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Guri Singh

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Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

2,180,357 просмотров

Holy shit... someone built a free portable tool that activates Windows, kills the bloatware, and shuts down every telemetry service Microsoft buried in your OS. It's called GTweak. One .exe file. No install. No subscription. You download it, run it, and your Windows is finally yours. Every "Windows debloater" before this made you pick one trade-off. Activate but keep the spyware. Debloat but lose update control. Disable telemetry but break Defender toggles. GTweak does all of it inside a single portable executable. Here's what makes it different from every Windows tweaker that came before: → HWID + KMS activation built in, no sketchy batch scripts, no Massgrave links, no PowerShell one-liners pasted from a Reddit thread → Removes Cortana, Copilot, Recall, OneDrive, Edge, and every pre-installed UWP app on Windows 10 and 11 in one click → Disables keyloggers and telemetry across Windows and NVIDIA including the data collection tasks hiding in Task Scheduler that nobody talks about → Blocks Microsoft's shadow domains at the hosts file AND firewall level so the OS literally cannot phone home → Disables Defender, SmartScreen, Antimalware, VBS, and UAC with proper toggles instead of registry hacks that break on the next update → Pauses Windows Updates entirely and wipes the cached update files Microsoft refuses to let you delete → Kills Teredo, ISATAP, and IPv6 along with the diagnostic services running silently in the background → Activates the hidden Ultimate Performance power plan and fixes the Realtek audio delay bug nobody at Microsoft has patched in 5 years → Runs custom .ps1, .cmd, .bat, .reg scripts with TrustedInstaller privileges, the highest permission level Windows has → Built-in hardware monitor, NTFS compression, RAM cleaner, and secure Windows.old wipe Killed: $30 Windows 11 Pro keys, every "debloat script" repo with 47 forks and no maintenance, the $5/mo "PC optimizer" garbage running on YouTube ads. Works on every official Windows build since 10 (18362.116). One .NET Framework 4.8 dependency that's already on your machine. BSD 3-Clause License. 100% Opensource.

Holy shit... someone built a free portable tool that activates Windows, kills the bloatware, and shuts down every telemetry service Microsoft buried in your OS. It's called GTweak. One .exe file. No install. No subscription. You download it, run it, and your Windows is finally yours. Every "Windows debloater" before this made you pick one trade-off. Activate but keep the spyware. Debloat but lose update control. Disable telemetry but break Defender toggles. GTweak does all of it inside a single portable executable. Here's what makes it different from every Windows tweaker that came before: → HWID + KMS activation built in, no sketchy batch scripts, no Massgrave links, no PowerShell one-liners pasted from a Reddit thread → Removes Cortana, Copilot, Recall, OneDrive, Edge, and every pre-installed UWP app on Windows 10 and 11 in one click → Disables keyloggers and telemetry across Windows and NVIDIA including the data collection tasks hiding in Task Scheduler that nobody talks about → Blocks Microsoft's shadow domains at the hosts file AND firewall level so the OS literally cannot phone home → Disables Defender, SmartScreen, Antimalware, VBS, and UAC with proper toggles instead of registry hacks that break on the next update → Pauses Windows Updates entirely and wipes the cached update files Microsoft refuses to let you delete → Kills Teredo, ISATAP, and IPv6 along with the diagnostic services running silently in the background → Activates the hidden Ultimate Performance power plan and fixes the Realtek audio delay bug nobody at Microsoft has patched in 5 years → Runs custom .ps1, .cmd, .bat, .reg scripts with TrustedInstaller privileges, the highest permission level Windows has → Built-in hardware monitor, NTFS compression, RAM cleaner, and secure Windows.old wipe Killed: $30 Windows 11 Pro keys, every "debloat script" repo with 47 forks and no maintenance, the $5/mo "PC optimizer" garbage running on YouTube ads. Works on every official Windows build since 10 (18362.116). One .NET Framework 4.8 dependency that's already on your machine. BSD 3-Clause License. 100% Opensource.

171,691 просмотров

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.

