🛠️ All DevTools

[Other] I designed a nibble-oriented CPU in Verilog to build a scientific calculator

[Package Manager] Show HN: Sx – an open-source package manager for AI skills, MCPs, and commands

[Other] Bun Rust rewrite: "codebase fails basic miri checks, allows for UB in safe rust"

[CLI Tool] Show HN: Termini – Open-Source Menu Bar Terminal for macOS

[Other] Show HN: Mezz, a curl-able WiFi sandbox for IoT pentesting

[Other] A 0-click exploit chain for the Pixel 10

[IDE/Editor] Show HN: Ane, a new chord-based terminal code editor with one-shot CLI edits I’ve had this idea for a new code editor chord grammar bouncing around in my head for over 2 years, and now I can finally release v0.1 of ane. You can run it one-shot from scripts or agents (`ane exec —chord…`) or use its full-on TUI editor. ane is pure Rust, and integrates tree-sitter and LSP to provide a pleasant and predictable terminal code editing experience. I’d love folks to try it out and give feedback! Thanks :)

[Other] Radicle: Sovereign {code forge} built on Git

[Other] Mercurial, 20 years and counting: how are we still alive and kicking? [video]

[Other] Prolog Basics Explained with Pokémon

[DevOps] Show HN: GlycemicGPT – Open-source AI-powered diabetes management I&#x27;m a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I&#x27;m an engineer, so I built the tool I needed — and now I&#x27;m open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:<p>Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you&#x27;re running in minutes)<p>What the AI layer does:<p>Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalation<p>Important: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:<p>Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-in<p>Stack:<p>Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn&#x2F;ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxed<p>Looking for contributors — especially folks with BLE&#x2F;Android experience or anyone in the diabetes tech space. Plugin SDK is documented if you want to add support for new devices. GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;GlycemicGPT&#x2F;GlycemicGPT" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;GlycemicGPT&#x2F;GlycemicGPT</a>

[Other] How Claude Code works in large codebases

[Other] Show HN: Openvid – open-source cinematic screen recorder and mockup editor

[Other] LLM Policy for Rust Compiler

[Other] OVMS: Open source electric vehicle remote monitoring, diagnosis and control

[Code Quality] Mado: Fast Markdown linter written in Rust

[Other] Show HN: Full Stack HQ – Claude.md and Agent Stack for Claude Code Permission-first config kit for Claude Code and Google Antigravity IDE. Installs CLAUDE.md + GEMINI.md + 10 specialist agents + 28 skills with one command. MIT license.

[Other] Show HN: JDS – a Copilot skill suite for structuring AI coding behavior A few months ago I stumbled on obra&#x27;s superpowers repository <a href="https:&#x2F;&#x2F;github.com&#x2F;obra&#x2F;superpowers" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;obra&#x2F;superpowers</a>. I really liked the approach and idea that you enforce discipline for your agent through a skill-based workflow. Even though coding agents (copilot included) have become a lot better at natively handling complex tasks, they still wander off and lose track of things. I really liked how superpowers fixed this and how it enabled long-running sessions without the agent losing its &quot;focus&quot;. So I decided to build a Copilot tailored skill suite around the core idea of superpowers. I didn&#x27;t just want to port superpowers to Copilot, I took inspiration from it and improved on it. JDS enforces a strict think -&gt; plan -&gt; execute pipeline where nothing gets skipped. It leverages Copilot&#x27;s built-in sql todo dependencies and provides a live task graph visualizer which helps visualize the agentic workflow and its parallelism. Curious whether others have tried similar approaches, and what&#x27;s worked or not.

[Other] Codex is now in the ChatGPT mobile app

[Other] Grok Build