Voidleap Code
Coding environment and harness. BYOK. Build better.
What it does
An agentic IDE for developers building with AI agents. We built the harness ourselves to improve token efficiency across the agent loop. Bring your own key. Local agentic runtime on your machine. Your code and prompts go to your model provider, never through our servers. Inspect and control agents. See every agent live in one view. Switch models and edit context mid-thread. Adapt Voidleap Code to your workflow rather than fit a fixed framework. Built by developers, for developers.
Voidleap Code is a desktop IDE for building software with AI agents, built for professional developers and AI engineers. It runs on your computer with your own provider accounts or a local model. Every prompt, tool call, and file change is visible.
Voidleap Code is a desktop app for building software with AI agents. It is an integrated development environment (IDE), built for professional developers and AI engineers. You direct the agents and see everything they do: every prompt, every tool call, every changed file. It runs on your computer, with your own AI provider accounts or a local model. Your code and prompts do not pass through our servers. Agents write more of the code now. Most tools hide how. You see a summary, not the steps. When output degrades, you guess. The tools are built to sell inference, not to make you better. And they try to work for everyone, from first prompt to production, so they commit to no one. Many still…from voidleap.com
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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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