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🙌🏼Unreal Engine 5.8 just leveled up #Character #Animation! #ControlRig #Dynamics brings fast, intuitive physics directly into your rigs! NO heavy setup, NO complexity. You can now: Spawn dynamic solvers in sec, build & control chains! Enjoy! 🤩

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Stéphane Biava

1,239 subscribers

57,871 Aufrufe • vor 1 Monat •via X (Twitter)

Kunst#Character#Animation#ControlRig#Dynamics
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Maria

17,855 Aufrufe • vor 22 Tagen

🟥 𝗣𝗼𝗰𝗸𝗲𝘁𝗙𝗶 𝟮.𝟬 𝗶𝘀 𝗛𝗘𝗥𝗘 — 𝘁𝗿𝗮𝗱𝗲 𝗦𝗼𝗹𝗮𝗻𝗮 𝘁𝗼𝗸𝗲𝗻𝘀 𝘃𝗶𝗮 𝗧𝗢𝗡 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 With PocketFi 2.0, we’ve unlocked the 𝗳𝘂𝗹𝗹 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 Solana for the TON community, transforming your everyday crypto experience into digital art. Start your Solana journey with PocketFi 2.0 💰 𝗧𝗢𝗡 ↔ 𝗦𝗼𝗹𝗮𝗻𝗮 💰 Swap tokens, explore top protocols, and top up Solana directly from your TON wallet. 💎 𝗚𝗮𝘀𝗹𝗲𝘀𝘀 𝘀𝘄𝗮𝗽𝘀 & 𝗯𝗲𝘀𝘁-𝗽𝗿𝗶𝗰𝗲 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 We cover all fees, manage rent, and route your trade for the best deal — all in USDC, no SOL required. 🌍 𝗔𝘂𝘁𝗼𝗽𝗶𝗹𝗼𝘁: 𝗯𝘂𝘆 𝘀𝗺𝗮𝗿𝘁, 𝗰𝗵𝗶𝗹𝗹 𝗵𝗮𝗿𝗱 Set up auto-buys for any Solana asset and average your entry price with gradual, low-risk stacking. 📁 𝗘𝘅𝗽𝗼𝗿𝘁 𝗦𝗼𝗹𝗮𝗻𝗮 𝘄𝗮𝗹𝗹𝗲𝘁 You can export Solana seed phrase anytime in settings — full control, whenever you need it. 🤩 …and of course new ref rewards. Invite your friends and get lifetime rewards — 𝘂𝗽 𝘁𝗼 𝟯𝟬% of their trading fees go to you. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗠𝗼𝗿𝗲: ▫️ Instantly switch between TON and Solana right from the home screen. ▫️ Track everything in the History tab — from deposits to Autopilot setups. ▫️ Flex your PnL with the community using the Share button in the Holdings tab. ▫️ View token data, favorite assets, and share them — all in one place on the token screen. ▫️ Always get the best price — smart routing finds top Solana DEX offers for you. ▫️ No guessing slippage — we auto-adjust it based on market conditions. ▫️ No stuck tokens — we handle rent and clean up unused accounts for you. 🟥 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗧𝗢𝗡 𝗲𝗿𝗮 𝗶𝘀 𝗹𝗶𝘃𝗲 — 𝘁𝗿𝘆 𝗣𝗼𝗰𝗸𝗲𝘁𝗙𝗶 𝟮.𝟬 𝘁𝗼𝗱𝗮𝘆.
0:14

