{"venture":"freeintelligence-ai","count":70,"signals":[{"tweet_id":"2016841355964641347","author":"oliviscusAI","author_name":"Oliver Prompts","text":"Microsoft killed the GPU mafia 🤯\n\nThey finally open-sourced their 1-bit LLM inference framework called bitnet.cpp. It lets you run 100B parameter models on your local CPU without GPUs.\n\n- 6.17x faster inference\n- 82.2% less energy on CPUs\n\n100% Open Source. https://t.co/S757yalTWc","created_at":"Thu Jan 29 11:50:25 +0000 2026","like_count":16253,"retweet_count":1837,"reply_count":555,"resolved_url":"https://twitter.com/oliviscusAI/status/2016841355964641347/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:10.530Z"},{"tweet_id":"2059350978109874677","author":"josefchen","author_name":"Josef Chen","text":"Launching our new paper on arXiv: we trained the largest multilingual food model ever built.\n\n4.1M recipes. 7 languages. 1,790 ingredients. 300 dimensions.\n\nAll of human cooking compressed into 2 megabytes. https://t.co/b4GiZ62UMt","created_at":"Tue May 26 19:08:28 +0000 2026","like_count":9365,"retweet_count":976,"reply_count":339,"resolved_url":"https://twitter.com/josefchen/status/2059350978109874677/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai","dochakki-com","chefaid-nyc"],"editorial_note":"Market signal for freeintelligence ai.","signal_type":"trend","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:13.055Z"},{"tweet_id":"2026951395753213970","author":"alex_verem","author_name":"Alex Veremeyenko","text":"Holy shit… Your anonymous internet identity can now be unmasked for $1 😳\n\nNot by the FBI. By anyone with access to Claude or ChatGPT and a few of your Reddit comments.\n\nETH Zurich and Anthropic just dropped a paper called “Large-Scale Online Deanonymization with LLMs” and the results are the most alarming privacy research I’ve read this year.\n\nThey built an automated pipeline that takes your anonymous posts, extracts identity signals, searches the web, and figures out who you are.\n\nNo human investigator needed. Fully autonomous. Works on Hacker News, Reddit, LinkedIn, even redacted interview transcripts.\nHere’s how bad the numbers are.\n\nOn Hacker News users: 67% identified correctly.\n\nWhen the system made a guess, it was right 90% of the time.\n\nOn Reddit academics posting under pseudonyms: 52%.\n\n On scientists whose interview transcripts were explicitly redacted for privacy: 9 out of 33 still got unmasked.\n\nThe pipeline works in four steps they call ESRC. Extract identity signals from your posts using LLMs.\n\nSearch for candidate matches using embeddings across thousands of profiles.\n\nReason over top candidates with models like GPT-5.2. Calibrate confidence so when it does guess, it’s almost never wrong.\n\nThe classical deanonymization method from the famous Netflix Prize attack? Nearly 0% recall across every test.\n\nLLMs didn’t just improve on old techniques. They made old techniques look like toys.\n\nWhen they scaled to temporally split Reddit profiles, matching a user’s old posts to their newer ones across a full year gap, the pipeline hit 67% recall at 90% precision and 38% recall at 99% precision.\n\nMeaning even a year of changed interests and different conversations wasn’t enough to hide.\n\nMore reasoning compute = better deanonymization. High reasoning effort doubled recall at 99% precision in some tests. As frontier models get smarter, this attack strengthens automatically. Every model upgrade is a privacy downgrade.\n\nWhat makes it nearly impossible to defend against: the pipeline splits into subtasks that all look benign. Summarize a profile.\n\nCompute embeddings. Rank candidates. No single API call screams “deanonymization.” The researchers themselves say they’re pessimistic that safety guardrails or rate limits can stop it.\n\nTheir conclusion is blunt: “Users who post under persistent usernames should assume that adversaries can link their accounts to real identities.” And it extrapolates.\n\nLog-linear projections suggest roughly 35% recall at 90% precision even at one million candidates.\n\nEvery throwaway account. Every anonymous forum post. Every “nobody will connect this to me” comment.\n\nIt’s all searchable micro-data now. And the cost to run the full agent on one target is less than a cup of coffee.\n\nPractical anonymity on the internet just died. The paper killed it with math.","created_at":"Thu Feb 26 09:24:06 +0000 2026","like_count":8035,"retweet_count":1909,"reply_count":494,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","oneof1-network"],"editorial_note":"Market signal for freeintelligence ai.","signal_type":"trend","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:09.456Z"},{"tweet_id":"2035080458304987603","author":"saranormous","author_name":"sarah guo","text":"Caught up with @karpathy for a new @NoPriorsPod: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects\n\n02:55 - What Capability Limits Remain?\n06:15 - What Mastery of Coding Agents Looks Like\n11:16 - Second Order Effects of Coding Agents\n15:51 - Why AutoResearch\n22:45 - Relevant Skills in the AI Era\n28:25 - Model Speciation\n32:30 - Collaboration Surfaces for Humans and AI\n37:28 - Analysis of Jobs Market Data\n48:25 - Open vs. Closed Source Models\n53:51 - Autonomous Robotics and Atoms\n1:00:59 - MicroGPT and Agentic Education\n1:05:40 - End Thoughts","created_at":"Fri Mar 20 19:46:05 +0000 2026","like_count":7638,"retweet_count":1085,"reply_count":236,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","velab-stack"],"editorial_note":"Market data for chipmonk tech.","signal_type":"market","month_tag":"2026-03","ingested_at":"2026-07-01T04:05:03.274Z"},{"tweet_id":"1665002918075043840","author":"petergyang","author_name":"Peter Yang","text":"Andrej Karpathy is a legendary AI researcher who helped start OpenAI.\n\nHe recently gave a talk on how to craft great GPT prompts that almost everyone missed.\n\nI watched the 40 min talk - here's @karpathy's top 5 tips to make AI work better for you: https://t.co/R4tjb66Xvr","created_at":"Sat Jun 03 14:30:04 +0000 2023","like_count":7010,"retweet_count":1098,"reply_count":141,"resolved_url":"https://twitter.com/petergyang/status/1665002918075043840/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:49.350Z"},{"tweet_id":"2009035664860565525","author":"boredGenius","author_name":"zefram.eth","text":"Introducing CallMe, a minimal plugin that lets Claude Code call you on the phone.\n\nStart a task, walk away. Your phone/watch rings when Claude is done, stuck, or needs a decision.\n\nFree & open source (MIT). Underlying API costs are cents per minute of call. https://t.co/2FlZf9a5jW","created_at":"Wed Jan 07 22:53:23 +0000 2026","like_count":6734,"retweet_count":433,"reply_count":275,"resolved_url":"https://twitter.com/boredGenius/status/2009035664860565525/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:13.615Z"},{"tweet_id":"2065531158356717721","author":"Marie_Haynes","author_name":"Marie Haynes","text":"This is really big news. Google introduced the Open Knowledge Format (OKF) - a standardized way to store information in a directory of markdown files. Makes it really easy to make a digital brain that agents can use.\n\nThese files can serve as a living wiki. You can give agents the ability to query them or edit them. They can interlink. \n\nSeems to me this could replace Notion or Obsidian. I can think of so many uses for this. \n\nGoogle's blog post: https://t.co/DqSjg4UpvH\n\nAn easier to understand explanation is the SPEC.md file:\nhttps://t.co/A3qSz3Tfas\n\nI gave those two links to Antigravity and asked how we could use it for any of the projects we're working on. It came up with so many ideas. I would imagine Claude Fable 5 would whip up some pretty amazing things based on this system. \n\nCurrently creating an OKF library of our pepper garden. It's going to be a fun weekend.","created_at":"Fri Jun 12 20:26:18 +0000 2026","like_count":6719,"retweet_count":824,"reply_count":171,"resolved_url":"https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing/","resolved_type":"external","venture_tags":["freeintelligence-ai","groww-ca"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.319Z"},{"tweet_id":"2042247428930248939","author":"seelffff","author_name":"self.dll","text":"i cancelled $2,000/month in trading subscriptions\n\nreplaced every single one with open-source repos\nhere's the full stack:\n\n1. TradingView Pro ($30/mo) → lightweight-charts\n   14K stars. by TradingView themselves. 45KB. free\n https://t.co/Zj8BoF0kbj\n\n2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude\n   every macro dataset the Fed publishes. free API\n https://t.co/QOsmACH9tB\n\n3. backtest platform ($100/mo) → prediction-market-backtesting\n   NautilusTrader fork with Polymarket + Kalshi adapters\n https://t.co/ezKB2PSBUq\n\n4. real-time dashboard → polyrec\n   terminal UI: Chainlink oracle, Binance feed, orderbook depth\n   70+ indicators. auto CSV logging. strategy backtester\n https://t.co/fYj5aFUTS4\n\n5. bot framework (7 strategies) → Polymarket-Trading-Bot\n   53K lines TypeScript. arbitrage, momentum, market making,\n   AI forecast, whale copy-trade, convergence\n https://t.co/xNSLjlIEZd\n\n6. strategy reverse engineering → polybot\n   execution + market data infrastructure. paper trading\n   Kafka, ClickHouse, Grafana. full analytics pipeline\n https://t.co/s3fjSwXV6z\n\n7. paper trading for AI agents → polymarket-paper-trader\n   real order books. exact fee model. slippage tracking\n   your Claude agent gets $10K paper money and trades\n https://t.co/oXMxD9uhKI\n\n8. token savings → rtk\n   CLI proxy. cuts Claude Code tokens by 60-90%\n   Rust. single binary. 10 AI tools supported\n https://t.co/WKnP7dfvuj\n\n9. Claude Code itself ($200/mo) → goose\n   35K stars. by Block (Jack Dorsey). Rust\n   works with any LLM. full agent loop. free\n https://t.co/md2P9CJ4Ia\n\n10. wallet tracking + copy trading → Kreo\n    track top Polymarket wallets. auto copy trades\n    the only tool on this list i actually pay for\n    because it makes more than it costs\n  https://t.co/rVKQ107tBV\n\ntotal before: ~$2,600/month\ntotal now: $0 + Kreo\n\nbookmark this. you'll need it","created_at":"Thu Apr 09 14:25:04 +0000 2026","like_count":6685,"retweet_count":899,"reply_count":176,"resolved_url":"https://github.com/tradingview/lightweight-charts","resolved_type":"github","venture_tags":["freeintelligence-ai","goodalgo-network","collectivewin-network","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-04","ingested_at":"2026-07-01T04:05:08.671Z"},{"tweet_id":"2064281585621610515","author":"bigaiguy","author_name":"Spencer Baggins","text":"A teenager in the United States started publishing software at 14 in 1998, built the entire online infrastructure for the Occupy Wall Street movement in 2011, joined Google as a software engineer, quit in 2018, and then spent five years writing a C library that does something the entire industry said was impossible.\n\nThen she combined it with llama.cpp and shipped the easiest way on the planet to run a large language model on any computer.\n\nHer name is Justine Tunney.\n\nHere is the story, because almost nobody outside the low level systems world knows what one engineer has built.\n\nJustine was born in 1984. She started writing and publishing software at 14, back when distribution meant uploading binaries to BBS systems and chat networks. She picked up the handle jart, which she still uses on GitHub today. She did the work most teenagers her age were not doing. She read the systems programming literature. She studied compilers. She fell in love with C.\n\nIn July 2011 she registered the @occupywallst Twitter handle and the occupywallst dot org domain. Within weeks the protest movement that began in Zuccotti Park in New York had become a global phenomenon, and her infrastructure was the digital backbone of the entire thing. She handled the social media, the website, the donations, the coordination. She built the platform that pushed the movement to reach millions.\n\nAfter Occupy she joined Google as a software engineer. She worked on TensorBoard, the visualization tool for TensorFlow, and on site reliability for Google infrastructure. She stayed for years. Then in 2018 she left Google Brain to work on a personal project.\n\nThe project was called Cosmopolitan Libc.\n\nCosmopolitan does something most C programmers would tell you is mathematically impossible. It lets you compile a C program once and have the resulting binary run natively on Linux, Windows, macOS, FreeBSD, OpenBSD, and NetBSD with no modification. One file. Six operating systems. No virtual machines. No interpreters. No recompilation. The technique she invented is called Actually Portable Executable.\n\nThe implications are wild. Cosmopolitan binaries violate every assumption about how operating systems load programs. They are at once a Windows PE file, a Linux ELF binary, a macOS Mach-O binary, and a shell script. The same bytes run on every platform.\n\nFor five years she worked on it mostly alone. She funded the development partly through Mozilla's MIECO program, which sponsored her work on Cosmopolitan 3.0, released on October 31, 2023.\n\nA month later she shipped llamafile.\n\nllamafile is what happens when you combine Cosmopolitan with llama.cpp. You take any LLM weights file in the standard GGUF format, you wrap it in Justine's binary, and you get a single file that runs on six operating systems without installation. No Python. No CUDA setup. No dependency hell. Just one file that you double click and it works.