27,404 просмотров

You're still scraping Google Flights like it's 2022. Meanwhile, people are finding business class seats for economy prices and $200 round-trips on dates nobody thinks to check. Someone just reverse-engineered the actual Google Flights API and turned it into an MCP server for Claude. It's called Fli. No scraping. No HTML parsing. No Playwright scripts that break every time Google ships a UI change. Drop it in your Claude Desktop config and ask in plain English: >> "Non-stop business JFK to NRT next month" >> "Cheapest Fridays NYC to London in January" >> "SFO to LAX under 6 hours, United or Delta only" Two tools do the work: → search_flights: filter by cabin, airlines, stops, time windows → search_dates: scan a date range for the cheapest days to fly Wildest part: it's also a Python library and a CLI. Build price trackers, pipe results into pandas, or just run `fli flights JFK LHR 2026-10-25` from your terminal. Everyone's been waiting for someone to crack Google Flights without the scraping tax. 100% open source. MIT licensed. (Link in the comments)

You're still scraping Google Flights like it's 2022. Meanwhile, people are finding business class seats for economy prices and $200 round-trips on dates nobody thinks to check. Someone just reverse-engineered the actual Google Flights API and turned it into an MCP server for Claude. It's called Fli. No scraping. No HTML parsing. No Playwright scripts that break every time Google ships a UI change. Drop it in your Claude Desktop config and ask in plain English: >> "Non-stop business JFK to NRT next month" >> "Cheapest Fridays NYC to London in January" >> "SFO to LAX under 6 hours, United or Delta only" Two tools do the work: → search_flights: filter by cabin, airlines, stops, time windows → search_dates: scan a date range for the cheapest days to fly Wildest part: it's also a Python library and a CLI. Build price trackers, pipe results into pandas, or just run `fli flights JFK LHR 2026-10-25` from your terminal. Everyone's been waiting for someone to crack Google Flights without the scraping tax. 100% open source. MIT licensed. (Link in the comments)

76,411 просмотров

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

20,591 просмотров

2. Irresistible Offer Architect "Make them say YES instantly" Prompt: Turn [product/service] into a high-value offer that social media managers & small business owners can’t ignore. Include bonuses, pricing psychology & value-boost tactics.

2. Irresistible Offer Architect "Make them say YES instantly" Prompt: Turn [product/service] into a high-value offer that social media managers & small business owners can’t ignore. Include bonuses, pricing psychology & value-boost tactics.

27,723 просмотров

4. Conversion Website Blueprint "Your site should SELL, not just sit there" Prompt: Design a high-converting website for my [business type] with page layouts & copy ideas for homepage, about, sales page & FAQ. Focus on trust & killing objections.

4. Conversion Website Blueprint "Your site should SELL, not just sit there" Prompt: Design a high-converting website for my [business type] with page layouts & copy ideas for homepage, about, sales page & FAQ. Focus on trust & killing objections.

20,410 просмотров

6. Customer Acquisition Formula "Get your first sales without ads" Prompt: Step-by-step plan to sell my [product/service] using only my personal network, Instagram & Canva skills — no paid ads.

6. Customer Acquisition Formula "Get your first sales without ads" Prompt: Step-by-step plan to sell my [product/service] using only my personal network, Instagram & Canva skills — no paid ads.

14,497 просмотров

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🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

