devdigest.io
One daily AI tech digest, and why it picked each story
What it does
You've got a dozen tabs open labeled "read later" and none of them get read. devdigest.io picks the best tech and AI stories from 110+ sources daily, tailored to your topics, in one email. Every story carries a one-line reason it's there: topic match, a source you've upvoted, or editorial ranking, so it's never a black box. Downvote something and say why, and it reweights what you see next. Solo-built, no ads, no data-selling. Free 14-day trial, no card required, €50/year after.
A daily tech digest that actually learns your taste. Curated from 130+ real sources, tuned by your own 👍/👎, delivered once a day.
AI reads 130+ sources before you wake up and sends you only what matters. Free forever: 5 a day, no card - and your first 14 days are the full thing. Every story has a 👍 More like this and 👎 Less like this . Each vote nudges that topic's weight in your mix - upvote Security a few times and you'll see more of it tomorrow, downvote AI and it fades - not a generic ranking everyone else gets, yours alone. And a downvote isn't a black box: tap it and you'll get a one-tap follow-up - wrong topic, not keen on this source, or too shallow/technical - so the fix lands on the actual problem instead of quietly narrowing your whole feed. Illustrative - your own mix starts even and shifts with your…from devdigest.io
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Life & fun · 10d ago · louisabraham.github.io
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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