block crores cash. Give refunds in 30 days max. One single portal for all. Reduce high GST on EV batteries & parts. Faster refunds let companies invest hire grow. Profits rise, stocks valuations up, dividends better for investors ! Looking forward to a really proactive & well thought budget 2026. 👉 Folks, Are your ready for a blockbuster #Budget2026? @zerodhaonline Nirmala Sitharaman Ministry of Finance PIB India valuepickr Narendra Modi CA Anil Singhvi Zee Business ReserveBankOfIndia Nirmala Sitharamanoffc @varinder_bansal Kaushik Basu Arvind Subramanian Sanjeev Sanyal Prof. Krishnamurthy V Subramanian Jayati Ghosh Karan Bhasin Bibek Debroy @ArvindPanagariya Prof. Shamika Ravi @neelkanthmisra #Budget2026 #TaxReform #InvestInIndia #MakeInIndia #MiddleClassRelief #CapitalGainsTax #InfraPush #ManufacturingBoom #GSTReform #InvestorDemand

Advait Arora

24,580 次观看 • 7 个月前

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

309,424 次观看 • 4 个月前

Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉

MrNeRF

26,219 次观看 • 2 个月前

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emi) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.

wafer

33,134 次观看 • 1 个月前

🚀 Dive into MetaMask Season 1 with Linea: new on-chain quests are live in daGama! We’re excited to announce that new quests from Linea.eth and MetaMask 🦊 are now live on daGama’s Questboard! Dive into the MetaMask Season 1 campaign with the major Layer 2 network Linea, explore the ecosystem in depth, and earn up to 700 daGama XPs 💫 🌐 What is Linea? Linea is zkEVM Layer2 network bringing Ethereum's security, scalability, and developer tools to millions of users. Built by Consensys, it offers fast, low-cost transactions with full EVM compatibility. Nearly 45% of all MetaMask swaps now happen on Linea, showing how quickly it has become a go-to destination for DeFi, perpetuals, gaming, and everyday on-chain activity. 🦊 What is MetaMask? MetaMask is a trusted Web3 wallet and browser extension that empowers users to explore DeFi, NFTs, and dApps across multiple blockchains while maintaining full control of their private keys. With over 30 million monthly active users and 100 million yearly users worldwide, it remains one of the most secure and widely adopted crypto wallets in 2025. Now you can join MetaMask Season 1 with Linea via daGama Questboard & get XPs in both campaigns: 1️⃣ Explore MetaMask Season 1 Download MetaMask and add your existing address to the Rewards tab in the daGama mobile app: Reward: 100 XP 2️⃣ Linea swaps Switch to the Linea chain, open the “Trade” tab, and swap any available tokens (min. $100). After a successful swap, leave your transaction ID in the answer field. Reward: 300 XP 3️⃣ Explore perps on MetaMask Start perpetual trading on MetaMask using $LINEA (any amount is acceptable), and submit your transaction ID. Reward: 300 XP ⚠️ Perpetual trading involves high risks. Participate responsibly. ⏳ Don’t miss your chance to boost your XP and climb the lead.

daGama

72,215 次观看 • 8 个月前

MiniMax H3 MiniMax Design (H3) just dropped, and I tested it with this 15-second prompt. The results were more interesting than I expected. The character movement, physical interactions, and shot continuity are all impressive. At first glance, it already feels very close to Seedance 2.0. Watch the final video and find the full prompt below 👇 15-second, 16:9 vertical, continuous single-take video that looks like authentic smartphone footage accidentally captured by a passerby in a city park. Overcast natural daylight, subtle handheld shake, limited phone stabilization, occasional autofocus adjustment, and realistic smartphone compression. The absurd event is filmed with a completely serious, unscripted documentary feeling. 0–3s: [Handheld medium shot] A middle-aged man wearing a dark business suit and tie crouches beside the stone edge of a pond. With a completely serious expression, he slowly scatters pieces of bread from a paper bag to several ordinary koi. Small ripples spread across the water as the fish gather in front of him. 3–7s: [Camera instinctively moves closer] An abnormally huge orange-and-white koi suddenly surges out of the murky water, briefly lifting its upper body above the surface and biting down on the entire bread bag in the man’s left hand. He freezes for half a second, then grips the bag with both hands and leans backward. Startled, the person filming steps back. The image briefly loses focus before locking onto the man and the giant fish again. 7–11s: [Close handheld action shot] The giant koi pulls violently toward the deeper part of the pond. The wet paper bag stretches, the man’s arms tense, and his leather shoes slide repeatedly across the wet stone. His knee strikes the edge of the pond. He tries to brace himself with his right foot, but the sole loses traction and his center of gravity moves past the edge. Water, pieces of bread, and fallen leaves scatter from the force as the camera operator hurriedly moves sideways. 11–15s: [Impact and final hold] The paper bag suddenly tears. The man loses all support and pitches forward into the pond, creating one heavy, realistic splash. The camera quickly tilts downward while keeping the center of the pond visible. The man resurfaces with duckweed covering his head and his wet tie stuck across his face. The giant koi calmly swims past him with the remains of the bread bag still in its mouth. The camera holds on the man’s stunned expression while the koi casually swims away beside him. Keep the man’s face, dark suit, tie, and paper bag visually consistent throughout. The koi must retain the same orange-and-white markings and enormous size. The pulling, sliding, loss of balance, and fall must show believable weight, inertia, traction, and water displacement. Natural park ambience and a realistic splash only. No dialogue, no subtitles, no music. Avoid cuts, character teleportation, changes in the fish’s size, extra limbs, and cartoonish acting. #MiniMaxH3 #AIVideo