🟥 𝗣𝗼𝗰𝗸𝗲𝘁𝗙𝗶 𝟮.𝟬 𝗶𝘀 𝗛𝗘𝗥𝗘 — 𝘁𝗿𝗮𝗱𝗲 𝗦𝗼𝗹𝗮𝗻𝗮 𝘁𝗼𝗸𝗲𝗻𝘀 𝘃𝗶𝗮 𝗧𝗢𝗡 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 With PocketFi 2.0, we’ve unlocked the 𝗳𝘂𝗹𝗹 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 Solana for the TON community, transforming your everyday crypto experience into digital art. Start your Solana journey with PocketFi 2.0 💰 𝗧𝗢𝗡 ↔ 𝗦𝗼𝗹𝗮𝗻𝗮 💰 Swap tokens, explore top protocols, and top up Solana directly from your TON wallet. 💎 𝗚𝗮𝘀𝗹𝗲𝘀𝘀 𝘀𝘄𝗮𝗽𝘀 & 𝗯𝗲𝘀𝘁-𝗽𝗿𝗶𝗰𝗲 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 We cover all fees, manage rent, and route your trade for the best deal — all in USDC, no SOL required. 🌍 𝗔𝘂𝘁𝗼𝗽𝗶𝗹𝗼𝘁: 𝗯𝘂𝘆 𝘀𝗺𝗮𝗿𝘁, 𝗰𝗵𝗶𝗹𝗹 𝗵𝗮𝗿𝗱 Set up auto-buys for any Solana asset and average your entry price with gradual, low-risk stacking. 📁 𝗘𝘅𝗽𝗼𝗿𝘁 𝗦𝗼𝗹𝗮𝗻𝗮 𝘄𝗮𝗹𝗹𝗲𝘁 You can export Solana seed phrase anytime in settings — full control, whenever you need it. 🤩 …and of course new ref rewards. Invite your friends and get lifetime rewards — 𝘂𝗽 𝘁𝗼 𝟯𝟬% of their trading fees go to you. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗠𝗼𝗿𝗲: ▫️ Instantly switch between TON and Solana right from the home screen. ▫️ Track everything in the History tab — from deposits to Autopilot setups. ▫️ Flex your PnL with the community using the Share button in the Holdings tab. ▫️ View token data, favorite assets, and share them — all in one place on the token screen. ▫️ Always get the best price — smart routing finds top Solana DEX offers for you. ▫️ No guessing slippage — we auto-adjust it based on market conditions. ▫️ No stuck tokens — we handle rent and clean up unused accounts for you. 🟥 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗧𝗢𝗡 𝗲𝗿𝗮 𝗶𝘀 𝗹𝗶𝘃𝗲 — 𝘁𝗿𝘆 𝗣𝗼𝗰𝗸𝗲𝘁𝗙𝗶 𝟮.𝟬 𝘁𝗼𝗱𝗮𝘆.

XPocket

232,502 Aufrufe • vor 1 Jahr

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.
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Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

West Lord

585,493 Aufrufe • vor 21 Tagen

🎉 Over 100,000 Users & OptimAI Lite Node v1.1 Released—Edge Node Coming Soon! Get OptimAI Lite Node v1.1 now: 🌐Chrome Extension: 📱Telegram Mini-app: In just 10 days since launching the OptimAI Lite Node on Chrome Extension and Telegram Mini-app, we’ve surpassed 100,000 active node participants—an incredible milestone thanks to your amazing support! ✨ OptimAI Lite Node v1.1 is now LIVE with significant upgrades: + Enhanced network stability for handling massive concurrent connections. + Improved UI for a smoother, more intuitive user experience. + Making your node experience smoother, more stable, and just plain better. + Automatic updates on Chrome and Telegram—no manual action required. 💡Our Chrome Extension has successfully passed rigorous testing by the Chrome Web Store, demonstrating industry-leading security and transparency. You're always in control, always secure! 🌟Up Next: OptimAI Edge Node for Mobile! We’re working diligently to launch the Edge Node—bringing decentralized AI computing directly to your mobile device. Exciting updates coming soon, so stay tuned! OptimAI Network: #L2 #DePIN Reinforcement Data Network for #Agentic #AI. ⭐ Mine Data. Fuel AI. Earn Rewards. ⭐ Turn Your Data into Tomorrow’s AI #Agent. Learn more at our website:
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🎉 Over 100,000 Users & OptimAI Lite Node v1.1 Released—Edge Node Coming Soon! Get OptimAI Lite Node v1.1 now: 🌐Chrome Extension: 📱Telegram Mini-app: In just 10 days since launching the OptimAI Lite Node on Chrome Extension and Telegram Mini-app, we’ve surpassed 100,000 active node participants—an incredible milestone thanks to your amazing support! ✨ OptimAI Lite Node v1.1 is now LIVE with significant upgrades: + Enhanced network stability for handling massive concurrent connections. + Improved UI for a smoother, more intuitive user experience. + Making your node experience smoother, more stable, and just plain better. + Automatic updates on Chrome and Telegram—no manual action required. 💡Our Chrome Extension has successfully passed rigorous testing by the Chrome Web Store, demonstrating industry-leading security and transparency. You're always in control, always secure! 🌟Up Next: OptimAI Edge Node for Mobile! We’re working diligently to launch the Edge Node—bringing decentralized AI computing directly to your mobile device. Exciting updates coming soon, so stay tuned! OptimAI Network: #L2 #DePIN Reinforcement Data Network for #Agentic #AI. ⭐ Mine Data. Fuel AI. Earn Rewards. ⭐ Turn Your Data into Tomorrow’s AI #Agent. Learn more at our website:

