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Here's a playbook that works today, right now, with tools that are either free or cheap: Someone finds a photo of you online. One photo. They run it through a face ID search and find your other photos across the internet. They drop one into GeoSpy, which analyzes background details in images to estimate where you live. A street sign, a building style, a type of tree. It's scarily accurate. Now they search Shodan for exposed camera feeds near that location. If you're in one of the 6,000+ communities using Flock Safety cameras, you might be in luck. Late last year, researchers found 67 Flock cameras streaming live to the open internet with no password and no encryption. A journalist watched himself in real time from his phone. Flock called it a "limited misconfiguration." They're valued at $...

Every day someone posts "AI will change everything" and it's always about agents scaling businesses, automating workflows, 10x productivity, whatever. Cool. But change everything for who? Go talk to the barber who loses 3 clients a week to no-shows and can't afford a booking system that actually works. Go talk to the solo attorney who's drowning in intake paperwork and can't afford a paralegal. Go talk to the tattoo artist who's on the phone all day instead of tattooing. Go talk to the author who wrote a book and has zero idea how to market it. These people don't need another app. They don't need to "learn to code." They don't need to understand what an LLM is. They need the tools that already exist and wired into their actual business. Their actual pain. The gap between "AI can do amazing...

Quick experiment I ran. Took two identical AI coding agents (Claude Code), gave them the same task — optimize a small language model. One agent worked from its built-in knowledge. The other had access to a search engine over 2M+ computer science research papers. Agent without papers: did what you'd expect. Tried well-known optimization techniques. Improved the model by 3.67%. Agent with papers: searched the research literature before each attempt. Found 520 relevant papers, tried 25 techniques from them — including one from a paper published in February 2025, months after the AI's training cutoff. It literally couldn't have known about this technique without paper access. Improved the model by 4.05% — 3.2% better. The interesting moment: both agents tried the same idea (halving the batch s...

Just found this “bullshit benchmark,” and sort of shocked by the divergence of Anthropic’s models from other major models (ChatGPT and Gemini). IMO this alone is reason to use Claude over others. submitted by /u/djiivu [link] [comments]

In April Congress is voting to extend the FISA Act on the 20th of April this year. The FISA Act allows the government to buy your emails, texts, and calls from corporations. With the newly established shady deal with Open AI surveillance has become even more accessible and applicable on a much more larger and invasive scale. It very important for the sake of maintaining our right of protest and the press in the future. Call/email your representatives in the US, protest, and speak in any way you can. submitted by /u/FrequentAd5437 [link] [comments]

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]

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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]