🛠️ All DevTools

[CLI Tool] Show HN: Gemini can now natively embed video, so I built sub-second video search Gemini Embedding 2 can project raw video directly into a 768-dimensional vector space alongside text. No transcription, no frame captioning, no intermediate text. A query like &quot;green car cutting me off&quot; is directly comparable to a 30-second video clip at the vector level.<p>I used this to build a CLI that indexes hours of footage into ChromaDB, then searches it with natural language and auto-trims the matching clip. Demo video on the GitHub README. Indexing costs ~$2.50&#x2F;hr of footage. Still-frame detection skips idle chunks, so security camera &#x2F; sentry mode footage is much cheaper.

[Other] Show HN: Running AI agents across environments needs a proper solution Hi HN folks,<p>I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory.<p>This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future?<p>I do not think so.<p>Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs already add significant latency, so adding heavy runtime overhead on top only makes things worse. Existing solutions start to fall apart here.<p>Agents built in Python also tend to have a large memory footprint, which becomes a serious problem when you want to scale to thousands of agents.<p>And open-source for agents is still not where it should be. Right now, I cannot easily reuse agents built by domain experts the same way I reuse open-source software.<p>These issues bothered me, and I realized that if agents are ever going to be democratized, they need to be open and easy to use. Just like Docker solved system dependencies, we need something similar for agents.<p>That is why I started building an agent framework in Rust. It is modular and follows the principle of true agency: an agent is an entity with tools, memory, and an executor. In AutoAgents, users can independently create and modify tools, executors, and memory.<p>With AutoAgents, I saw that powerful agents could be built without compromising on performance or memory the way many other frameworks do.<p>But the other problems still remained: re-sharing agents, sandboxing, and scaling to thousands of agents.<p>So I created Odyssey — a bundle-first agent runtime written in Rust on top of AutoAgents, the Rust agent framework. It lets you define an agent once, package it as a portable artifact, and run it through the same execution model across local development, embedded SDK usage, shared runtime servers, and terminal workflows.<p>Both AutoAgents and Odyssey are fully open source and built in Rust, and I am planning to build an Odyssey Agent Hub soon, with additional features like WASM tools, custom memory layers, and more.<p>My vision is to democratize agents so they are available to everyone — securely and performantly. Being open is not enough; agents also need to be secure.<p>The project is still in alpha, but it is in a working state.<p>AutoAgents Repo -&gt; <a href="https:&#x2F;&#x2F;github.com&#x2F;liquidos-ai&#x2F;AutoAgents" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;liquidos-ai&#x2F;AutoAgents</a><p>Odyssey Repo -&gt; <a href="https:&#x2F;&#x2F;github.com&#x2F;liquidos-ai&#x2F;Odyssey" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;liquidos-ai&#x2F;Odyssey</a><p>I would really appreciate feedback — especially from anyone who has dealt with similar problems. Your feedback help me shape the product.<p>Thanks for your time in advance!

[Package Manager] Nanobrew: The fastest macOS package manager compatible with brew

[CLI Tool] Show HN: ProofShot – Give AI coding agents eyes to verify the UI they build I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors.<p>So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds.<p>proofshot start --run &quot;npm run dev&quot; --port 3000 # agent navigates, clicks, takes screenshots proofshot stop<p>It works with whatever agent you use (Claude Code, Cursor, Codex, etc.) — it’s just shell commands. It&#x27;s packaged as a skill so your AI coding agent knows exactly how it works. It&#x27;s built on agent-browser from Vercel Labs which is far better and faster than Playwright MCP.<p>It’s not a testing framework. The agent doesn’t decide pass&#x2F;fail. It just gives me the evidence so I don’t have to open the browser myself every time.<p>Open source and completely free.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;AmElmo&#x2F;proofshot" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;AmElmo&#x2F;proofshot</a>

[Other] Ripgrep is faster than grep, ag, git grep, ucg, pt, sift (2016)

