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Found this site that tracks researchers and executives who left OpenAI, Google, Anthropic, and others over safety concerns. It's kind of amazing to see the patterns; concerns become really obvious across companies. I love AI but do want to see regulations. The interesting part: it extracts specific predictions the researchers made and tracks whether they come true. 4 confirmed, 1 disproven, 6 still open. I would think there are others, the number is not that high, but maybe also most people who leave do it quietly? What do you think? ethicalaidepartures.fyi submitted by /u/Junket6226 [link] [comments]

submitted by /u/boppinmule [link] [comments]

submitted by /u/esporx [link] [comments]

I built CodexLib (https://codexlib.io) — a curated repository of 100+ deep knowledge bases in compressed, AI-optimized format. The idea: instead of pasting long documents into your context window, you use a pre-compressed knowledge pack with a Rosetta decoder header. The AI decompresses it on the fly, and you get the same depth at ~15% fewer tokens. Each pack covers a specific domain (quantum computing, cardiology, cybersecurity, etc.) with abbreviations like ML=Machine Learning, NN=Neural Network decoded via the Rosetta header. There's a REST API for programmatic access — so you can feed domain expertise directly into your agents and pipelines. Currently 100+ packs across 50 domains, all generated using TokenShrink compression. Free tier available. Curious what domains people would find m...

Most people just type into ChatGPT like it's Google. Claude with a structured system prompt using XML tags behaves like a completely different tool. Example system prompt: <role>You are a senior equity analyst</role> <task>Analyse this earnings transcript and extract: 1) forward guidance tone 2) margin surprises 3) management deflections</task> <output>Return as structured JSON</output> Then paste the entire earnings call transcript. You get institutional-grade analysis in 4 seconds that would take an analyst 2 hours. Works on any 10-K, annual report, VC pitch deck. Game over for basic research. submitted by /u/broSleepNow [link] [comments]

submitted by /u/tw1st3d_m3nt4t [link] [comments]

submitted by /u/esporx [link] [comments]

"The team trained machine learning models to identify patterns in the data that can tell astronomers the type of event that has been detected, something that AI models excel at. RAVEN is designed to handle the whole exoplanet-detection process in one go — from detecting the signal to vetting it with machine learning and then statistically validating it. That means that it has an additional edge over other contemporary tools that only focus on specific parts of this process ... "RAVEN allows us to analyze enormous datasets consistently and objectively," senior team member and University of Warwick researcher David Armstrong said in the statement. "Because the pipeline is well-tested and carefully validated, this is not just a list of potential planets — it is also reliable enough to ...

What if building more and more datacenters was not the only option? If we are able to get similar levels of performance for top models at a consumer level from smarter systems, then its only a matter of time before the world comes to the realization that AI is a lot less expensive and a whole lot more obtainable. Open source projects like ATLAS are on the frontier of this possibility- where a 22 year old college student from Virginia Tech built and ran a 14B parameter AI model on a single $500 Consumer GPU and scored higher than Claude Sonnet 4.5 on coding benchmarks (74.6% vs 71.4% on LiveCodeBench, 599 problems). No cloud, no API costs, no fine-tuning. Just a consumer graphics card and smart infrastructure around a small model. And the cost? Only around $0.004/task in electricity. The b...