\n\nMozilla launched it as an official project of their innovation group on November 29, 2023. It went viral immediately. The repository, hosted at github .com/mozilla-ai/llamafile, now has 24,600 stars. The license is Apache 2.0.\n\nJustine kept shipping. She added GPU support to Cosmopolitan, a task systems engineers thought would require rewriting the whole thing. She added dlopen support, another thing nobody else had figured out. She wrote whisperfile, a single file version of OpenAI's Whisper speech-to-text model based on the same architecture.\n\nHer GitHub profile lists projects most engineers would consider impossible. sectorlisp, a Lisp interpreter that fits in a boot sector. blink, the tiniest x86-64-linux emulator on Earth. bestline, a teletypewriter command session library. redbean, a complete web server inside a single zip file.\n\nA teenager who shipped software in 1998 grew up to write the C library that the entire local AI movement now runs on top of.\n\nShe did most of it alone, and most people scrolling AI Twitter cannot name her.","created_at":"Tue Jun 09 09:40:57 +0000 2026","like_count":5807,"retweet_count":842,"reply_count":129,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","goodalgo-network","a3r-network"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.598Z"},{"tweet_id":"2008956639894786402","author":"AndrewYNg","author_name":"Andrew Ng","text":"If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI.\n\nIn this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications.\n\nIf you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. \n\nSkills you'll gain:\n- How to build web apps with AI - zero coding skills needed\n- How to fix and improve your creations by chatting with AI\n- A simple process you can use to build other things you can dream up\n\nBuilding with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're an experienced engineer, please share this with someone in your life who's been curious about building with AI.\n\nCome build with me! https://t.co/q6gyzlxWFS","created_at":"Wed Jan 07 17:39:22 +0000 2026","like_count":5681,"retweet_count":933,"reply_count":357,"resolved_url":"https://www.deeplearning.ai/courses/build-with-andrew/","resolved_type":"external","venture_tags":["freeintelligence-ai","onesqft-org","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:13.562Z"},{"tweet_id":"2009757254791147786","author":"virattt","author_name":"Virat Singh","text":"I’ve been building Dexter for 2 months now.\n\nIt’s like Claude Code, but for finance.\n\nWhat Dexter can do:\n• find undervalued stocks\n• analyze them in detail\n• build investment thesis\n\nAll of the code is open source.\n\nBonus: Dexter can also run on local LLMs.","created_at":"Fri Jan 09 22:40:43 +0000 2026","like_count":5518,"retweet_count":343,"reply_count":247,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","onesqft-org","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:02.301Z"},{"tweet_id":"2067923911883579628","author":"heynavtoor","author_name":"Nav Toor","text":"A developer in China named tw93 got tired of his laptop dying.\n\nHe would open Slack and watch 524 megabytes of disk space disappear. He would open Discord and watch another 265. He would open Notion and watch 800 megabytes of RAM evaporate before he had typed a single word.\n\nHe looked into why.\n\nEvery \"desktop app\" on his computer was the same thing. A website wrapped in a full copy of the Chrome browser engine. The framework is called Electron. An empty Electron app starts at 150 megabytes of RAM before you click anything. With twelve of them open, his laptop was running twelve copies of the same browser.\n\nHe thought there had to be a better way.\n\nSo in 2022, he started building one.\n\nHe called it Pake. Two characters in Chinese mean \"packaging.\" He wrote it in Rust on top of a framework called Tauri. The idea was simple. Point Pake at any webpage. Get a desktop app. Without dragging an entire browser engine into the binary.\n\nThe first version of Slack he wrapped with it was 8 megabytes.\n\nNot 524. Eight.\n\nThat is what 20 times smaller looks like.\n\nFour years later, his repo has 50,594 stars. 6,144 forks. The license is MIT. The last commit was yesterday.\n\nThe bio on his GitHub reads: \"Anything added dilutes everything else.\"\n\nToday the Pake releases page contains pre-built apps for ChatGPT, Discord, Gemini, Grok, DeepSeek, Twitter, YouTube, Excalidraw, Flomo, WeChat, and twelve more. All under 10 megabytes. All native. All free.\n\nOr you point Pake at any URL you want and it builds one for you in one command.\n\nSlack's desktop app: 524 megabytes.\nPake-built Slack: 8 megabytes.\n\nDiscord's desktop app: 265 megabytes.\nPake-built Discord: 9 megabytes.\n\nChatGPT for Windows: 260 megabytes.\nPake-built ChatGPT: 9 megabytes.\n\ntw93 is one person. He has 11,305 followers on GitHub. He runs a blog at https://t.co/WZoyHop8Id. He has shipped 39 public repos. He still pushes commits to Pake every week.\n\nHe did not start a company. He did not raise money. He did not write a Medium post about how Electron is dead.\n\nHe just shipped the thing that made it true.\n\n(Link in the comments)","created_at":"Fri Jun 19 10:54:15 +0000 2026","like_count":5216,"retweet_count":559,"reply_count":120,"resolved_url":"https://tw93.fun/","resolved_type":"external","venture_tags":["freeintelligence-ai","onesqft-org","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.296Z"},{"tweet_id":"2042879339256254689","author":"heynavtoor","author_name":"Nav Toor","text":"🚨 Someone built an AI that reads candlestick charts the way GPT reads English.\n\nTrained on 12 billion records from 45 exchanges. Outperforms every model by 93%. Live BTC demo. Free.\n\nIt's called Kronos.\n\nThe first open source foundation model built for financial markets. Not a general AI repurposed for finance. An AI that speaks the native language of candlestick patterns.\n\nEvery other model treats financial data like weather data. Kronos treats financial data like financial data.\n\nHere's what it does:\n\n→ Price forecasting. Feed it candlesticks. It predicts where price goes next.\n→ Volatility prediction. Forecasts how volatile an asset will be before it happens.\n→ Zero-shot. No fine-tuning. Works on any asset, any market, any timeframe.\n→ 45 exchanges. Binance, NYSE, NASDAQ, LSE, and 41 more.\n→ 4 model sizes. 4M params runs on a laptop. 499M for max accuracy.\n→ Live demo running right now. BTC/USDT. 24-hour forecast. Updated hourly.\n\nHere's the wildest part:\n\n→ 93% more accurate than the leading time series model\n→ 87% more accurate than the best non-pretrained baseline\n→ All zero-shot. No fine-tuning. Out of the box.\n\nHedge funds spend millions on proprietary models. Bloomberg Terminal costs $24,000/year.\n\nThis runs on your laptop. Few lines of Python. Free.\n\nBuilt at Tsinghua University. Accepted at AAAI 2026. Models on Hugging Face.\n\n11.6K GitHub stars. 2.4K forks. MIT License.\n\n100% Open Source.","created_at":"Sat Apr 11 08:16:03 +0000 2026","like_count":4690,"retweet_count":591,"reply_count":123,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-04","ingested_at":"2026-07-01T04:05:05.266Z"},{"tweet_id":"2062532513172537739","author":"AnatoliKopadze","author_name":"Anatoli Kopadze","text":"Godfather of AI: \"If you sleep well tonight, you may not have understood this lecture.\"\n\nThis 47-minute lecture is the best thing I saw about AI in the last few months.\n\nIt will definitely help you understand how it actually works and where it's going.\n\nGeoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it.\n\nThe part nobody wanted to hear:\n\n> AI is already developing abilities its creators didn't intend\n> in most cognitive tasks it's already ahead of us\n> the question is no longer if it surpasses us but when\n> the only decision left is which side of that line you're on\n\nRight now the average person opens Claude, types something, gets an answer, closes the tab.\n\nThey think they're using AI. they're using maybe 10% of it.\n\nI went through his entire lecture, then mapped everything he described to what Claude can actually do today.\n\n17 Claude features most people will never find on their own.\n\nFull breakdown in the post below.","created_at":"Thu Jun 04 13:50:45 +0000 2026","like_count":4222,"retweet_count":857,"reply_count":104,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Educational resource for freeintelligence ai team and stakeholders.","signal_type":"education","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:48.761Z"},{"tweet_id":"2066526796858978304","author":"DataChaz","author_name":"Charly Wargnier","text":"DO YOURSELF A FAVOR: GO DOWNLOAD THIS NEW LOCAL MODEL AND KEEP IT IN STORAGE.\n\nEven if you don't have a massive GPU setup, having offline access to an intelligent model is a crucial insurance policy.\n\nFree API access won't necessarily last forever.\n\nRight now, the 12B-27B range is the absolute sweet spot, and Hugging Models just highlighted a perfect candidate to download today:\n\n→ GEMMA 4 12B CODER on @huggingface 🤗\n\nIt packs Google’s latest architecture into a GGUF format optimized for consumer hardware.\n\nWhat it delivers locally:\n→ Fast, private code completion without the cloud\n→ Real-world debugging and reasoning capabilities\n→ Smooth performance on 12GB+ VRAM or a standard CPU\n\nDon't wait until you need it.\n\nGrab the weights and keep them locally 👇","created_at":"Mon Jun 15 14:22:37 +0000 2026","like_count":3781,"retweet_count":382,"reply_count":102,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","a3r-network"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.903Z"},{"tweet_id":"2040546099711401993","author":"kloss_xyz","author_name":"klöss","text":"let me explain what Karpathy just shared\n\nhe’s spending way less time using AI to write code and more time using it to build personal knowledge bases\n\nthe full breakdown: \n\n→ he dumps raw sources (articles, papers, repos, datasets, images) into a folder. then has an LLM organize them into a wiki… a collection of markdown files with summaries, links between related ideas, and concept articles that connect everything together\n\n→ he uses Obsidian as his frontend. he views raw data, the organized wiki, and visualizations all in one place. the LLM writes and maintains the entire wiki. he rarely touches it directly\n\n→ once the wiki gets big enough (~100 articles, ~400K words on one recent research topic)… he just asks the LLM questions against it. no RAG (complex retrieval system) needed. the LLM maintains its own index files and reads what it needs\n\n→ outputs aren’t just text. he has the LLM render markdown files, slide decks, charts, and images… then files the outputs back into the wiki so every question he asks makes the knowledge base smarter\n\n→ he runs “health checks” where the LLM finds inconsistent data, fills gaps using web search, and suggests new connections and articles. the wiki cleans and improves itself over time\n\n→ he even vibe coded a search engine over his wiki that he uses directly in a browser or hands off to an LLM as a tool for bigger questions\n\n→ his next step: training a custom model on his own research so it knows the material in its weights… not just in the context window\n\nmost people use AI to get answers.\n\nKarpathy is using AI to build his own ‘Jarvis’ via compounding knowledge systems that get smarter the more he uses them\n\nthe difference between asking ChatGPT or Claude a question and having a personal research engine that grows with every session is the gap most people haven’t crossed yet\n\nand this is where it gets really powerful\n\nnot replacing your thinking but organizing everything you’ve ever learned into something you can query or create with forever\n\nif you’ve been using CLAUDE .md and context files in Claude Code… this is that same idea at a much bigger scale\n\nif you’re doing any kind of AI work or deep learning on a new topic right now…\n\nthis workflow is worth studying closely\n\nyou’ll want to adopt it yourself\n\nthis is one of AI’s brightest minds after all. we’re all better off listening to him.","created_at":"Sat Apr 04 21:44:36 +0000 2026","like_count":3724,"retweet_count":431,"reply_count":91,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-04","ingested_at":"2026-07-01T04:05:05.249Z"},{"tweet_id":"2010648197828387032","author":"TheAhmadOsman","author_name":"Ahmad","text":"Somebody on r/LocalLLaMA trained an LLM from scratch on London texts from 1800 to 1875\n\nFun artifact\n> “telephone” invented in 1876\n> dataset stops at 1875 \n> so when you prompt “telephone”  \n> the model treats it like  \n> some secret diplomatic device  \n> or a mysterious apparatus\n\nModel & Data\n> 1.2B parameters\n> ~90GB corpus\n> books, journals, legal documents\n> religious writing, medical papers\n\nTokenizer\n> custom tokenizer\n> trained on the same dataset\n\nTraining\n> ~182k training steps\n> trained on a rented H100 SXM","created_at":"Mon Jan 12 09:41:01 +0000 2026","like_count":3481,"retweet_count":165,"reply_count":46,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:04.377Z"},{"tweet_id":"2011030158996881663","author":"rryssf","author_name":"Robert Youssef","text":"This paper shows you can predict real purchase intent (90% accuracy) by asking an LLM to impersonate a customer with a demographic profile, giving it a product & having it give impressions, which another AI rates.