🛠️ All DevTools

[Other] How Claude Code works in large codebases

[Other] LLM Policy for Rust Compiler

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

[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

[Other] Show HN: Browse 61 3D Printable Robots Robotics is advancing really fast lately, with AI inference, different controllers, software, and parts always changing. I wanted a place that supports many device types, Raspberry Pi, NVDA Jetson, Arduino, ESP32, hardware sources, and maximizes for printability. Instructables, Github, and Thingiverse are currently popular but aren&#x27;t really focused on robotics, So I built orobot.io to try and make printing robots as standardized and accessible as possible. It uses a lot of Agent built content custom to each project, and every project is designed to be used by humans or your agent.<p>Features:<p>- Photos and Estimated Prices for all projects<p>- Links back to source GitHub projects<p>- LLMs write descriptions and tips on how to build<p>- View + Download 3d printable STL files in browser<p>- BOM purchase links are kept up to date with LLMs checking Amazon link health<p>- LLMs write Javascript install and controller wrappers custom to each project so a single one-click install works across many frameworks and controller types<p>- Public skill files, clis, and prompts let your agent do everything it needs to walk you through the complexity.<p>It&#x27;s still pretty new, so somethings are broken, and there&#x27;s a lot more I want to build. But I&#x27;m very interested to have people try it out let me know if they want to use something like this and give me feedback about where they ran into problems so I can fix it. Thank you HN!

[Database] Show HN: Aion a Rust Database

[Other] Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA

[Other] Suite of reference architectures for building GPU-accelerated vision agents and AI-powered video analytics applications.

[Other] Show HN: Running the second public ODoH relay Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple&#x27;s iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.

[Other] A Claude Code and Codex Skill for Deliberate Skill Development

[Other] Show HN: Nibble An attempt at a single pass LLVM frontend in ~3000 lines of C without external dependencies, malloc, or an AST. Included are some graphical examples. The IR isn&#x27;t perfect, and the README touches on one particular downfall

[API/SDK] Show HN: Claude-pee: use Claude -p without the programmatic usage credit pool Anthropic announced today that starting June 15, paid Claude plans get a separate monthly credit pool for programmatic usage (claude -p, Agent SDK). Seems OK at first glance but it turns out the monthly credit pool is charged at API rates, which effectively kills any serious programmatic usage for hobbyists.<p>This is a small Claude Code wrapper which runs claude in a PTY, injecting input, finding the session transcript jsonl file, and using a stop hook to determine when claude is done.<p>It&#x27;s a drop-in replacement for claude code. All arguments except the &quot;-p&quot; argument are forwarded as-is.<p>claude-pee -p &quot;hello world&quot;<p>Could be used as a cheap way of using Anthropic subscriptions for OpenClaw, Hermes, etc.<p>Written in Rust, MIT license.

[Other] Show HN: Neural window manager, neural network moving windows from mouse actions I&#x27;d been mulling over this crazy idea for a while. Can programs be generated? Inspired by recent advances in world models, I wondered if we could do away with source code and generate pixels directly and interactively.<p>As an experiment to answer this, I set out to create a neural window manager, training a neural network to predict what the screen would look like next.<p>Basically, the idea was to generate the next frame based on the last two frames and the mouse position. That&#x27;s it: moving windows without programming an event system, just a simple convolutional neural network guessing pixels.<p>To implement the experiment, I used Pygame to simulate a turquoise desktop background, a gray window with a navy blue title bar, a white cursor, and four colors in total. Then, a bot randomly dragged the window, and I recorded everything, processing the frames as color index matrices (not RGB, to avoid complications) and the mouse delta (dx, dy, click) that caused each transition. 8000 frames, a few minutes in Colab.<p>The model is a unitary neural network (UNET). The encoder compresses the stacked frames, the decoder reconstructs the next one, and the mouse vector coordinates are projected with a linear layer to fit the spatial size of the bottleneck. There, they are concatenated before decoding, so that motion information feeds each jump connection.<p>And it works! Which still surprises me a little. You can drag, and the window follows you; when you release, it stops. There&#x27;s no internal state, no (x, y) coordinates anywhere. The model infers the position from what it sees, which works until it doesn&#x27;t. But after a couple of seconds of strange movement, the window starts to distort.<p>This will probably improve with more computing power for training and more examples, but to narrow the scope of the experiment and test it within a web browser, I decided to abandon the rendering aspect and have the model predict primitives instead of pixels, simply converting the motion engine into a neural network.<p>Basically, I trained a small MLP to receive (distance to the title bar, distance to the resize point, click) and generate (dx, dy, dw, dh), with two separate heads: one for moving and one for resizing. The trick is that they share nothing except the click signal, so the model can&#x27;t confuse dragging with resizing. I then exported it to ONNX as well, and now everything runs in the browser, without a server, just a canvas element and two small neural networks communicating with each other.<p>With this new approach, the renderer remains deterministic, with rectangles drawn in JavaScript, but the window&#x27;s behavior (where it moves, how it resizes) is learned from examples. It feels like a peculiar middle ground between traditional and neural, so you can feel the space the network has learned by interacting with it: dragging near the title bar moves it, but approaching the corner resizes the window. There are no conditionals or hitbox code; the network simply learned where those areas are from examples.<p>Sometimes it gets confused near the edges, which, frankly, is more interesting than if it worked perfectly; you can perceive how the probability changes. This makes sense when you think about it, because no (x, y) coordinates are stored in these models; the position is implied in the activations. It works well for short sequences, but fails when asked to maintain state over time.<p>Update: A few weeks later, Meta published the Neural Computers article (2604.06425, it&#x27;s worth reading). The premise is the same, but they go much further: cli and uis, real programs. Their failure modes are practically identical to those I found with the pure pixel version: &quot;challenges persist with routine reuse, controlled updates, and symbolic stability.&quot; which is a fancy way of saying that the window blurs after a few seconds (that was the reason for choosing deterministic rendering).

