Kelvane
Turneky Legacy code to agentic-ready apps with MCP
What it does
Kelvane helps you bring legacy software into the agentic era securely It features an open-source Rust WebAssembly runtime to sandbox untrusted neural policies with zero ambient authority and strict CPU/memory caps Additionally, our platform compiles outdated backend code into clean Next.js + Postgres dashboards featuring built-in Model Context Protocol (MCP) tools Hot-swap policies mid-flight, and try it live with 10 free daily UTC credits
Builder for running apps — routes, records, real data — not a UI mock. Wrap a contained legacy system toward modern SaaS. Hosting is a deployment choice, not the product.
Same rules. Same business logic. Just modern and better — secure by default, with observability and an audit trail your ops can actually trust. You work in your own account. Builds use credits — a free daily grant to start, no card required. Packs are optional when you need more. It writes a real app — routes, records, and a place for your data — then compiles it. What you preview is that build. Not a screenshot generator. Preview is live on Kelvane. Production can stay with us, move to your cloud, or ride an edge we stand up together. Hosting is not the product definition. First paint is an application: landing, a primary flow, seeded records, and a store. Auth, mail, API, and billing are…from kelvane.dev
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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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