OptimAI Network

224,465 Aufrufe • vor 1 Jahr

BREAKING: LLMs just learned to COMPUTE for real, it's mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured viking:// filesystem memory (L0 ultra-summary -> L2 full details) - agents now run 100+ steps with zero amnesia or hallucinations, 11.6k stars and climbing. Now this just dropped and the entire AI timeline is shaking. Startup Percepta embedded a full WASM virtual machine directly into Transformer weights. No more external Python sandboxes. No more hallucinations in exact tasks. The model streams raw machine code at 30,000+ tokens/sec on CPU, executes millions of steps, and solves the world’s hardest Sudoku via real backtracking + constraint propagation - 100% accurate, zero bullshit. They killed the Attention Bottleneck with Exponentially Fast Attention (HullKVCache + 2D heads + convex hull queries in log time). What used to die at 1k steps now flies. This is the bridge: System 1 intuition (normal LLMs) + System 2 deterministic logic (native code execution) in ONE brain. Agents won’t need tools anymore. Heavy simulations will run inside the weights. Check out: Now put it all together: MiroFish swarms + OpenViking infinite memory + Percepta native flawless compute = agents that can hardcore simulate millions of future scenarios, run perfect logic loops for days, and predict events/markets/reality with god-tier accuracy. No drift. No bullshit. Just pure foresight. This combo will change everything, imo. The era of predictive super-agents that actually print the future is here. We’re watching this one closely. Save this combo.
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BREAKING: LLMs just learned to COMPUTE for real, it's mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured viking:// filesystem memory (L0 ultra-summary -> L2 full details) - agents now run 100+ steps with zero amnesia or hallucinations, 11.6k stars and climbing. Now this just dropped and the entire AI timeline is shaking. Startup Percepta embedded a full WASM virtual machine directly into Transformer weights. No more external Python sandboxes. No more hallucinations in exact tasks. The model streams raw machine code at 30,000+ tokens/sec on CPU, executes millions of steps, and solves the world’s hardest Sudoku via real backtracking + constraint propagation - 100% accurate, zero bullshit. They killed the Attention Bottleneck with Exponentially Fast Attention (HullKVCache + 2D heads + convex hull queries in log time). What used to die at 1k steps now flies. This is the bridge: System 1 intuition (normal LLMs) + System 2 deterministic logic (native code execution) in ONE brain. Agents won’t need tools anymore. Heavy simulations will run inside the weights. Check out: Now put it all together: MiroFish swarms + OpenViking infinite memory + Percepta native flawless compute = agents that can hardcore simulate millions of future scenarios, run perfect logic loops for days, and predict events/markets/reality with god-tier accuracy. No drift. No bullshit. Just pure foresight. This combo will change everything, imo. The era of predictive super-agents that actually print the future is here. We’re watching this one closely. Save this combo.

