DebugBuddy
Stop googling your errors.
What it does
DebugBuddy explains your code errors in plain English. Paste your error and code, get a clear explanation of what went wrong, why it happened, and the corrected code. Built for students and developers who want to actually understand their errors, not just fix them. Powered by Claude AI. Supports Python, JavaScript, Java, C++, TypeScript, Rust, Go, PHP, Swift, Kotlin, C#, Ruby.
Does the same job
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- RARecompyle – A JavaScript developer-friendly console / debugger2023 · recompyle.com · ▲58
Hi HN, I'm Damien, the solo founder of Recompyle. Self-funded. As a JavaScript developer, I have been annoyed by the console.log not being practical enough, and the debugger being 'too slow'. So I made Recompyle to fix that. It's kind of a mix of the console and the debugger. Plus, some file parsing to automatically log the start of the functions of the files you are working on.
- DBDebugging Babel and Node done right2016 · ▲22
Requirement: - Excellent feedback during development - Debugging with source maps Method: I'm using these scripts in package.json. "scripts": { "di": "node-inspector -d=7000 -p=8000 --save-live-edit --hidden node_modules/", "ds1": "babel ./server -d ./build/server -s -w", "ds2": "nodemon --watch ./build/server --delay 100ms --debug=7000 ./build/server/main.js" }, Then run "npm run ds1; npm run ds2; npm run di" Why: I took some time to put this together, after navigating all the tools out there. I personally find this to be "correct". Thought I'd…
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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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AI · 18d ago · company-app.joinastute.com
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…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com