🛠️ All DevTools

[Other] The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

[Other] A curated list of awesome skills, hooks, slash-commands, agent orchestrators, applications, and plugins for Claude Code by Anthropic

[Other] Agent skills for Obsidian. Teach your agent to use Markdown, Bases, JSON Canvas, and use the CLI.

[Other] Show HN: Quillium, Git for Writers This is a tool which lets you easily manage different versions of ideas, helpful for writing essays.<p>I&#x27;ve found myself wanting this every single time I go through the drafting process when writing, and I&#x27;ve been frustrated every time I find myself accidentally working on an old draft just because there was a paragraph that I liked better. This solves it.<p>I hope the community like this as much I enjoyed working on it! Note that it&#x27;s currently a beta waitlist because there&#x27;s some bugs with the undo&#x2F;redo state management and so I want to dogfood it for a bit for reliability.<p>It says April 2nd, but I may allow earlier beta testers.

[Other] Show HN: Refrax – my Arc Browser replacement I made from scratch Open the same tab in two browser windows. In Chrome or Safari, you get two unconnected pages. In Arc, one window shows a placeholder. In Zen, it silently creates a duplicate.<p>In Refrax, the browser I built, both windows show the same page updating live. The same web page, in as many windows as you want.<p>This shouldn&#x27;t be possible. WebKit&#x27;s WKWebView can exist in exactly one view hierarchy at a time. With macOS 26, Apple added a SwiftUI API separating WebView from WebPage, so you can end up with multiple views referencing the same page. But if you try it, your app crashes. WebKit source code has a precondition with this comment: &quot;We can&#x27;t have multiple owning pages regardless, but we&#x27;ll want to decide if it&#x27;s an error, if we can handle it gracefully, and how deterministic it might even be...&quot;<p>So here&#x27;s how I did it.<p>CAPortalLayer is an undocumented private class that&#x27;s been in macOS since 10.12. It mirrors a layer&#x27;s composited output by referencing the same GPU memory, not copying it. Every scroll, animation, or repaint reflects instantly. This is what powers Liquid Glass effects, the iOS text selection magnifier, and ghost images during drag and drop. Apple uses portals for effects. I use them to put the same web page in two windows.<p>Refrax keeps one real WKWebView per tab and displays a CAPortalLayer mirror everywhere else. When you click a different window, the coordinator moves the real view there and the old window gets a portal. You can&#x27;t tell which is which.<p>This sounds simple in theory, but making this actually work seamlessly took quite a lot of effort. Each macOS window has its own rendering context, and the context ID updates asynchronously, so creating a portal immediately captures a stale ID and renders nothing. The portal creation needs to be delayed, but delaying creates a visual gap. I capture a GPU snapshot using a private CoreGraphics function and place it behind the portal as a fallback. Another hard part is that none of it is documented. Portals are very capricious and would crash the app if you use them incorrectly. I had to inspect the headers and then disassemble the binaries to explore exactly how it works in order to build something robust.<p>I never worked on a browser before this, I&#x27;ve only been a user. I started using Arc in 2022. I remember asking for an invite, learning the shortcuts, slowly getting used to it. I didn&#x27;t like it at first as it had too much Google Chrome in it for my taste, and I&#x27;d been using Safari at the time. But it grew on me, and by the time it was essentially abandoned and sold to Atlassian, I couldn&#x27;t go back to Safari anymore. I tried everything: Zen, SigmaOS, Helium. None felt right, and I didn&#x27;t want another Chromium fork. WebKit ships with the OS, but all you get is the rendering engine. Tabs, history, bookmarks, passwords, extensions, everything else has to be made separately. And so, being a very reasonable person, I decided to make my own Arc replacement from scratch.<p>And I did. Refrax is built in Swift and Objective-C with no external dependencies. The app itself is less than 30 MB. I have 393 tabs open right now using 442 MB of RAM; 150 tabs in Safari was already over 1 GB. I&#x27;ve been using it daily for over a month, and so have some of my friends.<p>The portal mirror is just one feature. The same approach, finding what Apple built for themselves and using it to create something they didn&#x27;t think about, runs through the entire browser. You can tint your glass windows with adjustable blend modes and transparency. The sidebar in compact mode samples the page and matches the colors. And it has support for Firefox and Chrome extensions.<p>The alpha is public. Download from the linked website, enter REFRAX-ALPHA-HACKERNEWS to activate. No account needed. Telemetry is crash reports and a daily active-user ping, nothing else. And if you find a bug – I built this alone, so I&#x27;ll actually read your report.