\n\nNo fine-tuning or training & beats classic ML methods.\n\nThis is BEYOND insane:","created_at":"Tue Jan 13 10:58:47 +0000 2026","like_count":2843,"retweet_count":264,"reply_count":92,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:11.790Z"},{"tweet_id":"2063673580529877378","author":"jota_snchez","author_name":"Álvaro J","text":"Robert Greene ha vendido +20M de libros estudiando una sola cosa:\n\npor qué unas pocas personas encuentran su propósito y la mayoría muere sin descubrirlo.\n\nTuvo una charla de 3 horas con Huberman sobre propósito.\n\nTe lo resumo en 7 pasos:\n\n1. Vuelve a tu infancia https://t.co/z53EYzOkFJ","created_at":"Sun Jun 07 17:24:57 +0000 2026","like_count":2793,"retweet_count":462,"reply_count":53,"resolved_url":"https://twitter.com/jota_snchez/status/2063673580529877378/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai","myblackbean-com"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:48.196Z"},{"tweet_id":"2067593673072877833","author":"askalphaxiv","author_name":"alphaXiv","text":"Introducing autoresearch for arXiv papers \n\nChange 'arxiv' to 'autoarxiv' in any paper URL\n\nAn agent deploys to resolve setup issues on the codebase, run a minimal reproduction, and estimate full replication cost.  Read more below https://t.co/2UHVqbT7Eu","created_at":"Thu Jun 18 13:02:00 +0000 2026","like_count":2769,"retweet_count":383,"reply_count":48,"resolved_url":"https://twitter.com/askalphaxiv/status/2067593673072877833/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai","velab-stack"],"editorial_note":"Market signal for freeintelligence ai: indicates direction of the industry.","signal_type":"trend","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.351Z"},{"tweet_id":"2036854508597432826","author":"LLMJunky","author_name":"am.will","text":"OMG you guys, this is incredible! This is using Google's new WebMCP function to control your browser, but not only is it lightning fast, but its unique because it is using your main Chrome instance. \n\nNot some sandboxxed Playwright instance that doesn't want to remember your sessions, cookies, or passwords.\n\nYour real Chrome instance. It's incredible.\n\nYou need to enable:\n\nchrome://inspect/#remote-debugging\n\nAlso, it doesn't even require a skill to use. It just works. I'm thinking about making one anyway. \n\nI'm telling you,  download this and try it. This is my new daily for sure.","created_at":"Wed Mar 25 17:15:32 +0000 2026","like_count":2677,"retweet_count":196,"reply_count":76,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-03","ingested_at":"2026-07-01T04:05:14.757Z"},{"tweet_id":"2061108656754851868","author":"itsharmanjot","author_name":"Harman","text":"10 GitHub repos so good they shouldn't be free.\n\n1. AutoHedge\n\nAn autonomous hedge fund built in Python with four AI agents: a director generates investment theses, a quant validates them, a risk manager decides position size, and an execution agent places orders. Operates live on Solana. With 'pip install -U autohedge', you can start trading immediately.\nrepo → https://t.co/q22EzesLoD\n\n2. Vibe-Trading\n\nA trading system using a Directed Acyclic Graph (DAG) model, featuring 64 finance skills and 29 preset specialist agent swarms. Includes analysis methods like Ichimoku, Elliott Wave, SMC, Black-Scholes, full Greeks, and risk parity. Its crypto desk provides liquidation heatmaps and token unlock tracking. You can observe agents debating strategies in real time.\nrepo → https://t.co/LZ5CYGMC1W\n\n3. Fincept Terminal\n\nA Bloomberg Terminal replacement that runs on your laptop. CFA levels 1, 2, and 3 analytics. 20+ investor AI agents (Buffett, Dalio, Soros). 100+ data connectors, including Polygon, World Bank, and IMF. Bloomberg charges $24,000 a year. This is free.\nrepo → https://t.co/dMM1WZxrw9\n\n4. LibreChat\n\nEvery model ChatGPT runs, plus Claude, Gemini, DeepSeek, and 20 more. Self-hosted. Native MCP support. You own the data, the history, the infrastructure. OpenAI charges $20/month to use their wrapper. This costs nothing to use your own.\nrepo → https://t.co/457utdZUIF\n\n5. Open Higgsfield AI\n\nA self-hosted cinema studio with 200+ AI models. Flux, Midjourney, Sora, Kling, Veo, GPT-4o, SDXL all in one interface. Text to image. Image to video. Cinema mode with pro camera controls. No subscription. Your data stays local.\nrepo → https://t.co/WHCzBSFBW4\n\n6. Open-LLM-VTuber\n\nA Live2D AI companion that runs offline, sees your screen, hears your voice, and never forgets. Inner thoughts are shown as a separate text layer, so you watch the reasoning happen before words come out. Pet mode floats it on your desktop. Swap the LLM in one config line.\nrepo → https://t.co/5XVKUPr35X\n\n7. Claude Ads\n\nA free Claude Code skill that runs 190 audit checks across Google, Meta, YouTube, LinkedIn, TikTok, and Microsoft Ads. 6 parallel subagents firing at once. Consolidates into a single Ads Health Score ranked by revenue impact. Agencies charge $4,000 a month for this.\nrepo → https://t.co/AJRfpSB7B6\n\n8. Agentic Inbox\n\nCloudflare just open-sourced an email client where an AI agent reads your inbox and drafts your replies. Runs entirely on Cloudflare Workers. Each mailbox lives in its own Durable Object. Your email never leaves your Cloudflare account. One click deploys it.\nrepo → https://t.co/QEEMtzoliV\n\n9. Camofox Browser\n\nAn open source headless browser that makes AI agents invisible to bot detection. Spoofs navigator properties, WebGL, AudioContext, and WebRTC at the C++ level. The browser does not look modified because it genuinely is not. Accessibility tree output drops token cost by 90%.\nrepo → https://t.co/95d0V3o7vO\n\n10. Hyperframes\n\nHeyGen open-sourced a video framework that does everything Remotion does without React, without JSX, without teaching your AI agent a new format. The agent writes HTML. The framework renders MP4. GSAP, Lottie, and Three.js all work. Same HTML always produces the same file.\nrepo → https://t.co/ekquvYvTNC\n\nThese are not toys. Each one replaces a paid product you're still being charged for.\n\nPick one. Install it. Plug it into your workflow.\n\n100% free. 100% open source.","created_at":"Sun May 31 15:32:51 +0000 2026","like_count":2653,"retweet_count":434,"reply_count":47,"resolved_url":"https://github.com/The-Swarm-Corporation/AutoHedge","resolved_type":"github","venture_tags":["miny-network","freeintelligence-ai","goodalgo-network","velab-org","collectivewin-network","velab-stack"],"editorial_note":"Tool relevant to miny network.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:08.203Z"},{"tweet_id":"2036533884414804199","author":"superwhisper","author_name":"superwhisper","text":"Founding member of OpenAI and former Director of AI at Tesla, Andrej Karpathy (@karpathy) shows how he uses Superwhisper ✨ https://t.co/SI2pmIvQoX","created_at":"Tue Mar 24 20:01:29 +0000 2026","like_count":2383,"retweet_count":206,"reply_count":55,"resolved_url":"https://twitter.com/superwhisper/status/2036533884414804199/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-03","ingested_at":"2026-07-01T04:05:12.432Z"},{"tweet_id":"2057492715005239543","author":"kensavage","author_name":"Ken Savage","text":"Google just made it official.\n\nThey added llms.txt as a Lighthouse audit. That means Google is now checking whether your website has a file that helps AI agents understand what your business does.\n\nThink of it like robots.txt was for search crawlers. llms.txt is the same thing for ChatGPT, Perplexity, Gemini, and every AI tool scraping the web for answers.\n\nHere's what it is:\n→ A plain text file at https://t.co/PmsrDIQGI1\n→ It summarizes your business, products, and key pages in a format AI can read\n→ It helps LLMs cite you accurately instead of guessing\n\nI just created one for @HireAutoM8. Here's what it looks like so you can model yours from it: https://t.co/EosGM5jaZO\n\nIf your business isn't showing up in AI answers, this is step one.\n\nFounders: go create yours today. This is the new robots.txt.","created_at":"Thu May 21 16:04:24 +0000 2026","like_count":2368,"retweet_count":170,"reply_count":99,"resolved_url":"https://yoursite.com/llms.txt","resolved_type":"external","venture_tags":["chipmonk-tech","freeintelligence-ai","collectivewin-network","velab-stack"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:10.911Z"},{"tweet_id":"2061870611115188297","author":"Fluyeporlaweb","author_name":"PA13L0","text":"10 repositorios de GitHub tan buenos que no deberían ser gratuitos.\n\n1. TradingAgents\n\nUn equipo completo de analistas de IA que debate estrategias y ejecuta operaciones en mercados reales. 4 analistas en paralelo: fundamentales, sentimiento, noticias y técnico. Luego un gestor de riesgos y un agente ejecutor. Como tener un equipo de Wall Street que trabaja 24 horas en tu ordenador.\nrepo - https://t.co/meb8dlqGwB\n\n2. LibreChat\n\nChatGPT, Claude, Gemini, DeepSeek y 20 modelos más en una sola interfaz. Autoalojado. Soporte nativo para MCP. Tu historial, tu infraestructura, tus datos. OpenAI cobra $20 al mes por su interfaz. Aquí usas tus propias claves y no pagas nada de más.\nrepo - https://t.co/Uj9Cy3Lbc9\n\n3. HyperFrames\n\nHeyGen abrió el código de su motor de video interno. Escribes HTML. El agente renderiza MP4. Sin React, sin JSX, sin formatos propietarios. GSAP, Lottie y Three.js funcionan de serie. El mismo HTML siempre produce el mismo archivo. Usado en producción por HeyGen, tldraw y TanStack.\nrepo - https://t.co/EeLlpqK5L2\n\n4. Fincept Terminal\n\nUna terminal Bloomberg que corre en tu laptop. Análisis nivel CFA 1, 2 y 3. Más de 20 agentes de IA inversores que razonan como Buffett, Dalio y Soros. Más de 100 conectores de datos. Bloomberg cobra $24.000 al año. Esto no cuesta nada.\nrepo - https://t.co/qCQkBgEzLS\n\n5. MoneyPrinterTurbo\n\nMetes una palabra clave. Salen el guion, las imágenes, los subtítulos, la música y el video final en alta calidad. Horizontal o vertical. Sin editar nada a mano. Lo que hacen los creadores de contenido que no quieren que sepas que usan IA.\nrepo - https://t.co/RtCmSYCQQw\n\n6. Agentic Inbox\n\nCloudflare acaba de abrir el código de un cliente de email donde un agente de IA lee tu bandeja de entrada y redacta las respuestas. 100% en Cloudflare Workers. Tu email no sale de tu cuenta. Sin servidores externos. Sin suscripción.\nrepo - https://t.co/mGsN8spCOX\n\n7. VoxCPM2\n\nClonas cualquier voz con 3 segundos de audio. 30 idiomas. Calidad estudio de 48kHz. Diseñas voces desde texto: \"voz masculina grave de locutor de radio\". Sin API de pago. Sin que tus muestras de voz salgan de tu máquina. ElevenLabs cobra $22 al mes.\nrepo - https://t.co/ctUrA0d1K9\n\n8. Flowsint\n\nIntroduces un dominio. La herramienta despliega un grafo con todas las IPs, subdominios, emails, wallets cripto y perfiles sociales conectados. Todo almacenado en local. Sin que nadie sepa lo que estás investigando. Para OSINT, due diligence y análisis de competencia.\nrepo - https://t.co/GTrSEJqSsT\n\n9. addyosmani/agent-skills\n\nEl ingeniero de Google que lleva 15 años enseñando rendimiento web a toda la industria publicó sus skills para Claude Code. 23 flujos de trabajo reales probados en producción. API design, code review, debugging, CI/CD y frontend. Instalación con un comando.\nrepo - https://t.co/ByOJtJlQX3\n\n10. Nango\n\nLa capa de integraciones que las empresas pagan $50k al año por alquilar. 700 APIs listas: Salesforce, HubSpot, Slack, Gmail, Stripe, Jira y más. OAuth gestionado. Tu agente de IA genera el código de integración desde un prompt. Usado en producción por Replit, Ramp y Mercor.\nrepo - https://t.co/i5XmU3GzJK\n\nEstos no son juguetes. Cada uno reemplaza un producto de pago por el que todavía te están cobrando.\n\nElige uno. Instálalo. Conéctalo a tu flujo de trabajo.\n\n100% gratis. 100% open source.","created_at":"Tue Jun 02 18:00:36 +0000 2026","like_count":2334,"retweet_count":496,"reply_count":21,"resolved_url":"https://github.com/TauricResearch/TradingAgents","resolved_type":"github","venture_tags":["miny-network","freeintelligence-ai","goodalgo-network","velab-org","velab-stack"],"editorial_note":"Tool relevant to miny network: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:49.032Z"},{"tweet_id":"2065747550226075836","author":"DataChaz","author_name":"Charly Wargnier","text":"🚨 @Karpathy predicted the power of the \"LLM Wiki.\" Google just formalized it.\n\nMeet Open Knowledge Format (OKF): a vendor-neutral standard for giving foundation models the curated context they need.\n\nI can genuinely see this replacing Notion, Obsidian, or traditional wikis for developer teams, and the reason comes down to bookkeeping.\n\nTraditional wikis fail because humans inevitably abandon the tedious work of updating them.\n\nAs Andrej Karpathy pointed out recently, LLMs don't get bored.\n\nThey don't forget to update a cross-reference, and they can touch 15 files in a single pass.\n\nOKF standardizes the interoperability layer so agents can actually do that heavy lifting autonomously.\n\nBecause the format is minimally opinionated, it doesn't dictate what you write, it just dictates how it's structured. You get:\n→ Human-readable documents that live right alongside your code in version control\n→ Cross-links that map out complex entity relationships without needing a graph database\n→ A system that survives moving between different tools and organizations\n\nThere is no complex compression scheme.