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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]

Personally I have been seeing some developments of AI for niche areas like ones relating to medicine. I feel like if done properly, this can be helpful for people who can't afford to visit a doctor. Of course, it's still important to be careful with what AI can advise especially to very specific or complicated situations, but these can potentially be a big help to those who need it. submitted by /u/pUkayi_m4ster [link] [comments]

I like the new ChatGPT generator but it takes too long to generate images for my purpose. I need something faster but also has the same quality. Google Gemini's Imagen seems to produce only low resolution images... I'm very uneducated in this area and really need advice. Can someone recommend me an engine? For context, I have to generate a lot of images for the B-roll of Instagram reels and TIktoks I record. submitted by /u/shouldIworkremote [link] [comments]

I read that o3 can tell where a photo was taken pretty accurately so decided to test it myself. Gotta say that I'm impressed and a bit scared at the same time. https://preview.redd.it/6yrfc6sx9uve1.jpg?width=703&format=pjpg&auto=webp&s=a749532d6c6cf9930a8b8b30daa28fcc6aad7638 submitted by /u/Altruistic-Hat9810 [link] [comments]

Interview here. submitted by /u/MetaKnowing [link] [comments]

Hey folks! I’ve been working with the team at Yandex Research on a way to make LLMs easier to run locally, without calibration data, GPU farms, or cloud setups. We just published a paper on HIGGS, a data-free quantization method that skips calibration entirely. No datasets or activations required. It’s meant to help teams compress and deploy big models like DeepSeek-R1 or Llama 4 Maverick on laptops or even mobile devices. The core idea comes from a theoretical link between per-layer reconstruction error and overall perplexity. This lets us: -Quantize models without touching the original data -Get decent performance at 3–4 bits per parameter -Cut inference costs and make LLMs more practical for edge use We’ve been using HIGGS internally for fast iteration and testing, and it's proven highl...

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