2,967,409 просмотров • 2 месяцев назад

Holy shit... A guy got laid off, built an AI job search system on Claude Code, evaluated 740+ job offers with it, and landed a Head of Applied AI role. Then he open-sourced the entire thing. It's called career-ops. One slash command. Full pipeline. Paste a job URL → get back a structured A-F evaluation, an ATS-optimized PDF tailored to that exact role, salary research, interview prep, and a tracker entry. All in one shot. No spreadsheets. No copy-pasting. No spray-and-pray. Here's what's inside: → 14 skill modes (evaluate, scan, pdf, batch, apply, deep research, negotiation scripts, LinkedIn outreach) → Portal scanner pre-loaded with 45+ companies — Anthropic, OpenAI, ElevenLabs, Mistral, Cohere, Stripe, Retool, Vercel, Decagon, the works → 19 search queries across Ashby, Greenhouse, Lever, Wellfound, Workable → ATS-optimized PDF generation via Playwright with Space Grotesk + DM Sans → Go terminal dashboard built with Bubble Tea to browse your pipeline → Batch mode that evaluates 10+ offers in parallel using Claude sub-agents → An interview Story Bank that accumulates STAR+Reflection stories across evaluations until you have 5-10 master answers for any behavioral question → Auto-fill for application forms The wildest part isn't the automation. It's the philosophy. Career-ops is explicitly NOT a spray-and-pray tool. It's a filter. The system literally refuses to recommend applying to anything scoring below 4.0/5. The whole point is to find the few offers worth your time out of hundreds, not to flood recruiters with garbage. It evaluates fit by reasoning about your CV vs the JD. Not keyword matching. And because it's all built on Claude Code skills, you can ask Claude to rewrite the system itself. "Change the archetypes to backend roles." "Add these 10 companies." "Translate the modes to English." It reads the same files it uses, so it knows exactly what to edit. 8.2k stars already. 100% Open Source. MIT licensed. (Link in the replies)

Holy shit... A guy got laid off, built an AI job search system on Claude Code, evaluated 740+ job offers with it, and landed a Head of Applied AI role. Then he open-sourced the entire thing. It's called career-ops. One slash command. Full pipeline. Paste a job URL → get back a structured A-F evaluation, an ATS-optimized PDF tailored to that exact role, salary research, interview prep, and a tracker entry. All in one shot. No spreadsheets. No copy-pasting. No spray-and-pray. Here's what's inside: → 14 skill modes (evaluate, scan, pdf, batch, apply, deep research, negotiation scripts, LinkedIn outreach) → Portal scanner pre-loaded with 45+ companies — Anthropic, OpenAI, ElevenLabs, Mistral, Cohere, Stripe, Retool, Vercel, Decagon, the works → 19 search queries across Ashby, Greenhouse, Lever, Wellfound, Workable → ATS-optimized PDF generation via Playwright with Space Grotesk + DM Sans → Go terminal dashboard built with Bubble Tea to browse your pipeline → Batch mode that evaluates 10+ offers in parallel using Claude sub-agents → An interview Story Bank that accumulates STAR+Reflection stories across evaluations until you have 5-10 master answers for any behavioral question → Auto-fill for application forms The wildest part isn't the automation. It's the philosophy. Career-ops is explicitly NOT a spray-and-pray tool. It's a filter. The system literally refuses to recommend applying to anything scoring below 4.0/5. The whole point is to find the few offers worth your time out of hundreds, not to flood recruiters with garbage. It evaluates fit by reasoning about your CV vs the JD. Not keyword matching. And because it's all built on Claude Code skills, you can ask Claude to rewrite the system itself. "Change the archetypes to backend roles." "Add these 10 companies." "Translate the modes to English." It reads the same files it uses, so it knows exactly what to edit. 8.2k stars already. 100% Open Source. MIT licensed. (Link in the replies)

973,412 просмотров • 1 месяц назад

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇

303,140 просмотров • 1 месяц назад

Alibaba just open-sourced the world's FIRST AI dubbing model that handles multi-speaker scenes. It's called Fun-CineForge and it nails what every other dubbing model fails at: Lip-sync. Emotion. Voice stability. Timing. Across multiple characters. Not a single open-source model could do this before. Here's how it works: ↓ Tongyi Lab Alibaba Group

Alibaba just open-sourced the world's FIRST AI dubbing model that handles multi-speaker scenes. It's called Fun-CineForge and it nails what every other dubbing model fails at: Lip-sync. Emotion. Voice stability. Timing. Across multiple characters. Not a single open-source model could do this before. Here's how it works: ↓ Tongyi Lab Alibaba Group

620,634 просмотров • 2 месяцев назад

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)