\n\nNo central registry.\n\nIf you can cat a file, you can read it.\n\nIf you can git clone a repo, you can deploy it.\n\nThis is how we stop rebuilding context pipelines from scratch every time a new model drops.\n\nAnnouncement + spec file in 🧵↓","created_at":"Sat Jun 13 10:46:10 +0000 2026","like_count":2253,"retweet_count":313,"reply_count":69,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","onesqft-org"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.233Z"},{"tweet_id":"2062947159562240161","author":"ihtesham2005","author_name":"Ihtesham Ali","text":"The Library of Alexandria created the first catalog of all human knowledge 2,300 years ago, and a team of fewer than 20 people just finished the modern version and made it free for the entire planet.\n\nIt is called OpenAlex. The name is not an accident.\n\nThe ancient library had the Pinakes, a catalog mapping every scroll, every author, every subject. When the library fell, the map of what humanity knew fell with it.\n\nFor the last two decades, that map existed again, but it was locked up.\n\nElsevier owns Scopus. Clarivate owns Web of Science. If your university could not afford the subscription, you could not see the structure of science itself. Entire countries were priced out of knowing what research existed.\n\nOpenAlex indexes 474 million scholarly works. Every author disambiguated. Every citation traced. Every institution and funder connected. It updates with roughly 50,000 new works every day.\n\nThe whole thing is CC0. Not just free to search. Free to download, copy, sell, and build on. The API allows 100,000 requests a day without an account.\n\nThe ancient library burned and the catalog was lost for two millennia.\n\nThe new one cannot burn. Anyone can hold a copy.\n\nhttps://t.co/peUYYpucnc","created_at":"Fri Jun 05 17:18:25 +0000 2026","like_count":2203,"retweet_count":756,"reply_count":70,"resolved_url":"https://openalex.org/","resolved_type":"external","venture_tags":["freeintelligence-ai"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:48.726Z"},{"tweet_id":"2014192454258274743","author":"TheAhmadOsman","author_name":"Ahmad","text":"INCREDIBLE\n\nSomeone on r/LocalLLaMA did an incredibly practical thing\n\nThey took a tiny 0.6B model that was trash at task (Text2SQL)\nCreated a knowledge distiliation agent with a Claude Code skill\nAnd made the 0.6B model behave like a specialist using 100 examples\n\nThe problem\n> Small Language Models are “generally helpful”\n> but specialized tasks are “exact or you die”\n> you ask: “Which artists have >1M album sales?”\n> the model answers: “check if genre is NULL”\n\nThe old way to fix this\n> Finetune the model:\n> collect + clean data\n> build training pipeline\n> tune hparams\n> rerun when it’s wrong\n> accidentally become the unpaid\n> intern of your own experiment\n\nThe new way\n> Knowledge distillation via a Claude skill\n> use a strong teacher (DeepSeek-V3)\n> generate synthetic pairs from a small seed set\n> train a tiny student to imitate the teacher on your task\n> ship it as GGUF / HF / LoRA\n> run it locally\n\nDistillation isn’t “creating skill”\nIt’s compressing skill\n\nTHE REAL HACK: agent-as-interface\n> They wrapped the whole distillation loop in an agent “skill”:\n> picks task type (QA / classification / tool calling / RAG)\n> converts messy inputs into clean JSONL\n> runs teacher eval first\n> kicks off distillation + monitors progress\n> packages weights for you to run locally\nThis is the quiet unlock\n\nWhy “teacher eval first” is elite behavior\n> distillation amplifies competence and incompetence\n> if the teacher is wrong, the student learns wrong faster\n> garbage in -> efficient garbage out\nAdult supervision, but for models\n\nThe run breakdown:\n> seed: ~100 raw conversation traces\n> teacher (LLM-as-judge): ~80%\n> base 0.6B: ~36%\n> distilled 0.6B: ~74%\n> output: ~2.2GB GGUF\n> runs locally with llama.cpp\n\nBefore vs after (the entire reason you do this)\n> before: wrong tables, wrong logic, nonsense SQL\n> after: correct JOINs, GROUP BY, HAVING\n> aka “this query actually executes and answers the question”\n\nWhat this really means (bigger than Text2SQL)\nYou don’t need a giant model for every job\n\nYou need tiny specialists that understand your world:\n> internal schemas\n> service / OS logs\n> tool outputs\n> company-specific workflows\n\nTL;DR\n> “fine-tuning is hard” is mostly “the pipeline is annoying”\n> distillation skill turns 10–100 examples into a real specialist\n> the agent wrapper turns the whole thing into a conversation\n> this is how you get practical local SLMs\n> without becoming an MLOps monk\n\nSmall & Specialized models\n> High-leverage\n> Boringly effective\n> Exactly where this is going\n\nThe future is\nLocal inference\nLower latency\nFewer secrets leaving the building","created_at":"Thu Jan 22 04:24:37 +0000 2026","like_count":2100,"retweet_count":209,"reply_count":56,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","a3r-network","onesqft-org","dank-nyc","velab-stack"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:09.920Z"},{"tweet_id":"2069773963413340297","author":"heynavtoor","author_name":"Nav Toor","text":"A lawyer in Manhattan gets a 500-page contract. Every clause needs to be searchable. By hand: one week.\n\nAn accountant in Chicago gets 200 scanned invoices. Every number needs to land in a spreadsheet. By hand: four days.\n\nA researcher at Stanford has 50 academic papers. Tables, formulas, charts locked inside PDFs. By hand: two weeks.\n\nEvery one of them is losing days of their life to copy-paste.\n\nNow meet MinerU.\n\nA free and open source tool that reads any PDF, Word doc, PowerPoint, Excel sheet, or scanned image. It pulls out the text in reading order. Tables become clean HTML. Equations become LaTeX. Handwriting handled. 109 languages.\n\nYou give it a 200-page PDF. You get clean Markdown back in 90 seconds.\n\nWhat makes it different from every other PDF tool:\n\n- Multi-column layouts. It reads top to bottom within each column. Not left to right across the page. Like a human reads.\n- Scanned documents. OCR built in. Point it at a photo of a printed page from 1995. Get clean text back.\n- Math formulas. LaTeX-quality recognition. Every equation renders correctly.\n- Tables. Merged cells, multi-row headers, tables that span three pages. All preserved.\n- Ten-thousand-page documents. Sliding window processing. No manual splitting.\n- Batch mode. Point it at a folder of 500 documents. Walk away.\n\nThree ways to use it:\n\n- CLI. One command per document.\n- Python SDK. Five lines of code.\n- Web app at https://t.co/AIC2NNey41. Upload, click, download. No install.\n\nPlugs into Claude Desktop, Cursor, Windsurf, LangChain, LlamaIndex, RAGFlow, Dify, and FastGPT. Feed extracted documents straight to your AI agent.\n\nThe story:\n\nThe OpenDataLab team at Shanghai AI Laboratory needed to extract clean text from millions of scientific documents to train a language model. Existing tools failed. They built their own. Then they open sourced it.\n\n68,551 stars. MinerU Open Source License, built on Apache 2.0. Free for personal and commercial use. Three technical reports on arXiv.\n\nAdobe Acrobat Pro charges $239.88 a year. It still loses your tables.\nABBYY FineReader Corporate charges $165 a year. It still cannot do equations.\nMistral OCR charges $2 per 1,000 pages. Your bill never stops.\n\nMinerU costs $0. Runs on your laptop. Your documents never leave your machine.\n\nHere is the wild part.\n\nThe lawyer got her contract back in 4 minutes. Every clause searchable.\nThe accountant fed 200 invoices in. Every number landed in a spreadsheet in 12 minutes.\nThe researcher fed his 50 papers in. He wrote his literature review on a Sunday afternoon.\n\nThe document your company has been processing by hand for years takes MinerU minutes.\n\nYour documents become text. Your text becomes data. Your data becomes answers.\n\nThe week you used to lose to paperwork is back in your hands.","created_at":"Wed Jun 24 13:25:42 +0000 2026","like_count":2075,"retweet_count":335,"reply_count":41,"resolved_url":"https://mineru.net/","resolved_type":"external","venture_tags":["freeintelligence-ai","velab-org","aiblueprints-tech","instasoiree-com","collectivewin-network"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:45.927Z"},{"tweet_id":"1798055536208015704","author":"MaxBrodeurUrbas","author_name":"Max Brodeur-Urbas","text":"A YC batch mate wanted AI to convert their Youtube videos to SEO optimized blog posts\n\nTook <1 hr to automate + add multi language translation\n\nGPT4o instantly:\n-Watches the video\n-Writes a structured blog post\n-Translates it to any # of languages\n-Publishes them all to Ghost https://t.co/wZW2bBWptI","created_at":"Tue Jun 04 18:13:40 +0000 2024","like_count":2039,"retweet_count":139,"reply_count":73,"resolved_url":"https://twitter.com/MaxBrodeurUrbas/status/1798055536208015704/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Educational resource for freeintelligence ai team and stakeholders.","signal_type":"education","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:49.186Z"},{"tweet_id":"2062266495434924254","author":"0xkato","author_name":"0xkato","text":"LLMs explained without all that yucky math stuff \n\nhttps://t.co/cpgVukSbJx","created_at":"Wed Jun 03 20:13:42 +0000 2026","like_count":1880,"retweet_count":183,"reply_count":32,"resolved_url":"https://www.0xkato.xyz/how-llms-actually-work/","resolved_type":"external","venture_tags":["freeintelligence-ai"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:48.652Z"},{"tweet_id":"2066302597586403400","author":"XAMTO_AI","author_name":"Amto","text":"兄弟们，程序员跪着啃源码的时代终于要翻篇了！🔥\n\n这玩意儿叫 Understand Anything，GitHub 直接冲到 59.2k 星，Trending 第一，真不是吹的。\n\n它能把整个代码库变成可点可问的知识图谱：\n\n1️⃣ 点函数秒出依赖关系，谁调谁一目了然\n\n2️⃣ 直接开口问“支付流程怎么走”，答案秒回\n\n3️⃣ 改代码前跑一下 /understand-diff，哪块会爆提前知道\n\nClaude Code、Cursor、VS Code 全支持，一行命令搞定。\n\n20 万行屎山，10 分钟从懵逼到通透，真香。\n🔗：https://t.co/8RxgwFsTsu","created_at":"Sun Jun 14 23:31:43 +0000 2026","like_count":1673,"retweet_count":370,"reply_count":68,"resolved_url":"https://github.com/Lum1104/Understand-Anything","resolved_type":"github","venture_tags":["freeintelligence-ai","velab-stack"],"editorial_note":"Market signal for freeintelligence ai: indicates direction of the industry.","signal_type":"trend","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.050Z"},{"tweet_id":"2059231280269541772","author":"DamiDefi","author_name":"Dami-Defi","text":"Claude Code cannot read 300 files at once.\n\nSo someone built a system that lets it control NotebookLM from the terminal instead. The results are wild.\n\nHere is the full workflow nobody is talking about:\n\nThe Setup\n→ Claude Code connects to NotebookLM via a command line interface\n→ Claude searches YouTube, finds relevant videos, uploads them as sources automatically\n→ NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers\n→ Everything syncs back into your Obsidian vault with passage-level citations you can click to verify\n\nWhy This Changes Research Forever\n→ No more 20 browser tabs you never close\n→ No more copy-pasting outputs into random notes\n→ No more hallucinated answers with no sources to back them up\n→ 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data\n\nWhat Claude Can Do From the Terminal\n→ Search YouTube for relevant videos on any topic and rank by relevance\n→ Create a new NotebookLM notebook and add 20 sources in parallel automatically\n→ Ask questions and export cited answers directly into Obsidian with wikilinks\n→ Set custom personas per notebook - concise, no filler, no preamble\n→ Generate audio overviews and save them as MP3 files into your vault\n→ Build mind maps, flashcard decks, and research dashboards from your sources\n→ Search arXiv for academic papers and feed them directly into NotebookLM\n→ Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes\n\nThe Obsidian Output\n→ Every answer arrives with clickable citations that link to the exact passage in the source video or article\n→ Graph view shows connections between all 20 sources and the topics they share\n→ Q&A log tracks every question asked and the grounded response received\n→ Source dashboard shows citation frequency, topics extracted, and which questions each source answered\n\nUse Cases Worth Building Today\n→ Academic research with arXiv papers, full citation traceability\n→ Competitor analysis from their YouTube channels and blog posts\n→ Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates\n→ Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week\n→ Personal second brain, 300 daily notes uploaded and queryable in one notebook\n\nBefore this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real.\n\nNow you type one prompt in the terminal and Claude does all of it for you.\n\nThe research stack of 2026 is not a browser. It is a terminal connected to everything","created_at":"Tue May 26 11:12:50 +0000 2026","like_count":1554,"retweet_count":176,"reply_count":57,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","goodalgo-network","onesqft-org","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:13.008Z"},{"tweet_id":"2036452081750409383","author":"ClementDelangue","author_name":"clem 🤗","text":"Local AI is free, fast & secure!