[Other] Log File Viewer for the Terminal

[Other] Sunsetting the Techempower Framework Benchmarks

[Other] Claude Code Cheat Sheet

[Other] How I'm Productive with Claude Code

[Other] Show HN: Burn Room – ephemeral SSH chat, messages burn after 1 hour I built Burn Room — a self-hosted SSH chat server where messages burn after 1 hour and rooms auto-destruct after 24 hours. Nothing is written to disk. No account, no email, no browser required.<p><pre><code> ssh guest@burnroom.chat -p 2323 password: burnroom </code></pre> Or connect from a browser (xterm.js web terminal): <a href="https:&#x2F;&#x2F;burnroom.chat" rel="nofollow">https:&#x2F;&#x2F;burnroom.chat</a>

[Other] Show HN: Shrouded, secure memory management in Rust Hi HN!<p>I&#x27;ve been building a project that handles high-value credentials in-process, and I wanted something more robust than just zeroing memory on drop. A comment on a recent Show HN[0] made me realize that awareness of lower-level memory protection techniques might not be as widespread as I thought.<p>The idea here is to pull out all the tools in one crate, with a relatively simple API. * mlock&#x2F;VirtualLock to prevent sensitive memory from being swapped (eg the KeePass dump) * Core dump exclusion using MADV_DONTDUMP on Linux &amp; Android * mprotect to minimize exposure over time * Guard pages to mitigate under&#x2F;overflows<p>After some battle testing, the goal here is to provide a more secure memory foundation for things like password managers and cryptocurrency wallets.<p>This was a fun project, and I learned a lot - would love any feedback!<p>[0] - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47073430">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47073430</a>

[IDE/Editor] Show HN: JulIDE – Lightweight Julia IDE Built with Tauri Hi HN! I built JulIDE - a lightweight Julia IDE using Tauri and Rust. Features: 10MB install (vs VSCode&#x27;s 300MB) Full LSP, debugger, Git integration Built-in dev containers with X11 forwarding Tauri&#x2F;Rust backend, React frontend Monaco based editor It&#x27;s beta but functional. Built this for fun and to solve a real need in the Julia community. Tech stack: Tauri 2, Rust, React, Monaco, LanguageServer.jl GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;sinisterMage&#x2F;JulIde" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sinisterMage&#x2F;JulIde</a> Would love feedback!

[Other] Outworked – An Open Source Office UI for Claude Code Agents

[Other] Show HN: Cq – Stack Overflow for AI coding agents Hi all, I&#x27;m Peter at Staff Engineer and Mozilla.ai and I want to share our idea for a standard for shared agent learning, conceptually it seemed to fit easily in my mental model as a Stack Overflow for agents.<p>The project is trying to see if we can get agents (any agent, any model) to propose &#x27;knowledge units&#x27; (KUs) as a standard schema based on gotchas it runs into during use, and proactively query for existing KUs in order to get insights which it can verify and confirm if they prove useful.<p>It&#x27;s currently very much a PoC with a more lofty proposal in the repo, we&#x27;re trying to iterate from local use, up to team level, and ideally eventually have some kind of public commons.<p>At the team level (see our Docker compose example) and your coding agent configured to point to the API address for the team to send KUs there instead - where they can be reviewed by a human in the loop (HITL) via a UI in the browser, before they&#x27;re allowed to appear in queries by other agents in your team.<p>We&#x27;re learning a lot even from using it locally on various repos internally, not just in the kind of KUs it generates, but also from a UX perspective on trying to make it easy to get using it and approving KUs in the browser dashboard. There are bigger, complex problems to solve in the future around data privacy, governance etc. but for now we&#x27;re super focussed on getting something that people can see some value from really quickly in their day-to-day.<p>Tech stack:<p>* Skills - markdown<p>* Local Python MCP server (FastMCP) - managing a local SQLite knowledge store<p>* Optional team API (FastAPI, Docker) for sharing knowledge across an org<p>* Installs as a Claude Code plugin or OpenCode MCP server<p>* Local-first by default; your knowledge stays on your machine unless you opt into team sync by setting the address in config<p>* OSS (Apache 2.0 licensed)<p>Here&#x27;s an example of something which seemed straight forward, when asking Claude Code to write a GitHub action it often used actions that were multiple major versions out of date because of its training data. In this case I told the agent what I saw when I reviewed the GitHub action YAML file it created and it proposed the knowledge unit to be persisted. Next time in a completely different repo using OpenCode and an OpenAI model, the cq skill was used up front before it started the task and it got the information about the gotcha on major versions in training data and checked GitHub proactively, using the correct, latest major versions. It then confirmed the KU, increasing the confidence score.<p>I guess some folks might say: well there&#x27;s a CLAUDE.md in your repo, or in ~&#x2F;.claude&#x2F; but we&#x27;re looking further than that, we want this to be available to all agents, to all models, and maybe more importantly we don&#x27;t want to stuff AGENTS.md or CLAUDE.md with loads of rules that lead to unpredictable behaviour, this is targetted information on a particular task and seems a lot more useful.<p>Right now it can be installed locally as a plugin for Claude Code and OpenCode:<p>claude plugin marketplace add mozilla-ai&#x2F;cq claude plugin install cq<p>This allows you to capture data in your local ~&#x2F;.cq&#x2F;local.db (the data doesn&#x27;t get sent anywhere else).<p>We&#x27;d love feedback on this, the repo is open and public - so GitHub issues are welcome. We&#x27;ve posted on some of our social media platforms with a link to the blog post (below) so feel free to reply to us if you found it useful, or ran into friction, we want to make this something that&#x27;s accessible to everyone.<p>Blog post with the full story: <a href="https:&#x2F;&#x2F;blog.mozilla.ai&#x2F;cq-stack-overflow-for-agents&#x2F;" rel="nofollow">https:&#x2F;&#x2F;blog.mozilla.ai&#x2F;cq-stack-overflow-for-agents&#x2F;</a> GitHub repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;mozilla-ai&#x2F;cq" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mozilla-ai&#x2F;cq</a><p>Thanks again for your time.

