Sweep AI
AI coding assistant in IntelliJ, PyCharm, and JetBrains IDEs
What it does
Sweep is an AI coding assistant purpose-built for JetBrains IDEs. We bring features like multi-file code "apply" and automatic context awareness into a secure, on-prem ready AI plugin for JetBrains.
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- SASweep – AI-Powered Junior Developer for Tech Debt2023 · github.com · ▲7
Hi HN, we recently built and open sourced Sweep, an AI powered junior developer. Kevin, Luke, and I were tired of handling tech debt when we knew LLMs could do this for us. Sweep solves small features, bugs, and missing documentation by writing pull requests. This allows you to review and validate the code before pushing it to your codebase. Features: - Turns issue descriptions directly into pull requests (without an IDE) - Addresses developer replies & comments - Fixes the code based on failing tests & linters from GitHub Actions Examples: https://docs.sweep.dev/examples…
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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, April 2025
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Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
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https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
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