slash1s

156,699 Aufrufe • vor 4 Monaten

🚨This is insane… this guy built a clawdbot that flipped $69 into $1M+ on POLYMARKET Sounds insane? 100%. Fake? Not even remotely. If you trade on Polymarket, this should have YOUR focus. A fresh wallet just surfaced with 22,173 resolved predictions and $1,013,168 in PNL It trades strictly in NEW 5-minute & 15-minute crypto markets Profile → COPYTRADE → Weekly profit is sitting around ~$100,000 And what powers it is almost offensively simple I reviewed the entire setup No sophisticated ML stacks No institutional-grade systems No complex data pipelines Just automated execution paired with statistical edge His COMPLETE playbook: 1. Ultra-short timeframes Only 5-min Up/Down contracts. Locked into 5 & 15m markets exclusively. Clawdbot-style execution framework. Pure intraday momentum extraction. No swing positions. No storytelling. Just speed and repetition 2. Order splitting + micro sizing Every position is broken into multiple small entries. Fixed predefined size per fill. Size scales only when conviction increases. This limits drawdowns and smooths the equity curve 3. Probability farming The model doesn’t hunt for moonshots. It stacks small mathematical edges. Over thousands of trades = that compounds into six figures monthly Largest trade so far: “Bitcoin Up or Down - January 19, 5-6AM ET” $13,320 → $36,887 (+176%) Now the important part: This can be replicated cursor + py-clob-client + python install the SDK generate Polymarket API keys define the Clawdbot strategy prompt AI builds the Clawdbot execution logic paper test with $1 You don’t need elite discretionary skill Bottom line Why are you still trading manually while automated systems run 24/7?
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🚨This is insane… this guy built a clawdbot that flipped $69 into $1M+ on POLYMARKET Sounds insane? 100%. Fake? Not even remotely. If you trade on Polymarket, this should have YOUR focus. A fresh wallet just surfaced with 22,173 resolved predictions and $1,013,168 in PNL It trades strictly in NEW 5-minute & 15-minute crypto markets Profile → COPYTRADE → Weekly profit is sitting around ~$100,000 And what powers it is almost offensively simple I reviewed the entire setup No sophisticated ML stacks No institutional-grade systems No complex data pipelines Just automated execution paired with statistical edge His COMPLETE playbook: 1. Ultra-short timeframes Only 5-min Up/Down contracts. Locked into 5 & 15m markets exclusively. Clawdbot-style execution framework. Pure intraday momentum extraction. No swing positions. No storytelling. Just speed and repetition 2. Order splitting + micro sizing Every position is broken into multiple small entries. Fixed predefined size per fill. Size scales only when conviction increases. This limits drawdowns and smooths the equity curve 3. Probability farming The model doesn’t hunt for moonshots. It stacks small mathematical edges. Over thousands of trades = that compounds into six figures monthly Largest trade so far: “Bitcoin Up or Down - January 19, 5-6AM ET” $13,320 → $36,887 (+176%) Now the important part: This can be replicated cursor + py-clob-client + python install the SDK generate Polymarket API keys define the Clawdbot strategy prompt AI builds the Clawdbot execution logic paper test with $1 You don’t need elite discretionary skill Bottom line Why are you still trading manually while automated systems run 24/7?

Shelpid.WI3M

10,486 Aufrufe • vor 4 Monaten

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.
0:19

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.