[Other] Show HN: Threadprocs – executables sharing one address space (0-copy pointers) This project launches multiple independent programs into a single shared virtual address space, while still behaving like separate processes (independent binaries, globals, and lifetimes). When threadprocs share their address space, pointers are valid across them with no code changes for well-behaved Linux binaries.<p>Unlike threads, each threadproc is a standalone and semi-isolated process. Unlike dlopen-based plugin systems, threadprocs run traditional executables with a `main()` function. Unlike POSIX processes, pointers remain valid across threadprocs because they share the same address space.<p>This means that idiomatic pointer-based data structures like `std::string` or `std::unordered_map` can be passed between threadprocs and accessed directly (with the usual data race considerations).<p>This accomplishes a programming model somewhere between pthreads and multi-process shared memory IPC.<p>The implementation relies on directing ASLR and virtual address layout at load time and implementing a user-space analogue of `exec()`, as well as careful manipulation of threadproc file descriptors, signals, etc. It is implemented entirely in unprivileged user space code: &lt;<a href="https:&#x2F;&#x2F;github.com&#x2F;jer-irl&#x2F;threadprocs&#x2F;blob&#x2F;main&#x2F;docs&#x2F;02-implementation.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jer-irl&#x2F;threadprocs&#x2F;blob&#x2F;main&#x2F;docs&#x2F;02-imp...</a>&gt;.<p>There is a simple demo demonstrating “cross-threadproc” memory dereferencing at &lt;<a href="https:&#x2F;&#x2F;github.com&#x2F;jer-irl&#x2F;threadprocs&#x2F;tree&#x2F;main?tab=readme-ov-file#demo" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jer-irl&#x2F;threadprocs&#x2F;tree&#x2F;main?tab=readme-...</a>&gt;, including a high-level diagram.<p>This is relevant to systems of multiple processes with shared memory (often ring buffers or flat tables). These designs often require serialization or copying, and tend away from idiomatic C++ or Rust data structures. Pointer-based data structures cannot be passed directly.<p>There are significant limitations and edge cases, and it’s not clear this is a practical model, but the project explores a way to relax traditional process memory boundaries while still structuring a system as independently launched components.

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

[Other] I wrote a 750-page guide to self-hosting production apps

[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: I made a tool for converting text snippets to shareable image Hey guys, I needed a way to convert Code, CSV, and Markdown snippets to shareable images so I built this over the weekend. It all runs locally in the browser using a few lines of jQuery. Nothing fancy.<p>Sharing in case someone else might find it useful!