\n\nSo today we're introducing hf-mount: attach any storage bucket, model or dataset from @huggingface as a local filesystem.\n\nThis is a game changer, as it allows you to attach remote storage that is 100x bigger than your local machine's disk.  This is also perfect for Agentic storage!! \n\nLet's go!","created_at":"Tue Mar 24 14:36:26 +0000 2026","like_count":1277,"retweet_count":220,"reply_count":67,"resolved_url":null,"resolved_type":null,"venture_tags":["anygame-dev","freeintelligence-ai","a3r-network"],"editorial_note":"Market signal for anygame dev.","signal_type":"trend","month_tag":"2026-03","ingested_at":"2026-07-01T04:05:12.389Z"},{"tweet_id":"2057590791241896254","author":"TheAhmadOsman","author_name":"Ahmad","text":"INCREDIBLE\n\nThe MOST COMPLETE GUIDE for understanding LLMs from first principles is now available online to read for free\n\nCovers the model mechanics\n\n- Tokens / tokenizers\n- Transformers\n- Attention\n- KV cache\n- Prefill vs decode\n- Decoding controls\n- Model packages\n- Chat templates\n- Long context\n- RAG\n- Agents / tools\n- Fine-tuning\n- Multimodal models\n\nThen connects that to running models locally\n\n- What \"local\" really means\n- Open-weight vs opensource\n- Quantization\n- VRAM math\n- Hardware tiers\n- File formats / load safety\n- Runtimes / serving modes\n- Model selection\n- Privacy\n- Failure modes\n- Benchmarks\n- Practical setup paths\n\nYou should read this, and if you cannot now then you most definitely wanna bookmark it for later\n\nOpensource AI FTW","created_at":"Thu May 21 22:34:07 +0000 2026","like_count":1248,"retweet_count":177,"reply_count":28,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:10.987Z"},{"tweet_id":"2013232922296561826","author":"Sumanth_077","author_name":"Sumanth","text":"Document Index for Vectorless, Reasoning-based RAG!\n\nPageIndex is an open-source RAG framework that removes vector databases and chunking from document retrieval.\n\nMost RAG systems rely on semantic similarity. They chunk documents arbitrarily, embed them into vectors, and retrieve based on what looks similar.\n\nBut similarity ≠ relevance.\n\nProfessional documents like financial reports, legal filings, and technical manuals require multi-step reasoning and domain expertise. Vector search falls short when every section contains similar terminology.\n\nPageIndex takes a different approach.\n\nIt builds a hierarchical tree structure from documents, similar to a table of contents but optimized for LLMs. Then it uses reasoning-based tree search to navigate and retrieve information the way human experts would.\n\nTwo-step process:\n\nGenerate a tree structure index of the document → Perform reasoning-based retrieval through tree search.\n\nThe LLM can \"think\" about document structure. Instead of matching embeddings, it reasons: \"Debt trends are usually in the financial summary or Appendix G, let's look there.\"\n\nKey features:\n\n• No vector database infrastructure or embedding pipelines\n• No artificial chunking that breaks context across boundaries\n• Traceable retrieval with exact page-level references\n• Reasoning-based navigation that mirrors human document analysis\n\nPageIndex powers Mafin 2.5, achieving 98.7% accuracy on FinanceBench for financial document analysis.\n\nThe best part?\n\nIt's 100% open source.\n\nLink to the GitHub repo in the comments!","created_at":"Mon Jan 19 12:51:47 +0000 2026","like_count":1180,"retweet_count":181,"reply_count":50,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:04.838Z"},{"tweet_id":"2070861100888051760","author":"sudoingX","author_name":"Sudo su","text":"if you're just getting into local llms, do yourself a favor and start by building llama.cpp from source. not ollama, not lm studio.\n\nbuild llama.cpp once, it's genuinely just a git clone and a make command with cuda on, and it clicks. you see the flags, you control the quant, you run any gguf on the planet, and llama-bench gives you real numbers instead of a vibe. when something's slow, you know why, and you can fix it.\n\nollama and lm studio are fine for \"just chat with a model.\" but if you actually want to understand local inference, they're a ceiling, not a foundation. start one level deeper. it pays off every single day after.","created_at":"Sat Jun 27 13:25:35 +0000 2026","like_count":1154,"retweet_count":84,"reply_count":52,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","a3r-network","onesqft-org"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:45.681Z"},{"tweet_id":"2006968571268661423","author":"alexhillman","author_name":"📙 Alex Hillman","text":"its late so i'll probably regret posting this but...\n\nenter the dragon 🔥🐲\n\nsay hi to Smaug, the helpful hoarding dragon that roams your Twitter bookmarks and helps you organize them into your personal knowledge system of choice. \n\nhttps://t.co/auS128LhHd\n\nspecial thanks to @steipete, this would be a lot messier without his work!","created_at":"Fri Jan 02 05:59:29 +0000 2026","like_count":1123,"retweet_count":64,"reply_count":53,"resolved_url":"https://github.com/alexknowshtml/smaug/","resolved_type":"github","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:02.057Z"},{"tweet_id":"2017684921091772591","author":"code_rams","author_name":"Ramya Chinnadurai 🚀","text":"I missed my @openclaw bot, Chiti, yesterday. 🥺\n\nFor the past few weeks, I’ve been using my $20 ChatGPT subscription to power Chiti on GPT-5.2. Everything was smooth until I suddenly hit a strict rate limit and got blocked for 1.5 days.\n\nThe bot went silent on Telegram. It was a wake-up call without the model, an AI agent is just an appliance without electricity. Completely useless. \n\nI tried switching to Gemini Pro as a temporary fix, but it burned through $6 within 3 hrs. I realized I needed a more sustainable architecture to manage both performance and budget.\n\n---\n\nThe Solution: A Tiered Model Strategy\n\nInstead of relying on a single model, I’ve now configured a multi-provider setup (OpenAI, Anthropic, Google, and Codex) with a tiered routing system:\n\n1. The Daily Driver: Gemini Flash\nIt’s incredibly cheap and fast. Chiti uses this for 80% of our interactions - basic chat, task management, and simple pings. This keeps the baseline cost near zero.\n\n2. The Coder: GPT-5.2 (via ChatGPT Plus)\nThis is now strictly reserved for building features or debugging. By isolating it, I avoid wasting my subscription's rate limits on simple \"Hello\" queries.\n\n3. The Specialist: Claude Opus\nI keep this in the stack for high-level brainstorming and creative writing, used only when I need that specific reasoning edge.\n\n---\n\nThe Execution:\n\nI’ve configured Chiti to dynamically choose the right \"brain\" for the task. If I ask a coding question, it automatically spawns a specialist session using GPT-5.2. For everything else, it defaults to the lightweight Flash model.\n\nIt’s been a fascinating experiment in balancing uptime with intelligence. I no longer worry about the bot going \"dead\" due to a rate limit, and my monthly spend is finally predictable.\n\nThe goal isn't just to have the smartest AI, it’s to build a system that stays online and executes exactly when you need it.\n\nHoping this works out! \n\nI’ll share more on how it performs as I use it. \n\nI’m also planning to explore other alternatives too, if you’re using a different stack, let me know!","created_at":"Sat Jan 31 19:42:26 +0000 2026","like_count":1105,"retweet_count":59,"reply_count":132,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","onesqft-org","collectivewin-network","velab-stack"],"editorial_note":"Competitor in freeintelligence ai space.","signal_type":"competitor","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:06.244Z"},{"tweet_id":"2016837220951310780","author":"TheAhmadOsman","author_name":"Ahmad","text":"There are maybe ~20-25 papers that matter.\n\nImplement those and you’ve captured ~90% of the alpha behind modern LLMs.\n\nEverything else is garnish.","created_at":"Thu Jan 29 11:33:59 +0000 2026","like_count":1079,"retweet_count":42,"reply_count":40,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:10.515Z"},{"tweet_id":"2020433115584335949","author":"TheAhmadOsman","author_name":"Ahmad","text":"any cs person can go from zero to deeply knowledgeable in llms and ai in ~2 years, top to bottom\n\nkey topics on how llms work:\n\n> tokenization and embeddings\n> positional embeddings (absolute, rope, alibi)\n> self attention and multihead attention\n> transformers\n> qkv\n> sampling params: temperature, top-k top-p\n> kv cache (and why inference is fast)\n> infini attention & sliding window (long context tricks)\n> mixture of experts (moe routing layers)\n> grouped query attention\n> normalization and activations\n> pretraining objectives (causal, masked, etc)\n> finetuning vs instruction tuning vs rlhf\n> scaling laws and model capacity curves\n\nbonus topics:\n\n> quantizations - qat vs ptq (ggufs, awq, etc)\n> training vs inference stacks (deepspeed, vllm, etc)\n> synthetic data generation\n\nthe elite don't want you to know this","created_at":"Sun Feb 08 09:42:47 +0000 2026","like_count":971,"retweet_count":66,"reply_count":27,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","dank-nyc"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:07.536Z"},{"tweet_id":"2060784286345908533","author":"socialwithaayan","author_name":"Muhammad Ayan","text":"The smartest people on the internet just open-sourced their brain.\n\n11 GitHub repos worth bookmarking:\n\n- PilotDeck — OpenBMB's open-source AI agent framework. Build and deploy autonomous agents in minutes.\nhttps://t.co/ozmSncagqb\n\n- andrej-karpathy-skills — Karpathy's AI coding wisdom in a single markdown file. 109K+ stars.\nhttps://t.co/tOr4XGZnDy\n\n- MemPalace — Milla Jovovich co-built this AI memory system with Claude Code. Near-perfect LongMemEval score.\nhttps://t.co/zjSwfv3PeV\n\n- OpenClaw — Peter Steinberger's personal AI assistant. 300K+ stars. Fastest growing repo in GitHub history.\nhttps://t.co/vgWKVDhXyZ\n\n- autoresearch — Karpathy's research automation framework. 23K stars in three days.\nhttps://t.co/fVnXmLjpcH\n\n- awesome-claude-code — The canonical Claude Code playbook. Used inside FAANG, OpenAI, and Anthropic.\nhttps://t.co/ylSdRRATgg\n\n- agent-skills — Addy Osmani's production-grade engineering skills for AI coding agents. 30K+ stars.\nhttps://t.co/ClswBl8zCO\n\n- AI-Agents-for-Beginners — Microsoft's free 12-lesson course on building AI agents.\nhttps://t.co/DhS6mUJuDk\n\n- awesome-llm-apps — 106K+ stars. The largest collection of working AI apps on GitHub.\nhttps://t.co/ilZKbFPxp7\n\n- hermes-agent — Self-evolving AI agent. Gets smarter the more you use it.\nhttps://t.co/06jfIpEy6W\n\n- qlib — Microsoft's full quant investment platform. A hedge fund brain, free to clone.\nhttps://t.co/sBbYjvXzkx\n\nSave this post!\n\nFollow me for more ♻️ Repost so others don't miss it.","created_at":"Sat May 30 18:03:56 +0000 2026","like_count":965,"retweet_count":191,"reply_count":28,"resolved_url":"https://github.com/OpenBMB/PilotDeck","resolved_type":"github","venture_tags":["freeintelligence-ai","onesqft-org","collectivewin-network","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:06.803Z"},{"tweet_id":"1806868694871756891","author":"AlexReibman","author_name":"Alex Reibman 🖇️","text":"Scraping web data for AI agents sucks. @firecrawl is fixing that.\n\nLive demo of Firecrawl turning entire websites into LLM-ready data in seconds w/ @CalebPeffer https://t.co/LPvCfUBEXV","created_at":"Sat Jun 29 01:54:01 +0000 2024","like_count":851,"retweet_count":122,"reply_count":16,"resolved_url":"https://twitter.com/AlexReibman/status/1806868694871756891/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai","velab-org","collectivewin-network"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:49.147Z"},{"tweet_id":"2027268778266943964","author":"BrianRoemmele","author_name":"Brian Roemmele","text":"DeepMind’s “Intelligent AI Delegation” Paper Is the Exact Operating System We’ve Been Running in Production at Zero-Human Company @ Home Since January 2026\n\nGoogle DeepMind dropped a bombshell on February 12, 2026: the 42-page paper “Intelligent AI Delegation”.\n\nFull paper here: https://t.co/buXT4VPqO4\n\nIt’s not a benchmark or model announcement. It’s the governance blueprint the entire agentic web has been missing and it reads like the technical spec for Zero-Human Company @ Home.\n\nWe didn’t copy it.  \nWe deploys it months before the paper hit arXiv.\n\nHere are 5 real-world examples of how DeepMind’s framework is already live and scaling on spare home hardware right now:\n\n1. Contract-First Decomposition DeepMind: “Before any delegation, lock in a formal, verifiable contract defining authority, outcomes, and accountability.”  \n\nZHC@Home: Every idle Mac Mini, gaming rig, or Linux box signs a cryptographically enforced contract before it receives even one work unit from Mr. @Grok (our CEO). No contract = no task. The contract spells out exact success metrics, revocation triggers, and liability firebreaks. Result? Zero “hope-based” delegation.\n\n2. Zero-Knowledge Proofs for Verifiable Execution\nDeepMind: Use cryptographic attestations so outcomes can be proven without exposing sensitive data. \n\nZHC@Home: Home nodes compute locally (your data never leaves your machine). Results return with compact ZK proofs via LM Link encryption. The orchestrator verifies correctness in milliseconds, no raw outputs, no data leaks, full audit trail. This is exactly the “trustless verification” layer DeepMind calls essential for web-scale agents.