Selectd
Stop security risks and threats before they become incidents
What it does
Selectd gives lean IT teams an AI security team that turns vulnerabilities, threats, regulatory changes and findings into outcomes. • Know what needs attention now for your company • Get decisions ready faster • Stop repeating the same research A shared company context carries technologies, owners, controls and past decisions forward, so every agent starts with what the company already knows. Free to start. No credit card.
Selectd Terminal is the AI security team for lean IT teams. Selectd turns relevant security change into prioritized, prepared and traceable security work.
Specialized agents handle recurring security work that would otherwise keep landing on your IT team. Security tools deliver findings. Your IT team still has to add context, priority, ownership and follow-up. Security tools keep producing work that small teams cannot keep up with. Severity alone does not tell you what matters to your company. Relevance, priority and the next step still have to be worked out. Tools produce findings. Turning them into owned, prioritized, finished work is still manual. Put findings in context. Prioritize what matters. Prepare what comes next. Findings and changes from your tools and trusted external sources. Agents investigate signals, connect evidence and work…from selectd.ai
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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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Launched alongside, August 2026
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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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