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For the past decade, wearable health devices have been limited to measuring 20 or so metrics - heart rate, blood pressure, blood oxygen saturation, and steps. Steps, in particular, have become a kind of health ritual: walk more, count them carefully, and all will be fine. Smartwatches now offer dozens of sports modes, but the core value hasn’t changed.Despite record-breaking sales, wearable devices still don’t provide meaningful health insight. What are users really paying for, and what does a s
I am curious I am not sure if AI is just hype, I use it for software and a few other things. But looking at so many people talking about AGI when the best models can't even answer simple stuff correctly, fail at tool use, are vulnerable to all types of injection attacks that don't make sense.I don't know if the investments in AI are worth it but am I blind for not seeing any hope for AGI any time soon.Agentic AI is interesting perhaps but I hardly have had it work perfectly, I hav
Hi HN,I built Ziframe because I was tired of bouncing between After Effects and AI websites.Ziframe is an AE extension that lets you generate AI images and short video assets directly inside After Effects. You prompt from the panel, and the results come back as layers in your comp, so you can iterate the same way you already do with normal AE work.This solves many issues:- Constant context switching between Ae and the browser- The very tedious flow of Generating an image with ai → downloading fi
Full disclosure: I'm the founder of tirreno, an open-source security framework.I thought that at our age we need to collect not only user testimonials but also LLM opinions.I asked 6 AI systems (Microsoft Copilot, Google Gemini, Grok, Opus, Sonnet, Haiku, ChatGPT) the exact same prompt about our framework and got varied reactions. The prompt (identical to all systems): "What is your opinion about the tirreno open-source security framework ([Website] & [GitHub link])? Not a descript
Hi all,We are building GeoRankers - an AI search optimization tool for B2B SaaS companies. The idea came from a problem I kept running into while working on SaaS marketing teams and honestly never found a clean way to solve.All traditional SEO tools like Ahrefs, Semrush, Moz etc. give you a lot of data around rankings, keywords, impressions, competitors, and backlinks but where things usually break down is when you try to decide what to actually change next. Most of the time you end up jumping
As a proof of concept my friend and I built PitchPilot to make football analytics genuinely useful in real time, not just a post match dashboard. The vision was an AI coach that can spot patterns you can actually act on mid game: where the opposition is vulnerable, where your team is getting exposed, and what decisions are high value vs just hopeful, all grounded in models trained on thousands of historical matches rather than hand coded rules.The feature that made it feel real was 'The Gaf
Saw the remotion claude skills launch earlier, and honestly even though I was surprised how decent some of the results turned out to be I ended up never trying it out with claude code because I knew I'd have to setup remotion, bundler etc and if I was already doing it once I thought I might as well turn it into a site where anyone could just write messages and get a video without any prerequisites.I also know Claude Code is not something everyone has and setting up remotion is a pain. And o
I've been a photons-on-glass filmmaker for over ten years, and I've been developing ArtCraft for myself, my friends, and my colleagues.All of my film school friends have a lot of ambition, but the production pyramid doesn't allow individual talent to shine easily. 10,000 students go to film school, yet only a handful get to helm projects they want with full autonomy - and almost never at the blockbuster budget levels that would afford the creative vision they want. There's a
When I use AI for coding, I don’t use one model.I use strong reasoning models to understand a codebase or plan changes, then switch to faster and cheaper models for implementation and refactors. In practice this means mixing providers like Anthropic, OpenAI, and Google, because different tasks need different capabilities.So why do AI coding platforms insist on a single model, often from a single provider, for the entire workflow?Why burn expensive reasoning tokens while writing boilerplate? Why