244,515 просмотров • 2 месяцев назад

Holy shit... Typeless just solved the most annoying problem in multilingual communication. Speak in English. Get perfectly written text in Spanish, French, or any language phrased like a native speaker wrote it. No Google Translate. No awkward phrasing. No copy-pasting between apps. Just hold the Speak button, swipe up, and talk. It doesn't just translate your words, it translates your intent. Filler words gone. Grammar fixed. Output reads like a local typed it. Available on Mac and iOS. 4,000 words/week free. Try it: iOS:

Holy shit... Typeless just solved the most annoying problem in multilingual communication. Speak in English. Get perfectly written text in Spanish, French, or any language phrased like a native speaker wrote it. No Google Translate. No awkward phrasing. No copy-pasting between apps. Just hold the Speak button, swipe up, and talk. It doesn't just translate your words, it translates your intent. Filler words gone. Grammar fixed. Output reads like a local typed it. Available on Mac and iOS. 4,000 words/week free. Try it: iOS:

271,704 просмотров • 2 месяцев назад

Cloud GPU training is a scam. A single M4 MacBook does 2.9 TFLOPS. Seven friends with MacBooks match an NVIDIA A100. Alexander Hayes just open-sourced a tool that makes this work over Wi-Fi. It's called AirTrain. Here's how it works: Traditional distributed training (DDP) syncs gradients after every single step. For a 124M parameter model, that's ~500MB exchanged per step. You need 50 GB/s of sustained bandwidth. Impossible over Wi-Fi. AirTrain uses the DiLoCo algorithm. Each Mac trains independently for 500 steps, then syncs only the difference. One sync per 500 steps instead of one per step. 500x less network communication. Wi-Fi actually works. The entire sync takes ~2 seconds. Here's what makes it wild: → Zero-config discovery. Devices find each other automatically via mDNS/Bonjour. Same protocol as AirDrop. → Fault tolerant. Nodes can join and leave mid-training without killing the run. → Checkpoint relay. Train for a few hours, export a checkpoint, hand it off to someone else to continue. Like a relay race for ML training. → Built on Apple's MLX framework. Native to M1/M2/M3/M4/M5 unified memory. No host-to-device copy overhead. → Local dashboard. Real-time loss curves, peer monitoring, throughput metrics in your browser. Here's the wildest part: An M4 Max with 128GB unified memory can train a 70B parameter model without offloading. An NVIDIA RTX 4090 has 24GB VRAM. Apple Silicon gets ~245-460 GFLOPS per watt. Training on MacBooks costs almost nothing in electricity compared to cloud GPUs. And there are hundreds of millions of Apple Silicon Macs in the world. The math: Traditional DDP: 1 sync per step = 50 GB/s required AirTrain (DiLoCo): 1 sync per 500 steps = 0.1 GB/s required Wi-Fi handles 0.1 GB/s. That's it. That's the breakthrough. They even built a community platform at with live session browsing, checkpoint sharing, and a contributor leaderboard. Training a 124M parameter GPT-2? Instead of renting cloud GPUs at $3/hr, pool three MacBooks in a coffee shop and train for free. MIT licensed. Built in Python. 1 contributor. Early stage but the idea is insane. 100% Open Source. (Link in the comments)