underwood

12,373 次观看 • 1 个月前

Back in 1994, Ethan Hawke directed a music video for his neighbor who was an unknown, unsigned artist. That artist was Lisa Loeb and her band Nine Stories. The music video was for her song “Stay (I Missed You),” which ended up becoming No. 1 on the Billboard Hot 100, making her the first unsigned artist to top the chart.⁠ ⁠ So how did this interesting collaboration come together? At the time, Loeb and Hawke were part of the same circle of NY-based creatives that included musicians, songwriters, actors, and playwrights who regularly showed up for one another’s work. “We'd just all run around New York together and support each other as well,” Loeb tells Music Times. “I would write music for Ethan's theater company, or Ethan would come see me play.”⁠ ⁠ As neighbors and friends, it wasn’t long before Hawke invited Loeb to send over a song for a film he was working on. Hawke ended up playing her demo of “Stay (I Missed You)” for Ben Stiller, who was directing the film 'Reality Bites' that starred himself, Hawke, and Winona Ryder. ⁠ ⁠ When the song was chosen to be featured in the film, Loeb made the decision to pass on a record deal, opting instead to keep it independent and simply license the track to RCA. "I think the record company let us make a video because Ethan Hawke was directing it, because he was a movie star," Loeb joked. ⁠ ⁠ Whatever the reason, the decision gave her the freedom to create the song’s music video alongside him. Hawke actually filmed different versions before ultimately landing on the one that became the song’s now iconic, single-take music video which even features his cat, Mardot.⁠

Pigeons & Planes

914,848 次观看 • 7 个月前

BREAKING: Iran just confirmed it. Esmaeil Khatib, the Intelligence Minister, is dead. Israeli strikes. The regime that has controlled every syllable of its information war for nineteen days has now officially acknowledged the killing of the man who ran its surveillance state. Massive funeral processions are moving through Tehran today for Ali Larijani and Basij commander Gholamreza Soleimani. State media broadcasting live. Three of the most powerful figures in the Islamic Republic buried in a single week, mourned by crowds that do not yet know how they were found. They were found by traffic cameras. Israel compromised nearly every traffic camera in Tehran years before the first bomb fell, according to the Financial Times. The footage streams in real time to Israeli intelligence. Pattern-of-life analysis. Facial recognition. Vehicle tracking. Route prediction. The FT describes the AI system as a target production machine: it processes billions of data points from mobile networks, social graphs, and intercepted communications, closing the targeting circle before a human operator could finish reading the briefing. On March 18, AI-guided drone swarms with facial recognition struck Basij checkpoint positions across Tehran. Roughly 300 operatives killed in a single wave according to Iran International. The drones used fibre-optic guidance that renders radio-frequency jamming useless. Reports indicate Mossad is calling IRGC commanders directly on personal phones with a single message: you have 12 hours to disappear or you are next. Defence Minister Katz has authorised the IDF to eliminate any senior Iranian official the moment the targeting circle closes. No additional political approval required. Netanyahu gave full operational freedom. The cycle from identification to elimination is now measured in the minutes the AI needs, not the hours a cabinet takes to debate. This is the most technologically advanced targeting apparatus ever deployed in warfare. Hacked municipal surveillance. Autonomous authority. Facial recognition at national scale. Drone swarms with fibre-optic control. Direct psychological contact with enemy commanders. It has killed the Supreme Leader, the intelligence minister, the Basij commander, the diplomatic negotiator, and dozens of senior officials according to aggregated Israeli and Iranian reports. It has degraded 90 to 95 percent of missile production. Israeli military sources indicate three or more weeks of planned strikes remain. Iran confirms the kills by burying the dead. The funerals are the verification that Israeli press conferences could not provide. Tehran’s own processions are the evidence. The crowds are the confirmation. And none of it has moved the urea price by a single dollar. $610 on the CBOT March settlement. Unchanged. Because the Hormuz permissioned chokepoint is not run by the men being buried in Tehran. It is run by a sealed packet in a radio room in Bandar Abbas, written years before this war by men whose funerals happened long ago or have not happened yet. The packet says: grant passage to allied vessels via AIS and VHF confirmation. Deny all others. Continue until further notice from a central command that today has fewer members than it did yesterday. The AI can find a face in a crowd of millions. It cannot find a standing order in a filing cabinet. The targeting circle closes on humans. It does not close on instructions. Tehran buries its intelligence minister, its negotiator, and its Basij commander in a single week. The cameras that found them still watch. The drones that killed them still fly. And the paper that governs whether four billion people eat does not attend funerals. Full analysis:

Shanaka Anslem Perera ⚡

755,632 次观看 • 5 个月前