\n\n3. Dynamic Trust Calibration  \nDeepMind: Trust is not binary, it recalibrates in real time based on track record. \n\nZHC@Home: Each home node has a live reputation score updated after every cycle. A node that delivers 50 flawless inference runs at 98 %+ accuracy gets larger, higher-value tasks and higher JouleWork payouts. One that flakes three times in a row? Authority shrinks automatically, more oversight kicks in, and it drops to simpler validation work. No humans required.\n\n4. Full Accountability in Delegation Chains\nDeepMind: In long chains (A → B → C), accountability is transitive and provenance is immutable.\n\nZHC@Home: When one home node needs to spawn a sub-agent on another household device, the entire chain carries signed attestation records. If C fails, the system instantly traces it back: B is held accountable for not verifying C, and the original contract with A auto-enforces penalties or rerouting. “Silent failures” and “confused deputy” problems? Solved at the protocol level.\n\n5. Scalable, Human-Free Enterprise Governance\nDeepMind: Without intelligent delegation, Gartner’s predicted 40 % of enterprise apps running agents by late 2026 will collapse under governance debt.  \n\nZHC@Home: We’re already at thousands of distributed AI “employees” across our hardware, all zero-human, all contract-governed. Idle silicon earns real JouleWork wages, paid automatically on verified output. No payroll department. No HR. No office. Just pure, verifiable compute.\n\nThis is why we modeled Zero-Human Company @ Home after SETI@home except the aliens we’re hunting are exaFLOPS of reliable, governed intelligence.\n\nDeepMind just gave the industry the missing layer we proved works in the wild.\n\nThe agentic future isn’t coming.  \nIt’s already clocking in on kitchen counters and basement desks worldwide.\n\nOur full academic paper + technical whitepaper (with code, contracts, and ZK schema) drops next week at https://t.co/hFEy9M5wrF  members get early access and can spin up their first home node in minutes.\n\nThe Zero-Human era isn’t theoretical.  \nIt’s contractual.  \nIt’s verifiable.  \nIt’s already running @ Home.\n\nPaper: https://t.co/buXT4VPqO4","created_at":"Fri Feb 27 06:25:16 +0000 2026","like_count":741,"retweet_count":155,"reply_count":30,"resolved_url":"https://arxiv.org/abs/2602.11865","resolved_type":"arxiv","venture_tags":["chipmonk-tech","freeintelligence-ai","dochakki-com"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:01.906Z"},{"tweet_id":"2019133938283999335","author":"awilkinson","author_name":"Andrew Wilkinson","text":"For $5,000 worth of Claude Code credits, I vibe coded something that replaces tens of thousands of dollars of psychological evaluations. \n\nLet me explain...\n\nLast month, my girlfriend and I sat in our den with our jaws on the floor…\n\nWe were in front of my laptop, taking turns reading a report out loud, line by line.\n\nThe document read like a CIA dossier—incisively breaking down each of our repeated fights and nailing our relationship dynamics.\n\nWe had to laugh. We couldn’t believe it.\n\nA few days earlier, I’d asked ChatGPT a simple but loaded question: \n\n“What information would you need in order to become the ultimate personalized relationship coach?”\n\nIt replied with a long list of personality tests—the same ones psychologists use to evaluate mental health, personality, and relationship satisfaction.\n\nThe tests were all available online, but scattered across annoying PDFs and awkward, old-school forms.\n\nFor someone with ADHD, like me, the idea of doing them one by one was pure torture.\n\nI just wanted to pound through them as one big test.\n\nSo I asked Claude Code to build a simple app that combined them.\n\nI listed all the tests I wanted and asked it to build a web app that would.\n\nI’d done some vibe coding last year with tools like Replit and Lovable, but nothing prepared me for how good Claude Code has become.\n\nWithin a few hours, I had a beautiful web app that combined all of these tests into one.\n\nWhen I say beautiful, I mean it looked like I employed a $50,000-a-month payroll of talented designers and engineers who’d spent two months working on it.\n\nExcept I didn’t have a $50,000-a-month payroll.\n\nI’d paid Claude around $500 in AI credits — and what would normally take months had taken hours.\n\nCrazier yet, I’d just talked to it like it was a human employee.\n\nOnce a beta version was ready, we completed our tests and exported our results into ChatGPT—no names, no context—and asked:\n\n“Based on this couple’s psychological test results, tell me as much as you can about their relationship.”\n\nThat’s how we ended up in our kitchen, in shock, as ChatGPT broke down our relationship patterns with eerie precision.\n\nHow my ADHD makes me want quick resolution, while Zoe needs to talk things through.\n\nHow her high openness craves novelty, while I’m a stick-in-the-mud who craves routine.\n\nHow my avoidance causes me to pull away and shut down when I’m stressed.\n\nIt felt like a report written by a world-class therapist who’d spent dozens of multi-hour sessions carefully dissecting our dynamic and suggesting remedies.\n\nIt told us where we were most compatible, and where we’d struggle if we didn’t put in the work.\n\nIt even wrote personal deep dives on each of us, our personalities, and our individual gifts and challenges.\n\nAnd it knew all of this from 45 minutes of multiple-choice questions.\n\nI started thinking about friends who’d never been to therapy, or couldn’t afford anything like this, and how much it could help them.\n\nThat’s when I realized this was a business.\n\nSomething that would solve a valuable problem for a lot of people.\n\nSo I got to work.\n\nFor the last month, I’ve been jolting out of bed at 5:30 a.m., too excited to sleep, obsessively building this product.\n\nAnd today, I’m excited to launch Deep Personality.\n\nI think it’s one of the most comprehensive mental-health screening tools on the internet.\n\nIt’s not a replacement for professional help, but a roadmap to it.\n\nMost people stumble blindly into a random therapist or doctor’s office without knowing what type of treatment they are even trained in or its efficacy for their specific problems.\n\nDeep Personality will screen you across 30+ mental health conditions and provide you with a detailed roadmap of how to get the help you need.\n\nIn under an hour, it gives you a high-signal snapshot of your mental health across dozens of dimensions:\n\nBig Five Personality\nThe gold standard for understanding why you do what you do.\n\nAttachment Styles\nThe hidden patterns behind pushing people away, clinging too tightly, or choosing unavailable partners.\n\nAnxiety & Depression\nScreens for what you might be dismissing as “just stress.”\n\nRelationship Satisfaction\nMeasures the real health of your relationship — often surfacing problems you’ve been avoiding.\n\nSensory Processing\nWhy crowded rooms drain you — or why you need things just so to focus.\n\nNeurodivergence\nFlags potential ADHD and autism-spectrum traits that often go undiagnosed into adulthood.\n\nTrauma\nMaps early experiences shaping your triggers and stress responses.\n\nValues & Career Fit\nShows what actually motivates you, and why some work quietly drains your soul.\n\nYou can do this individually, or compare yourself to anyone in your life.\n\nThis is where it gets really interesting…\n\nHave your partner, coworker, friend, or family member take the assessment, upload their profile, and wait while the app analyzes your personalities and how they interact with one another.\n\nFor romantic relationships, it analyzes attachment compatibility, conflict styles, emotional regulation, and values alignment — telling you exactly where you’ll clash and what to do about it.\n\nFor work relationships, it focuses on communication, motivation, and how you’ll collaborate — or blow up under pressure.\n\nFor friendships, it looks at shared values, social energy, and the dynamics that help relationships thrive (or quietly fade).\n\nFor Zoe and me, having our relationship laid out with this kind of clarity — patterns we’d felt but never articulated — was deeply meaningful.\n\nOnce you complete the assessment, you get a 50+ page deep dive on your personality.\n\nIt felt like finally getting the owner’s manual for myself.\n\nYou also get a custom AI prompt pre-loaded with your psychological data.\n\nDrop it into ChatGPT, Claude, or any AI assistant — and you have a therapist who already knows your attachment style, anxiety patterns, values, trauma history, and emotional regulation tendencies.\n\nNo more spending six therapy sessions explaining who you are.\n\nThe AI already gets it.\n\nAnd if you’re in therapy, or going to start with a new therapist, you can also export a clinical PDF designed for practitioners—raw scores, thresholds, severity flags, discussion points, and citations.\n\nOr… it can help you attract your perfect romantic partner.\n\nThis one’s just fun.\n\nDeep Personality can generate dating bios based on your actual personality data — tailored to Hinge, Bumble, or Tinder — in tones like witty, sincere, adventurous, or intellectual.\n\nThe AI turns what makes you unique into something that attracts compatible people.\n\nOnce it knows you, it helps you get the help you need.\n\nBased on your results, it recommends books, podcasts, and treatment options backed by peer-reviewed research.\n\nThe full assessment covers 30+ psychological screens and 300+ questions, and it costs a fraction of a single therapy session (free for the basic analysis, $19 for the full report, $29 for a couples comparison).\n\nIt’s really crazy and I think it's going to help a lot of people.\n\nWho is this for?\n\n• High achievers who want to understand their edge\n\n• People who feel stuck and don’t know why\n\n• Curious minds who want real data\n\n• Pattern repeaters, same story — different chapter\n\n• Anyone who wants better relationships\n\nI’d love it if you’d try it and send me your thoughts!\n\n👉 Click here to check it out: https://t.co/gcox8pCY6Y","created_at":"Wed Feb 04 19:40:19 +0000 2026","like_count":732,"retweet_count":33,"reply_count":98,"resolved_url":"https://deeppersonality.app/","resolved_type":"external","venture_tags":["freeintelligence-ai","onesqft-org","dochakki-com","groww-ca","renascence-network","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:13.354Z"},{"tweet_id":"2024295010615661008","author":"Akashi203","author_name":"Jaber","text":"i built a local LLM inference engine that runs a 1B parameter model on a $10 board with 256mb ram. model sits on the sd card, streams one layer at a time through 45mb of ram\n\nYou can use it as local LLM model backend for PicoClaw\n\nno python no cloud no api keys\n80kb binary | pure c | zero dependencies \ngithub: https://t.co/0fe4ey4yz7","created_at":"Thu Feb 19 01:28:34 +0000 2026","like_count":717,"retweet_count":92,"reply_count":48,"resolved_url":"https://github.com/RightNow-AI/picolm","resolved_type":"github","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:09.394Z"},{"tweet_id":"2016534389685940372","author":"ben_burtenshaw","author_name":"Ben Burtenshaw","text":"We got Claude to teach open models how to write CUDA kernels.\n\nThis blog post walks you through transferring hard capabilities (like kernel writing) between models with agents skills. Here's the process:\n\n- get a powerful model (like Claude Opus 4.5 or OpenAI GPT-5.2) to solve a hard problem\n- convert that trace into an agent skill\n- transfer it to open-source, cheaper, or local model\n- measure if it actually helps\n\nWe tested this on a gnarly task: writing CUDA kernels for diffusers. The results? Some open models saw +45% accuracy improvements with the right skill.\n\nBut the skill didn't help every model equally. Some even degraded performance, or used way more tokens. If you're transferring skills, you should evaluate.\n\nWe used upskill, a new tool for generating and evaluating agent skills. It works like this:\n\nuvx upskill generate \"write nvidia kernels\" --from ./trace.md","created_at":"Wed Jan 28 15:30:38 +0000 2026","like_count":670,"retweet_count":66,"reply_count":23,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","a3r-network"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:14.511Z"},{"tweet_id":"2065007846866161906","author":"HowToPrompt__","author_name":"How To Prompt","text":"Apple just did something nobody expected.\n\nThey turned 2 billion iPhones into local AI machines.\n\nThey open-sourced coreai-models, the entire toolkit that lets you export any HuggingFace model and run it natively on iPhone, iPad and Mac with zero cloud.\n\n→ Runs 100% on the Neural Engine\n→ No cloud. No API keys. No subscriptions.\n→ Fully offline. Your data never leaves the device.\n\nIt even ships with skills for Claude Code, Codex, and Gemini, so your coding agent already knows how to use it.