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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...

"Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache, a high-speed "digital cheat sheet" that stores frequently used information under simple labels so a computer can retrieve it instantly without having to search through a slow, massive database. Vector quantization is a powerful, classical data compression technique that reduces the size of high-dimensional vectors. This optimization addresses two cri...

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

Something interesting happened this month. March 11: Perplexity announced Personal Computer. An always-on Mac Mini running their AI agent 24/7, connected to your local files and apps. Cloud AI does the reasoning, local machine does the access. March 16: Meta launched Manus "My Computer." Same idea. Their agent on your Mac or Windows PC. Reads, edits local files. Launches apps. Multi-step tasks. $20/month. March 23: Anthropic shipped computer use and Dispatch for Claude. Screen control, phone-to-desktop task handoff, 50+ service connectors, scheduled tasks. Three separate companies. Same architecture. Same two weeks. I've been running a version of this pattern for months (custom AI agent on a Mac Mini, iMessage as the interface, background cron jobs, persistent memory across sessions)....