Cloud GPU training is a scam. A single M4 MacBook does 2.9 TFLOPS. Seven friends with MacBooks match an NVIDIA A100. Alexander Hayes just open-sourced a tool that makes this work over Wi-Fi. It's called AirTrain. Here's how it works: Traditional distributed training (DDP) syncs gradients after every single step. For a 124M parameter model, that's ~500MB exchanged per step. You need 50 GB/s of sustained bandwidth. Impossible over Wi-Fi. AirTrain uses the DiLoCo algorithm. Each Mac trains independently for 500 steps, then syncs only the difference. One sync per 500 steps instead of one per step. 500x less network communication. Wi-Fi actually works. The entire sync takes ~2 seconds. Here's what makes it wild: → Zero-config discovery. Devices find each other automatically via mDNS/Bonjour. Same protocol as AirDrop. → Fault tolerant. Nodes can join and leave mid-training without killing the run. → Checkpoint relay. Train for a few hours, export a checkpoint, hand it off to someone else to continue. Like a relay race for ML training. → Built on Apple's MLX framework. Native to M1/M2/M3/M4/M5 unified memory. No host-to-device copy overhead. → Local dashboard. Real-time loss curves, peer monitoring, throughput metrics in your browser. Here's the wildest part: An M4 Max with 128GB unified memory can train a 70B parameter model without offloading. An NVIDIA RTX 4090 has 24GB VRAM. Apple Silicon gets ~245-460 GFLOPS per watt. Training on MacBooks costs almost nothing in electricity compared to cloud GPUs. And there are hundreds of millions of Apple Silicon Macs in the world. The math: Traditional DDP: 1 sync per step = 50 GB/s required AirTrain (DiLoCo): 1 sync per 500 steps = 0.1 GB/s required Wi-Fi handles 0.1 GB/s. That's it. That's the breakthrough. They even built a community platform at with live session browsing, checkpoint sharing, and a contributor leaderboard. Training a 124M parameter GPT-2? Instead of renting cloud GPUs at $3/hr, pool three MacBooks in a coffee shop and train for free. MIT licensed. Built in Python. 1 contributor. Early stage but the idea is insane. 100% Open Source. (Link in the comments)

160,201 просмотров • 1 месяц назад

🚨BREAKING: An open-source agentic video production system just dropped. 11 pipelines, 49 tools, and a full product ad produced for $0.69 total. It's called OpenMontage. And it's not a text-to-video tool. It's a full production orchestration system where your AI coding assistant (Claude Code, Cursor, Copilot, Windsurf) becomes the director. Describe what you want in plain language. The agent researches, scripts, generates assets, edits, and renders the final video. Here's what the pipeline actually does: → Live web research first: 15-25+ searches across YouTube, Reddit, news sites before writing a single word of script → 12 video generation providers: Kling, Runway Gen-4, Google Veo 3, MiniMax, plus local GPU options (WAN 2.1, Hunyuan, CogVideo) → 8 image generation providers: FLUX, Google Imagen 4, DALL-E 3, Stable Diffusion locally → 4 TTS providers: ElevenLabs, Google (700+ voices), OpenAI, and Piper offline for free → WhisperX word-level subtitles burned in automatically → Remotion for React-based animated composition with spring physics, transitions, TikTok-style captions → Budget governance: cost estimate before execution, per-action approval above $0.50, hard cap at $10 Here's the wildest part: One product ad. 4 AI-generated images, TTS narration, royalty-free music, word-level subtitles, Remotion data visualizations. Total cost: $0.69. Zero manual asset work. Works with zero API keys too. Piper narrates locally, Pexels/Pixabay provide free stock, Remotion animates everything. No spend required to start. 100% Open Source. AGPL v3 License. (Link in the comments)

🚨BREAKING: An open-source agentic video production system just dropped. 11 pipelines, 49 tools, and a full product ad produced for $0.69 total. It's called OpenMontage. And it's not a text-to-video tool. It's a full production orchestration system where your AI coding assistant (Claude Code, Cursor, Copilot, Windsurf) becomes the director. Describe what you want in plain language. The agent researches, scripts, generates assets, edits, and renders the final video. Here's what the pipeline actually does: → Live web research first: 15-25+ searches across YouTube, Reddit, news sites before writing a single word of script → 12 video generation providers: Kling, Runway Gen-4, Google Veo 3, MiniMax, plus local GPU options (WAN 2.1, Hunyuan, CogVideo) → 8 image generation providers: FLUX, Google Imagen 4, DALL-E 3, Stable Diffusion locally → 4 TTS providers: ElevenLabs, Google (700+ voices), OpenAI, and Piper offline for free → WhisperX word-level subtitles burned in automatically → Remotion for React-based animated composition with spring physics, transitions, TikTok-style captions → Budget governance: cost estimate before execution, per-action approval above $0.50, hard cap at $10 Here's the wildest part: One product ad. 4 AI-generated images, TTS narration, royalty-free music, word-level subtitles, Remotion data visualizations. Total cost: $0.69. Zero manual asset work. Works with zero API keys too. Piper narrates locally, Pexels/Pixabay provide free stock, Remotion animates everything. No spend required to start. 100% Open Source. AGPL v3 License. (Link in the comments)