[IDE/Editor] Show HN: A C/C++ IDE written in C from scratch with zero dependencies I’ve spent the last ~2 years building a C&#x2F;C++ IDE for Windows entirely in C99. My goal was to eliminate the bloat of the modern Visual Studio environment keeping its core features.<p>Key Technical Highlights:<p>Zero Dependencies: Just the Windows API<p>Performance: Starts in 0.1ms on a twelve year old machine. It handles projects with 500k+ lines without UI lag or indexing stutters.<p>Integrated Tools: Includes a custom-built debugger, profiler, and Intellisense engine designed for speed.<p>Portability: It’s a single executable. No installer required.<p>I built this because I was tired of waiting 10 seconds for Visual Studio to &quot;wake up&quot; and seeing my RAM disappear just to boot it up. It’s currently hosted on itch.io while I gather funds to set up a dedicated site.

[IDE/Editor] Show HN: Revise – An AI Editor for Documents I started building this 10 months ago, largely using agentic coding tools. I&#x27;ve stayed very involved in the code base and architecture, and have never moved faster in my life as a dev.<p>The word processor engine and rendering layer are all built from scratch - the only 3rd party library I used was the excellent Y.js for the CRDT stack.<p>Would love some feedback!

[Other] Building an FPGA 3dfx Voodoo with Modern RTL Tools

[Testing] Show HN: I replaced every function in a codebase with English – it still works Tril converts every function in a repo into plain English descriptions, then runs and tests them — using an LLM as the interpreter instead of a runtime.<p>The concept: programming languages exist because machines couldn&#x27;t understand human intent. LLMs can. So what happens if you remove the code entirely and just... describe what each function should do?<p>The tool replaces functions one by one, runs your test suite after each swap to confirm nothing breaks, and outputs a .md file. Then `tril run` spins up an HTTP server that sends each function&#x27;s English description to Claude and returns the result.<p>Tested on a unit converter (JS) and a 625-line Python CLI tool — tests passed, results matched to 6 decimal places (fortunately).<p>This is mainly a thought experiment: can any code become plain natural language? Will it still work? Let&#x27;s find out!<p>npm: npx @sliday&#x2F;tril convert URL<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;sliday&#x2F;tril" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sliday&#x2F;tril</a>

