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TIME article: https://time.com/7279010/ai-virus-lab-biohazard-study/ submitted by /u/MetaKnowing [link] [comments]

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

submitted by /u/PrincipleLevel4529 [link] [comments]

In the race for ever-larger models, its easy to forget just how powerful small LLMs can be—blazingly fast, resource-efficient, and surprisingly capable. I am biased, because my team builds these small open source LLMs - but the potential to create an exceptional user experience (fastest responses) without compromising on performance is very much achievable. I built Arch-Function-Chat is a collection of fast, device friendly LLMs that achieve performance on-par with GPT-4 on function calling, and can also chat. What is function calling? the ability for an LLM to access an environment to perform real-world tasks on behalf of the user.'s prompt And why chat? To help gather accurate information from the user before triggering a tools call (manage context, handle progressive disclosure, ...

People often include comparisons to different models, but why not include humans too? submitted by /u/katxwoods [link] [comments]

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

Ray Kurzweil's analogy using the Human Genome Project to illustrate how linear perception underestimates exponential progress, where reaching 1% in 7 years meant completion was only 7 doublings away: Halfway through the human genome project, 1% had been collected after 7 years, and mainstream critics said, “I told you this wasn’t going to work. 1% in 7 years means it’s going to take 700 years, just like we said.” My reaction was, “We finished one percent - we’re almost done. We’re doubling every year. 1% is only 7 doublings from 100%.” And indeed, it was finished 7 years later. A key question is why do some people readily get this, and other people don’t? It’s definitely not a function of accomplishment or intelligence. Some people who are not in professional fields understand this very r...

I code A LOT. It's sorta my life. I used to work on a lot of different projects but now I'm fully invested in getting Google's A2A protocol off the ground. If you have any questions about A2A, I'd be happy to answer them. At the current moment, I consider myself one of the foremost experts (given how nascent the protocol is and how long I've been staring at code and getting types to compile, I think it's a fair statement.) If you want to follow the progress of the protocol, join in at r/AgentToAgent (my sub because of course it is) submitted by /u/robert-at-pretension [link] [comments]

Social Stockfish is an AI that predicts 7 moves in any conversation, helping you craft the perfect response based on your goals, whether you’re asking someone out, closing a deal, or navigating a tricky chat. Here’s the cool part: it uses two Gemini 2.5 models (one plays you, the other plays your convo partner) to simulate 2187 possible dialogue paths, then runs a Monte Carlo simulation to pick the best next line. It’s like having a chess engine (inspired by Stockfish, hence the name) but for texting! The AI even integrates directly into WhatsApp for real-time use. I pulled this off by juggling multiple Google accounts to run parallel API calls, keeping it cost-free and fast. From dating to business, this thing sounds like a game-changer for anyone who’s ever choked on words. What do...

Source submitted by /u/MetaKnowing [link] [comments]