Alok

62,133 Aufrufe • vor 9 Tagen

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.
0:11

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Alok

174,987 Aufrufe • vor 15 Tagen

Impeccable 3.7 brings linting to design. Until now it was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccable
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Impeccable 3.7 brings linting to design. Until now it was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccable

Impeccable

230,789 Aufrufe • vor 1 Monat

You know that moment when you're testing video models and something just... floats across the frame that shouldn't? Yeah, Kling 3.0 apparently got tired of that too. Fast Company's piece on the new Chinese models actually gets into why Kling feels different. Not because the pixels are prettier — but because stuff finally moves like it has weight. Gravity exists. Objects occlude properly. A car turning actually follows an arc instead of just... teleporting sideways. Someone in the comments called it "directing instead of gambling" and that's exactly right. Old workflow: type prompt, pull lever, pray the physics gods smile on you. New workflow: you actually have some damn control. The character locking thing is huge too. Elements feature lets you upload a reference video and it holds identity across shots. No more "wait why does his face look like a different person now" every time the camera angle changes. For short-form series creators, that's basically a "we'll fix it in post" delete button. Still detectable if you pixel-peep. But here's the thing — the threshold moved. Average viewers? They're not gonna catch it anymore. Only the 24/7 screen-staring crowd is still in the "definitely AI" camp.
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You know that moment when you're testing video models and something just... floats across the frame that shouldn't? Yeah, Kling 3.0 apparently got tired of that too. Fast Company's piece on the new Chinese models actually gets into why Kling feels different. Not because the pixels are prettier — but because stuff finally moves like it has weight. Gravity exists. Objects occlude properly. A car turning actually follows an arc instead of just... teleporting sideways. Someone in the comments called it "directing instead of gambling" and that's exactly right. Old workflow: type prompt, pull lever, pray the physics gods smile on you. New workflow: you actually have some damn control. The character locking thing is huge too. Elements feature lets you upload a reference video and it holds identity across shots. No more "wait why does his face look like a different person now" every time the camera angle changes. For short-form series creators, that's basically a "we'll fix it in post" delete button. Still detectable if you pixel-peep. But here's the thing — the threshold moved. Average viewers? They're not gonna catch it anymore. Only the 24/7 screen-staring crowd is still in the "definitely AI" camp.