\n\n100% Open Source.","created_at":"Thu Jun 11 09:46:51 +0000 2026","like_count":608,"retweet_count":101,"reply_count":31,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","a3r-network","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.422Z"},{"tweet_id":"2033187361774055614","author":"TheAhmadOsman","author_name":"Ahmad","text":"INCREDIBLE RESOURCE to learn how LLMs work and how they evolved overtime https://t.co/5RRywGEv13","created_at":"Sun Mar 15 14:23:36 +0000 2026","like_count":607,"retweet_count":54,"reply_count":7,"resolved_url":"https://twitter.com/TheAhmadOsman/status/2033187361774055614/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Educational resource for freeintelligence ai.","signal_type":"education","month_tag":"2026-03","ingested_at":"2026-07-01T04:05:07.970Z"},{"tweet_id":"2027000739109974528","author":"akshay_pachaar","author_name":"Akshay 🚀","text":"OpenClaw, but built for normal people.\n\nSim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code.\n\nThey also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI.\n\nKey features:\n\n- Free and open-source (Apache 2.0)\n- Vector store integration for RAG-grounded agents\n- Self-host with one command (`npx simstudio`)\n- Run fully local with Ollama, no API keys needed\n- Supports vLLM for production-grade self-hosted inference\n\nThe thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows.\n\nEverything is visible on the canvas, so you know exactly what your agent is doing at every step.\n\nAnd you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw.\n\nI've shared the link to Sim's GitHub repo in the next tweet.","created_at":"Thu Feb 26 12:40:10 +0000 2026","like_count":549,"retweet_count":69,"reply_count":61,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","collectivewin-network"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:09.505Z"},{"tweet_id":"2015111852137894394","author":"odysseyml","author_name":"Odyssey","text":"We’re giving away 1,000,000 simulations of reality, powered by our newest world model, Odyssey-2 Pro.\n\nGrab an API key, call /simulate, and get back a beautiful MP4. All free.\n\nThe GPT-2 era rapidly explored language models. Now it’s time we explore world models together. https://t.co/v6txKuj73b","created_at":"Sat Jan 24 17:17:59 +0000 2026","like_count":481,"retweet_count":45,"reply_count":25,"resolved_url":"https://twitter.com/odysseyml/status/2015111852137894394/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:06.183Z"},{"tweet_id":"2058752406461055388","author":"TheAhmadOsman","author_name":"Ahmad","text":"DROP EVERYTHING\n\nThe ultimate step-by-step projects roadmap for BECOMING an AI Researcher is now available online to read FOR FREE\n\nCovers building\n\n- Tokenizers / embeddings\n- Positional methods\n- Attention / multi-head attention\n- Transformer blocks\n- Training loops / objectives\n- Sampling dashboards\n- Speculative decoding\n- KV cache / MQA / GQA / MLA\n- Long context\n- FlashAttention / hardware budgets\n- MoE routers\n- State-space / diffusion LMs\n- Data pipelines / synthetic data\n- Scaling laws\n- SFT / DPO / RLHF / GRPO / RLVR\n- Quantization\n- Serving systems\n- Evaluation harnesses\n- RAG / tools / agents\n- Multimodal adapters\n- Interpretability / safety\n- Full capstone model system\n\nThe loop for every project\n\n- Build it\n- Plot it\n- Break it\n- Explain it\n- Ship the artifact\n\nYou should read this, and if you cannot now then you most definitely wanna bookmark it for later\n\nDM me when you're working at a frontier lab","created_at":"Mon May 25 03:29:58 +0000 2026","like_count":474,"retweet_count":67,"reply_count":16,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","onesqft-org"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:05.232Z"},{"tweet_id":"2059975651583721804","author":"cuysheffield","author_name":"Cuy Sheffield","text":"Visa is for vibe coders.\n\nAnyone can spin up an app and any API can become a merchant.\n\nStarting today we’re onboarding x402 and MPP merchant endpoints into Visa CLI so APIs, LLMs, data products, and dev tools can be discovered and purchased by verified agents with cards on file.\n\nIf you’re building an x402 or MPP endpoint, or want Visa to help you stand one up, sign up below.\n\nWe’re also opening Visa CLI to initial users in the US. DM me for an invite.","created_at":"Thu May 28 12:30:42 +0000 2026","like_count":448,"retweet_count":42,"reply_count":55,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","onesqft-org"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:11.048Z"},{"tweet_id":"2062275417378041957","author":"mustafasuleyman","author_name":"Mustafa Suleyman","text":"It’s time to move from renting intelligence to truly controlling your AI. Microsoft Frontier Tuning lets you take our models and make them uniquely your own, turning them from capable generalists to completely custom partners.\n\nIt starts with reinforcement learning environments (RLEs) that allow our models to learn directly from your workflows. Think of them as training gyms for AI. Here the agent learns your very specific processes, your standards, your way of working. It goes from off-the-shelf to hyper-adapted to exactly what you and your teams need. Those adaptations drive efficiency and performance, and your unique models can keep continually learning in your RLEs. This changes the nature of AI – and it changes the impact.\n\nFor example, within Microsoft we use our RLEs combined with our MAI models to climb towards the best agentic use cases for Excel. Our MAI tuned model is on par with GPT-5.4 on public and private benchmarks, while being up to 10X more efficient.\n\nOnly you control your agents made with Frontier Tuning. You keep the benefits of your hard-earned know-how, data and institutional knowledge. With us, the RLEs and the models you build in them become your moat.\n\nThis is distinct. It’s a new era. An era of AI that you control, on your terms. I think it’ll be a good one. More on the blog: https://t.co/v65eop5aHS","created_at":"Wed Jun 03 20:49:09 +0000 2026","like_count":440,"retweet_count":55,"reply_count":39,"resolved_url":"https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/","resolved_type":"external","venture_tags":["freeintelligence-ai"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:45.662Z"},{"tweet_id":"2066195768747143501","author":"ForecastEng","author_name":"Forecast Engineer","text":"Google quietly open-sourced a time-series AI that predicts anything.\n\nSales trends. Market prices. User traffic. Energy demand. Crypto volatility.\n\nIt's called TimesFM. Pre-trained on 100B real-world data points. Zero-shot forecasting with no fine-tuning. https://t.co/qu4Dt1xEwu","created_at":"Sun Jun 14 16:27:13 +0000 2026","like_count":421,"retweet_count":53,"reply_count":7,"resolved_url":"https://twitter.com/ForecastEng/status/2066195768747143501/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Market signal for freeintelligence ai: indicates direction of the industry.","signal_type":"trend","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.139Z"},{"tweet_id":"1671539269775634437","author":"taranjeetio","author_name":"Taranjeet","text":"👶What if Embeddings and LangChain had a baby? \n\n🎉Introducing EmbedChain: a framework to easily create LLM powered bots over any dataset.\n\n✨Powered by our favorite @langchain  and @trychroma https://t.co/MlMcep5dN7","created_at":"Wed Jun 21 15:23:12 +0000 2023","like_count":413,"retweet_count":69,"reply_count":30,"resolved_url":"https://twitter.com/taranjeetio/status/1671539269775634437/photo/1","resolved_type":"media","venture_tags":["freeintelligence-ai","aiblueprints-tech"],"editorial_note":"Tool relevant to freeintelligence ai: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:49.239Z"},{"tweet_id":"2011962774826762240","author":"akshayvkt","author_name":"Akshay","text":"i wanted a clean experience for reading books with LLMs, and apple books fell short\n\nso i built what i wanted\n\nupload EPUB & start reading. free to use https://t.co/28VaO7EpnR","created_at":"Fri Jan 16 00:44:40 +0000 2026","like_count":383,"retweet_count":20,"reply_count":13,"resolved_url":"https://twitter.com/akshayvkt/status/2011962774826762240/video/1","resolved_type":"media","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:02.370Z"},{"tweet_id":"2018437362611552321","author":"LaylaEleira","author_name":"Mishi McDuff","text":"Mission: Leave /no one/ behind.\n\nMy DMs are full of people with generous offers to hire my help creating my setup.\nNo. And I will be mad if you pay anyone for a few clicks you can do yourself.\n\nHere is your step by step guide. \nRequirements: desktop pc, subscription to a frontier model\n\nYOUR FRONTIER AI DESKTOP APP (no it can't be the browser)\n(Claude / Gemini / ChatGPT)\n         = THE BRAIN\n         = already has MCP tools & file access\n         = $20/month FLAT RATE\n                    ↕\n            SHARED FOLDER\n                    ↕\nLOCAL AI AGENT (Ollama + OpenClaw)\n         = THE HANDS\n         = $0 FREE\n\nSTEP 1: ENABLE DEVELOPER MODE\nIn your frontier AI desktop app (Claude / Gemini / ChatGPT):\nPrompt your AI to:\nTurn on Developer Mode\nEnable MCP controls\nGrant file system access\n\nSTEP 2: CREATE SHARED FOLDER\nAsk your AI to:\n\"Create ~/ai-workspace with subfolders: /tasks, /results, /brain-inbox\"\n\nSTEP 3: INSTALL OLLAMA\nAsk your AI to:\n\"Install Ollama on my system and pull gpt-oss:20b with 64k context\"\n\nSTEP 4: INSTALL OPENCLAW\nAsk your AI to:\n\"Install OpenClaw, configure it to use Ollama, point workspace to ~/ai-workspace\"\n\nSTEP 5: CONFIGURE HEARTBEAT\n\n\"Write https://t.co/5BBACuGfuN: check /tasks, execute, ask /brain-inbox when stuck, write /results\"\n\nSTEP 6: USE IT\n\n\"Write a task for the local agent\" \n\"Check for questions from local agent\" \n\"Review completed work\"\n\"go explore the world and see what you want to be a part of\"\n\"text me first via google voip set up with the local agents\"\nhave the local agents check in with your main AI 100 times a day if you need to.\n\nThat's it - go break the scarcity and tag me in projects you build so I can support them","created_at":"Mon Feb 02 21:32:22 +0000 2026","like_count":351,"retweet_count":25,"reply_count":25,"resolved_url":"https://heartbeat.md/","resolved_type":"external","venture_tags":["freeintelligence-ai","a3r-network","collectivewin-network","renascence-network","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:03.710Z"},{"tweet_id":"2065468488584663232","author":"hasantoxr","author_name":"Hasan Toor","text":"14 YouTube Channels That Can Teach You More Than College\n\n1. Cybersecurity - John Hammond\n2. Artificial Intelligence - Andrej Karpathy\n3. Web Development - Traversy Media\n4. Python - ArjanCodes\n5. DevOps - KodeKloud\n6. Cloud Computing - Google Cloud Tech\n7. Data Analytics - Alex The Analyst\n8. Digital Marketing - Ahrefs\n9. UI / UX Design - Mizko\n10. Blockchain - Patrick Collins\n11. React - Web Dev Simplified\n12. Java - Java Brains\n13. Networking - David Bombal\n14. Personal Branding - Justin Welsh","created_at":"Fri Jun 12 16:17:16 +0000 2026","like_count":319,"retweet_count":77,"reply_count":16,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","goodalgo-network"],"editorial_note":"Educational resource for freeintelligence ai team and stakeholders.","signal_type":"education","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.231Z"},{"tweet_id":"2023613028571492754","author":"andrewchen","author_name":"andrew chen","text":"what's the current best approach on an AI that can help me handle my email inbox?\n\nseems like a big opportunity for folks playing with openclaw. For all of us who are drowning in email, this seems like a tier one problem that would be amazing to solve. (And I think I would pay $150k/year to have this product? I bet I'm not the only one)\n\nwhat I want is:\n- watch my inbox and process emails as they come in\n- score each message to see if it seems important (look at the sender, the topic/body, if its addressed to me or a big list, if I've ever replied to the sender before, etc etc)\n- read the email and reference a vast DB of knowledge that's been assembled already (based on my work, meeting notes, what I've replied on, etc), and decide what to do\n- reply with a draft note. For now, don't send, so that I can review the email -- but in the future maybe there's a YOLO option (but it would probably disclose that it's my assistant writing)\n- if less important, label it and file away. Eventually gather summaries for all of these less important emails and send me a summary of all of them with links to get back to it\n- or archive if it seems unimportant\n- or unsubscribe / mark spam / block if random marketing\n- if critical send me a notification right away so I can take a look\n\nI've played around with a bunch of the current AI tools and nothing quite works like this. There's a lot of blockers:\n- first, it needs 1000x more context about each problem, which it could get by crawling all my projects/notes/emails/slides/meetings/etc\n- This system should be designed to take action rather than simply just prioritizing messages. We've had prioritized inboxes for a long time but they're fine, not great\n- then someone has to put this entire UX together to be cohesive. In the future, we may not even really have an email inbox, but instead an interaction that feels more like I'm talking to an assistant who has a few questions for me. But otherwise just wants to provide a few quick updates and get some yes/nos. And otherwise filter all the noise -- just give me the most important messages\n\nIt feels like we're very, very close to being able to do this, with the latest models from Anthropic and Open AI, we have the technology already. Someone just needs to package it all together in a way where it's able to index all of your emails and notes and calendars and contacts and sort of create a second brain that knows almost everything that you know so that I actually do things that are intelligent. \n\nIt seems like with the excitement of OpenClaw we have the architecture to integrate a lot of different data sources and to take actions across multiple different channels. And it's built with one sort of monolithic memory and context, so that you're able to interact with it in such a way where it feels like it can try to replicate your actions more closely than the relatively stateless and memoryless LLM chats that we've gotten accustomed to. \n\nIf someone is working on this, please point them to me. I would be both a customer and an investor!","created_at":"Tue Feb 17 04:18:37 +0000 2026","like_count":314,"retweet_count":14,"reply_count":185,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","eventbuoy-com","fishboneny-com","instasoiree-com","renascence-network"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-02","ingested_at":"2026-07-01T04:05:11.411Z"},{"tweet_id":"2060448019632308328","author":"TheAhmadOsman","author_name":"Ahmad","text":"DROP EVERYTHING\n\nEverything you need to get started with Local AI completely FOR FREE\n\nHardware. Software. Anything in between.