[CLI Tool] $ teebot.dev – from terminal to tee in 6 seconds

[Other] Sashiko: An agentic Linux kernel code review system

[Other] Show HN: ClawMem – Open-source agent memory with SOTA local GPU retrieval So I&#x27;ve been building ClawMem, an open-source context engine that gives AI coding agents persistent memory across sessions. It works with Claude Code (hooks + MCP) and OpenClaw (ContextEngine plugin + REST API), and both can share the same SQLite vault, so your CLI agent and your voice&#x2F;chat agent build on the same memory without syncing anything.<p>The retrieval architecture is a Frankenstein, which is pretty much always my process. I pulled the best parts from recent projects and research and stitched them together: [QMD](<a href="https:&#x2F;&#x2F;github.com&#x2F;tobi&#x2F;qmd" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tobi&#x2F;qmd</a>) for the multi-signal retrieval pipeline (BM25 + vector + RRF + query expansion + cross-encoder reranking), [SAME](<a href="https:&#x2F;&#x2F;github.com&#x2F;sgx-labs&#x2F;statelessagent" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sgx-labs&#x2F;statelessagent</a>) for composite scoring with content-type half-lives and co-activation reinforcement, [MAGMA](<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2501.13956" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2501.13956</a>) for intent classification with multi-graph traversal (semantic, temporal, and causal beam search), [A-MEM](<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2510.02178" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2510.02178</a>) for self-evolving memory notes, and [Engram](<a href="https:&#x2F;&#x2F;github.com&#x2F;Gentleman-Programming&#x2F;engram" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Gentleman-Programming&#x2F;engram</a>) for deduplication patterns and temporal navigation. None of these were designed to work together. Making them coherent was most of the work.<p>On the inference side, QMD&#x27;s original stack uses a 300MB embedding model, a 1.1GB query expansion LLM, and a 600MB reranker. These run via llama-server on a GPU or in-process through node-llama-cpp (Metal, Vulkan, or CPU). But the more interesting path is the SOTA upgrade: ZeroEntropy&#x27;s distillation-paired zembed-1 + zerank-2. These are currently the top-ranked embedding and reranking models on MTEB, and they&#x27;re designed to work together. The reranker was distilled from the same teacher as the embedder, so they share a semantic space. You need ~12GB VRAM to run both, but retrieval quality is noticeably better than the default stack. There&#x27;s also a cloud embedding option if you&#x27;re tight on vram or prefer to offload embedding to a cloud model.<p>For Claude Code specifically, it hooks into lifecycle events. Context-surfacing fires on every prompt to inject relevant memory, decision-extractor and handoff-generator capture session state, and a feedback loop reinforces notes that actually get referenced. That handles about 90% of retrieval automatically. The other 10% is 28 MCP tools for explicit queries. For OpenClaw, it registers as a ContextEngine plugin with the same hook-to-lifecycle mapping, plus 5 REST API tools for the agent to call directly.<p>It runs on Bun with a single SQLite vault (WAL mode, FTS5 + vec0). Everything is on-device; no cloud dependency unless you opt into cloud embedding. The whole system is self-contained.<p>This is a polished WIP, not a finished product. I&#x27;m a solo dev. The codebase is around 19K lines and the main store module is a 4K-line god object that probably needs splitting. And of course, the system is only as good as what you index. A vault with three memory files gives deservedly thin results. One with your project docs, research notes, and decision records gives something actually useful.<p>Two questions I&#x27;d genuinely like input on: (1) Has anyone else tried running SOTA embedding + reranking models locally for agent memory, and is the quality difference worth the VRAM? (2) For those running multiple agent interfaces (CLI + voice&#x2F;chat), how are you handling shared memory today?

[Other] Floci – A free, open-source local AWS emulator

[Other] Professional video editing, right in the browser with WebGPU and WASM

[Other] SSH Certificates and Git Signing

[Other] Show HN: Joonote – A note-taking app on your lock screen and notification panel I finally built this app after many years of being sick of unlocking my phone every goddamn time I need to take or view my notes. It particularly sucks when I&#x27;m doing my grocery and going down the list.<p>I started building last year June. This is a native app written in Kotlin. And since I&#x27;m a 100% Web dev guy, I gotta say this wouldn&#x27;t have been possible without this AI to assist me. So this isn&#x27;t &quot;vibe-coded&quot;. I simply used the chat interface in Gemini website, manually copy paste codes to build and integrate every single thing in the app! I used gemini to build it just because I was piggybacking on my last company&#x27;s enterprise subscription. I personally didn&#x27;t subscribe to any AI (and still don&#x27;t cuz the free quota seems enough for me :)<p>So I certainly have learnt alot about Android development, architecture patterns, Kotlin syntax, and obeying Google&#x27;s whims. Can&#x27;t say I love it all, but for the sake of this app, I will :)<p>Anyway, I finally have the app I wish existed, and I&#x27;m using it everyday. It not only does the main thing I needed it to do, but there&#x27;s also all this stuff:<p>- Make your notes private if you don&#x27;t want to show them on lock screen. - Create check&#x2F;to-do lists. - Set one time or recurring reminders. - Full-text search your notes in the app. - Speech-to-text. - Organize your notes with custom or color labels. - Pin the app as a widget on your home screen. - You can auto backup and restore your notes on new install or Android device. - Works offline. - And no funny business happening in the background <a href="https:&#x2F;&#x2F;joonote.com&#x2F;privacy" rel="nofollow">https:&#x2F;&#x2F;joonote.com&#x2F;privacy</a><p>It&#x27;s 30-day trial, then a one-time $9.99 to go Pro forever.<p>I would love you all to check it out, FWIW.<p>Ok thanks!