SANI BULA

662,960 Aufrufe • vor 5 Monaten

Good Morning World ☀️😎 Let me give you a real world example of what using vooi feels like. Imagine you're at a bustling café in Lisbon. Your friend just texted you: SOL is pumping,should I buy? You open your phone, tap one app, and within ten seconds you're long SOL, no wallet popups, no gas fees, no bridge first, pray later. That app is VOOI (vooi), and it feels less like crypto engineering and more like ordering an espresso. VOOI is a noncustodial perpetual and spot aggregator that hides every blockchain headache behind a single, friendly screen. It speaks Ethereum, Solana, BNB, and half a dozen other chains fluently, so you never have to. You log in with an email or a Twitter account, see one balance in dollars, and trade anything,crypto, tokenized Apple shares, perpetual futures,without ever touching native gas tokens. The protocol pays the gas for you, then quietly settles everything onchain while you sip your coffee. How is that even possible? Three quiet revolutions: 1️⃣Intent based routing. Instead of signing a transaction on every chain, you sign a single intent (e.g., 'I want 0.5 SOL'). VOOI's engine scans liquidity across multiple decentralized exchanges, stitches together the cheapest path, and executes the trade behind the scenes.. 2️⃣ Account abstraction. Your identity is tied to a smart wallet that lives in the cloud, protected by the same login you already trust. You still control the keys,VOOI never sees them,but you don't need to remember seed phrases or browser extensions. 3️⃣ A weekly 6millionpoint rewards pool. Every trade you make drips points into your account. Redeem them for fee rebates, USDC cashback, or simply bragging rights. It turns active trading into a loyalty program you actually want to use. Real people are already living this. Take Leila, a freelance designer in Dubai. She keeps a modest USDC balance on VOOI. Last Tuesday, between client calls, she shorted SOL for two hours, closed in profit, and never once thought about gas or bridges. The trade settled instantly, her points balance ticked up, and she went back to her mockups. Or Carlos in Sao Paulo, who wants exposure to Tesla but hates local broker fees. He bought tokenized TSLA on VOOI with the same swipe he uses for USDT. The token lives in his smart wallet; if Tesla rallies, he sells directly back to USDC, no brokerage account, no paperwork. Even David, a fulltime degen, runs tiny arbitrage loops between Ethereum and Solana. Before VOOI, each roundtrip cost him $8 in gas. Now the protocol covers the fees, so his edges stay intact and his profit margins widen. Behind the curtain, VOOI raised $1.25 million in its March 2025 ICO and counts YZi Labs (formerly Binance Labs) as a strategic backer. Liquidity partnerships already pull depth from the biggest DEXs on every major chain. The community on X has ballooned past 190 k followers, not because of flashy marketing, but because the product simply works. The takeaway is simple: VOOI turns the multichain maze into one smooth sidewalk. Whether you're a firsttime buyer who still calls Bitcoin magic internet money or a seasoned trader hunting basis points, you get the same experience,open app, pick market, swipe, done. No gas, no bridges, no custody risk. Just trading that finally feels like the internet we were promised.
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Good Morning World ☀️😎 Let me give you a real world example of what using vooi feels like. Imagine you're at a bustling café in Lisbon. Your friend just texted you: SOL is pumping,should I buy? You open your phone, tap one app, and within ten seconds you're long SOL, no wallet popups, no gas fees, no bridge first, pray later. That app is VOOI (vooi), and it feels less like crypto engineering and more like ordering an espresso. VOOI is a noncustodial perpetual and spot aggregator that hides every blockchain headache behind a single, friendly screen. It speaks Ethereum, Solana, BNB, and half a dozen other chains fluently, so you never have to. You log in with an email or a Twitter account, see one balance in dollars, and trade anything,crypto, tokenized Apple shares, perpetual futures,without ever touching native gas tokens. The protocol pays the gas for you, then quietly settles everything onchain while you sip your coffee. How is that even possible? Three quiet revolutions: 1️⃣Intent based routing. Instead of signing a transaction on every chain, you sign a single intent (e.g., 'I want 0.5 SOL'). VOOI's engine scans liquidity across multiple decentralized exchanges, stitches together the cheapest path, and executes the trade behind the scenes.. 2️⃣ Account abstraction. Your identity is tied to a smart wallet that lives in the cloud, protected by the same login you already trust. You still control the keys,VOOI never sees them,but you don't need to remember seed phrases or browser extensions. 3️⃣ A weekly 6millionpoint rewards pool. Every trade you make drips points into your account. Redeem them for fee rebates, USDC cashback, or simply bragging rights. It turns active trading into a loyalty program you actually want to use. Real people are already living this. Take Leila, a freelance designer in Dubai. She keeps a modest USDC balance on VOOI. Last Tuesday, between client calls, she shorted SOL for two hours, closed in profit, and never once thought about gas or bridges. The trade settled instantly, her points balance ticked up, and she went back to her mockups. Or Carlos in Sao Paulo, who wants exposure to Tesla but hates local broker fees. He bought tokenized TSLA on VOOI with the same swipe he uses for USDT. The token lives in his smart wallet; if Tesla rallies, he sells directly back to USDC, no brokerage account, no paperwork. Even David, a fulltime degen, runs tiny arbitrage loops between Ethereum and Solana. Before VOOI, each roundtrip cost him $8 in gas. Now the protocol covers the fees, so his edges stay intact and his profit margins widen. Behind the curtain, VOOI raised $1.25 million in its March 2025 ICO and counts YZi Labs (formerly Binance Labs) as a strategic backer. Liquidity partnerships already pull depth from the biggest DEXs on every major chain. The community on X has ballooned past 190 k followers, not because of flashy marketing, but because the product simply works. The takeaway is simple: VOOI turns the multichain maze into one smooth sidewalk. Whether you're a firsttime buyer who still calls Bitcoin magic internet money or a seasoned trader hunting basis points, you get the same experience,open app, pick market, swipe, done. No gas, no bridges, no custody risk. Just trading that finally feels like the internet we were promised.

Crypto Sinan

17,410 Aufrufe • vor 10 Monaten