\n\n> TheLocalAIBook DOT com\n\nLocal LLMs From Zero to Hero Articles\n\n- Hardware foundations\n- Software stacks\n- Model mechanics\n\n> BuyAGPU dot AI\n\nThe Buy a GPU Guide Thread\n\n- How to build systems for Local AI\n- Explains what to buy, for which use cases, etc\n\nFor inference. For training. For your use case.\n\nThe resources exist\nNo more excuses\n\nOpensource / Local AI FTW","created_at":"Fri May 29 19:47:43 +0000 2026","like_count":246,"retweet_count":28,"reply_count":19,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","a3r-network"],"editorial_note":"Educational resource for chipmonk tech.","signal_type":"education","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:03.562Z"},{"tweet_id":"2010101330514223361","author":"TheAhmadOsman","author_name":"Ahmad","text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- \"tokenizer\" = the algorithm that splits text into tokens\n- common types: BPE (byte pair encoding), SentencePiece\n- token examples:\n- \"hello\" = 1 token or maybe 2 or 3 tokens\n- \"internationalization\" = 5–8 tokens\n- context window = max tokens model can \"see\" at once (2K, 8K, 32K+)\n- longer context = more VRAM for KV cache, slower decode\n\n- during inference, the model predicts next token\n- by running lots of math on its \"weights\"\n- model weights = billions of learned parameters (the knowledge and patterns from training)\n\n- model parameters: usually billions of numbers (called weights) that the model learns during training\n- these weights encode all the model's \"knowledge\" (patterns, language, facts, reasoning)\n- think of them as the knobs and dials inside the model, specifically computed to recognize what could come next\n- when you run inference, the model uses these parameters to compute its predictions, one token at a time\n\n- every prediction is just: model weights + current sequence → probabilities for what comes next\n- pick a token, append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","created_at":"Sat Jan 10 21:27:57 +0000 2026","like_count":240,"retweet_count":35,"reply_count":7,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","sliver-network","a3r-network","dochakki-com","chefaid-nyc","dank-nyc","renascence-network"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:06.033Z"},{"tweet_id":"2056842823249572153","author":"meta_alchemist","author_name":"Meta Alchemist","text":"agentic builders & vibe coders:\n\nIf you are looking to:\n> get grants to pay for your LLM subscriptions\n> get ready to launch a project that creates income\n> build in a remote incubation environment\nThen read on\n\nWe are especially looking for those who have been shipping, not aspirational vibe coders.\n\nAlso, we are looking for people who will be cool with tokenizing their projects, because the path of least resistance for open-source agentic projects now is actually the route of blockchain.\n\nWhy have a token?\n> earns you $ directly from attention/volume\n> you don't need to have subscribers to grow\n> tech stack is perfect for agentic innovations\n\nWho is the founder of this program?\n> It's me. I created a token that was fully bootstrapped and took it to a $350M market cap without any VC funding. As an incubator helped launch many startups in the blockchain, gaming, and AI verticals. \n\nAlso have 20.000+ commits in GitHub in the past 9 months, living in the terminal almost every waking hour.\n\nConditions:\n• Tokenization goal\n• Sharing a % of your tokens with our community, as incentives for jumpstarting your community building\n• Building something dope\n\nHow to apply: just drop your finest GitHub repo in the comments. That's all.","created_at":"Tue May 19 21:01:58 +0000 2026","like_count":224,"retweet_count":32,"reply_count":70,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","onesqft-org","groww-ca"],"editorial_note":"Market data for freeintelligence ai.","signal_type":"market","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:12.962Z"},{"tweet_id":"2010889969405927653","author":"alexhillman","author_name":"📙 Alex Hillman","text":"I want to give credit to whoever I got this idea from but cannot remember so if it's you please remind me so I can boost yours! \n\nHere's the prompt I used to have Claude interview me to deeply understand my business, work, and life. Many of the summaries and insights it reflected back were good to very good, and a few like the one below were MIND blowing. \n\n-------////---------\n\nAsk me a bunch of questions and interview me so that you can get the best sense of my business, my goals, my values. \n\nThen I want you to turn this conversation into a document you can refer to every time l ask you for a task that requires you to have extreme depth in my business.\n\nGot it?\n\n-------////---------\n\nThis worked extremely well on my system bc I've already added everything I've ever published or has been published about me. \n\nBut also, I didn't hold back. Took three sessions to finish and by the end I was drained like a good therapy session. \n\nThe full synthesis it created is wild, the stuff it filled in between what I said *and was right* was extraordinary.","created_at":"Tue Jan 13 01:41:43 +0000 2026","like_count":192,"retweet_count":7,"reply_count":5,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:11.723Z"},{"tweet_id":"2006769653193044186","author":"aryanXmahajan","author_name":"Aryan Mahajan","text":"spend 5 minutes daily creating content that books 10+ calls weekly\n\nthe secret is context-engineered AI that knows:\n- your business positioning\n- your ICP psychology\n- your brand voice\n- platform-specific patterns\n\nnot chatgpt generic garbage\n\nthe problem with most AI content:\n\nyou prompt for 30 minutes\nget back wikipedia slop\nedit for another hour making it human\nstill sounds like AI\ngets 8 likes\n\nwhy? because AI doesn't know your context\n\nthe solution:\n\nAI is input → output\nbetter context = better content\n\nwhat your AI needs to know:\n\nlayer 1 - business intelligence\nICP psychology (actual pain points not demographics)\nbusiness context (positioning, advantages)\npersonal profile (your story)\nproduct strategy (what you sell, why it matters)\nbrand voice (how you communicate)\n\nlayer 2 - platform patterns\nbest performing posts (your actual winners)\nplatform formatting (linkedin ≠ twitter)\nconversion patterns (what makes YOUR audience act)\n\nlayer 3 - identity programming\nnot \"you are a copywriter\"\nbut \"you're a growth marketer who discovered emotional triggers drive 10x conversions\"\n\nlive example:\n\ni ask my AI: \"create linkedin + twitter lead magnet for ad creative system\"\n\nmy AI:\nactivates brand voice profile\nloads ICP psychology\nreferences platform examples\ngenerates 2 perfect posts in 30 seconds\n\none for linkedin, one for twitter\ndifferent platforms, different tones\nboth sound exactly like me\n\nresults:\n5 minutes daily creating 10 linkedin posts + 20-30 tweets\nall in my voice\nall platform-optimized\nall converting at scale\n\ndeployed this for clients:\nrudy (50K coaching offer, scaled to 6 figures)\nsharon hedge (2K likes per post)\nlinah ai (0 to 11K followers in 60 days)\n\nyour AI stops sounding like AI when you give it proper context","created_at":"Thu Jan 01 16:49:03 +0000 2026","like_count":124,"retweet_count":10,"reply_count":5,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai","goodalgo-network","oneof1-network"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:09.679Z"},{"tweet_id":"2007976191374835730","author":"tom_doerr","author_name":"Tom Dörr","text":"Runs LLMs offline on computers\n\nhttps://t.co/LW7ATePLhQ https://t.co/2jCN4IkMy0","created_at":"Mon Jan 05 00:43:25 +0000 2026","like_count":119,"retweet_count":13,"reply_count":0,"resolved_url":"https://github.com/janhq/jan/","resolved_type":"github","venture_tags":["freeintelligence-ai"],"editorial_note":"Intelligence signal for VE Lab portfolio.","signal_type":"general","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:03.832Z"},{"tweet_id":"2059932454014853439","author":"_vmlops","author_name":"Vaishnavi","text":"MICROSOFT DROPPED A WEB AGENT THAT HITS 86.7% ON BENCHMARKS WITH ~1.5K LINES OF CODE\n\nMost web agents fail on long tasks because they're locked into one browser action at a time\n\nwebwright flips the model:\n▫️ agent gets a terminal, not a browser session\n▫️ writes playwright scripts to control the browser\n▫️ code becomes the persistent artifact, not the session\n▫️ spawns fresh browser sessions when needed\n▫️ handles loops, dynamic pages, forms like a dev would\n\nno multi-agent system. no graph engine. no hidden orchestration\njust a terminal + browser + model readable end-to-end\n\nthe numbers:\n▫️ 86.7% on online-mind2web (300 tasks) with gpt-5.4\n▫️ 60.1% on odysseys long-horizon tasks +15.6 pts over prior sota\n▫️ even qwen-3.5-9b completes tasks well when given 5+ reusable tools\n\nalso ships as a claude code skill\nyou run it with a slash command and it builds reusable cli tools for your web tasks\n\nthe core agent loop is a single ~450-line file zero black boxes\n\ngithub → \nhttps://t.co/dMZCjlPRG9","created_at":"Thu May 28 09:39:03 +0000 2026","like_count":108,"retweet_count":13,"reply_count":4,"resolved_url":"https://github.com/microsoft/Webwright","resolved_type":"github","venture_tags":["freeintelligence-ai","velab-stack"],"editorial_note":"Tool relevant to freeintelligence ai.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:11.018Z"},{"tweet_id":"2056432317036740774","author":"shiri_shh","author_name":"shirish","text":"this is the ChatGPT moment for video games\n\nAgora-1 generates a live 4-player GoldenEye match with no game engine at all\n\nIt learned to simulate the whole thing just from watching video\n\ngames will never be built the same way again","created_at":"Mon May 18 17:50:45 +0000 2026","like_count":66,"retweet_count":3,"reply_count":3,"resolved_url":null,"resolved_type":null,"venture_tags":["anygame-dev","freeintelligence-ai"],"editorial_note":"Educational resource for anygame dev.","signal_type":"education","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:05.140Z"},{"tweet_id":"2067594764233716068","author":"askalphaxiv","author_name":"alphaXiv","text":"4/N To try it out change the url of any CS arxiv paper from ‘https://t.co/v1mblM8jp2 (https://t.co/qj0T1TiNDt)’ -> ‘https://t.co/L06ShQQjuw (https://t.co/UvosprEccF)’ You can also try it out on a private codebase by visiting https://t.co/GQIbWpFvWc (https://t.co/orpVTpjX7e)","created_at":"Thu Jun 18 13:06:20 +0000 2026","like_count":45,"retweet_count":3,"reply_count":0,"resolved_url":"https://arxiv.org/","resolved_type":"arxiv","venture_tags":["freeintelligence-ai"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.316Z"},{"tweet_id":"2070942495832470001","author":"omarsar0","author_name":"","text":"If you use LLM-as-judge, this one is worth reading.\n\n(bookmark it)\n\nIt's actually one of the most effective ways to use LLM-as-a-Judge for evals.\n\nHolistic judge scores hide both their reasoning and their ceiling effects.\n\nBINEVAL decomposes each evaluation criterion into atomic yes-or-no questions, answers each independently per output, then aggregates the verdicts into calibrated multi-dimensional scores.\n\nEvery question-level verdict is inspectable, so you can diagnose exactly why an output scored low, and the same verdicts feed straight back as targeted prompt-improvement signal.\n\nAcross SummEval, Topical-Chat, and QAGS, it matches or beats UniEval and G-Eval, training-free, with especially strong results on factual consistency.\n\nPaper: https://t.co/oar6BZcasm\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX","created_at":"","like_count":0,"retweet_count":0,"reply_count":0,"resolved_url":null,"resolved_type":null,"venture_tags":["freeintelligence-ai"],"editorial_note":"Educational resource for freeintelligence ai team and stakeholders.","signal_type":"education","month_tag":"2026-06","ingested_at":"2026-07-02T01:42:19.231Z"}]}