112,201 просмотров • 1 месяц назад

🚨 BREAKING: 170K+ traders are opening leveraged positions directly from geopolitical alerts, before the headline even hits your timeline. You're still refreshing CNN. Glint built a Telegram bot with an in-house LLM trained on 400+ news and OSINT sources. It tracks conflicts, sanctions, leadership changes, economic shifts and tells you exactly what's moving the S&P 500 before the rest of the market reacts. Then you open a leveraged position on Hyperliquid or place a bet on Polymarket. Same chat. Same second. No CEX. No KYC. No app switching. Non-custodial wallet. It's called Glint Bot. By the time you hear about it on Twitter, their users have already traded it. Watch it in action ↓

🚨 BREAKING: 170K+ traders are opening leveraged positions directly from geopolitical alerts, before the headline even hits your timeline. You're still refreshing CNN. Glint built a Telegram bot with an in-house LLM trained on 400+ news and OSINT sources. It tracks conflicts, sanctions, leadership changes, economic shifts and tells you exactly what's moving the S&P 500 before the rest of the market reacts. Then you open a leveraged position on Hyperliquid or place a bet on Polymarket. Same chat. Same second. No CEX. No KYC. No app switching. Non-custodial wallet. It's called Glint Bot. By the time you hear about it on Twitter, their users have already traded it. Watch it in action ↓

113,181 просмотров • 2 месяцев назад

Finally tried Computer and used it to make a beautiful stock tracker I’ve been wanting for a while. It did it in <10 minutes and pulls in latest share price via API. The fact that I can spin up new apps in a few minutes w/o a terminal is crazy

Finally tried Computer and used it to make a beautiful stock tracker I’ve been wanting for a while. It did it in <10 minutes and pulls in latest share price via API. The fact that I can spin up new apps in a few minutes w/o a terminal is crazy

126,049 просмотров • 3 месяцев назад

Google sent the founder a cease and desist. He was 13. Stanford AI. Taught alongside Andrew Ng. Forbes 30 Under 30. Now: $81M raised. $700M valuation. Reid Hoffman, Steve Wozniak, Superhuman CEO all use it daily.

Google sent the founder a cease and desist. He was 13. Stanford AI. Taught alongside Andrew Ng. Forbes 30 Under 30. Now: $81M raised. $700M valuation. Reid Hoffman, Steve Wozniak, Superhuman CEO all use it daily.

24,015 просмотров • 3 месяцев назад

🚨BREAKING: This guy just dropped an entire Lovable masterclass for free. Full course. Zero fluff. Build real apps 10x faster. I genuinely think this is better than any $500 bootcamp out there.

🚨BREAKING: This guy just dropped an entire Lovable masterclass for free. Full course. Zero fluff. Build real apps 10x faster. I genuinely think this is better than any $500 bootcamp out there.

15,478 просмотров • 2 месяцев назад

1. The Meta-Prompt Copy this into ChatGPT: “You are to act as my prompt engineer. I would like to accomplish: [insert your goal]. Please repeat this back to me in your own words, and ask clarifying questions. Once we confirm, generate the final optimized prompt.” That’s it. This turns ChatGPT into your personal prompt engineer.

1. The Meta-Prompt Copy this into ChatGPT: “You are to act as my prompt engineer. I would like to accomplish: [insert your goal]. Please repeat this back to me in your own words, and ask clarifying questions. Once we confirm, generate the final optimized prompt.” That’s it. This turns ChatGPT into your personal prompt engineer.

21,888 просмотров • 9 месяцев назад

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