[Database] Grafeo – A fast, lean, embeddable graph database built in Rust

[Other] Show HN: AI SDLC Scaffold, repo template for AI-assisted software development I built an open-source repo template that brings structure to AI-assisted software development, starting from the pre-coding phases: objectives, user stories, requirements, architecture decisions.<p>It&#x27;s designed around Claude Code but the ideas are tool-agnostic. I&#x27;ve been a computer science researcher and full-stack software engineer for 25 years, working mainly in startups. I&#x27;ve been using this approach on my personal projects for a while, then, when I decided to package it up as scaffold for more easy reuse, I figured it might be useful to others too. I published it under Apache 2.0, fork it and make it yours.<p>You can easily try it out: follow the instructions in the README to start using it.<p>The problem it solves:<p>AI coding agents are great at writing code, but they work much better when they have clear context about what to build and why. Most projects jump straight to implementation. This scaffold provides a structured workflow for the pre-coding phases, and organizes the output so that agents can navigate it efficiently across sessions.<p>How it works:<p>Everything lives in the repo alongside source code. The AI guidance is split into three layers, each optimized for context-window usage:<p>1. Instruction files (CLAUDE.md, CLAUDE.&lt;phase&gt;.md): always loaded, kept small. They are organized hierarchically, describe repo structure, maintain artifact indexes, and define cross-phase rules like traceability invariants.<p>2. Skills (.claude&#x2F;skills&#x2F;SDLC-*): loaded on demand. Step-by-step procedures for each SDLC activity: eliciting requirements, gap analysis, drafting architecture, decomposing into components, planning tasks, implementation.<p>3. Project artifacts: structured markdown files that accumulate as work progresses: stakeholders, goals, user stories, requirements, assumptions, constraints, decisions, architecture, data model, API design, task tracking. Accessed selectively through indexes.<p>This separation matters because instruction files stay in the context window permanently and must be lean, skills can be detailed since they&#x27;re loaded only when invoked, and artifacts scale with the project but are navigated via indexed tables rather than read in full.<p>Key design choices:<p>Context-window efficiency: artifact collections use markdown index tables (one-line description and trigger conditions) so the agent can locate what it needs without reading everything.<p>Decision capture: decisions made during AI reasoning and human feedback are persisted as a structured artifact, to make them reviewable, traceable, and consistently applied across sessions.<p>Waterfall-ish flow: sequential phases with defined outputs. Tedious for human teams, but AI agents don&#x27;t mind the overhead, and the explicit structure prevents the unconstrained &quot;just start vibecoding&quot; failure mode.<p>How I use it:<p>Short, focused sessions. Each session invokes one skill, produces its output, and ends. The knowledge organization means the next session picks up without losing context. I&#x27;ve found that free-form prompting between skills is usually a sign the workflow is missing a piece.<p>Current limitations:<p>I haven&#x27;t found a good way to integrate Figma MCP for importing existing UI&#x2F;UX designs into the workflow. Suggestions welcome.<p>Feedback, criticism, and contributions are very welcome!

[Other] AI Team OS – Turn Claude Code into a Self-Managing AI Team

[CLI Tool] purl: a curl-esque CLI for making HTTP requests that require payment