[Database] Launch HN: Ardent (YC P26) – Postgres sandboxes in seconds with zero migration Hey HN! We’re Vikram and Evan from Ardent (<a href="https:&#x2F;&#x2F;tryardent.com">https:&#x2F;&#x2F;tryardent.com</a>). We&#x27;re building database sandboxes for you and your coding agents.<p>In the last two years coding agents have gotten dramatically more capable at handling complex engineering tasks. But without access to a realistic sandbox at the DB layer for testing, they ship garbage that can take down production databases. I spent over a year building an AI Data Engineer that failed for this exact reason. Evan spent the last 12 years in data engineering and hit this wall building agents at his last company.<p>Ardent was built to make it possible for coding agents to get near instant access to production-like sandboxes so they can test their work. To do this we write a replication stream out of the target DB, scaling with kafka onto a read replica with copy on write enabled and autoscaling compute (we currently prefer neon as a primary branching engine due to their implementation of these properties).<p>Our replication stream uses logical replication + ddl triggers to enable usage on any hosted postgres DB since most platforms do not allow physical replication which is traditionally used for creating replicas.<p>This provides a few primary benefits:<p>1. Does not require a platform migration to a DB provider like neon, allowing strong separation of production and development concerns. 2. Minimal impact on the production database while allowing clones to spin up in &lt;6s, even at TB scale with copy-on-write<p>Security matters a lot with cloning production so we run a proxy layer to generate custom postgres URLs and route all connections to allow more granular access control to clones, prevent credential leak, and follow a split plane architecture to allow full data residency on your cloud through BYOC.<p>We also support anonymization through the ability to register SQL that runs on branches before they are returned. This has been used for PII redaction and branch modification.<p>Our goal is to make every data infrastructure platform “cloneable” in one place so agents can fully test the impact of their changes on production like data environments without risk.<p>Here&#x27;s a demo of it: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;5S1kwPtiRU0" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;5S1kwPtiRU0</a><p>We’d love to understand how you work with coding agents on the DB and if you try Ardent (it&#x27;s free to get started) what worked, what broke and what’s missing.

[DevOps] Show HN: Mistle – Open-source infrastructure for running sandboxed coding agents Hi HN, I&#x27;m Jonathan. My co-founder, Thomas, and I started building Mistle in Feb.<p>We saw larger tech companies like Ramp (Inspect) and Stripe (Minions) build this internally and thought an open source version should exist.<p>We made a few very intentional decisions when working on this:<p>1. Credentials are kept out of the sandbox. Authorized access goes through a proxy, so agents do not directly receive credentials.<p>2. The harness is not our problem. We&#x27;re not going to tackle things like memory, self-learning.<p>3. No magic. Configurations are explicit. You can bring your own keys for models, sandboxes, and other providers. You can write your own instructions and agent.<p>Mistle can be run locally with a single command: <a href="https:&#x2F;&#x2F;github.com&#x2F;mistlehq&#x2F;mistle#run-mistle-locally" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mistlehq&#x2F;mistle#run-mistle-locally</a><p>Questions, feedback and ideas are welcome!

[Other] Show HN: Promptcellar – capture every Claude Code prompt as JSONL in your repo

[Other] Show HN: Rotunda - A browser built for agents with simulated typing Hi HN! Pierce here.<p>Rotunda is a firefox fork primarily intended for agent use, which I’ve been hacking on nights&#x2F;weekends.<p>There was a [lengthy](<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48024859">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48024859</a>) discussion last week on how expensive computer use models are. The cost is going to drop eventually, but I think on some level it&#x27;s still usually the wrong primitive. The web gives us access to beautiful structured formats, plaintext, etc... why throw that away if we don&#x27;t have to?<p>I realized at some point that for 99% of automations I just want agents to be able to control my Chrome instance. But that’s easier said that done: CDP (the Chrome automation protocol) leaks a ton of state about being programmatically controlled, either by toggling window attributes or by running `page.evaluate()` commands right in the page context. Plus if you look at an automation running it&#x27;s pretty obvious what happens: the mouse jumps around, fields are filled instantly, etc.<p>Rotunda tries to fix this. Its standout features:<p>- Realistic simulation of mouse movements and keyboard commands, powered by a trained RNN on my own timing patterns from the last week. (still feel weird about opting-in to a key logger but whatever)<p>- Doesn’t lie about its host specs, only fibs about some client side details. Stealth browsers are too easy to flag statistically when you’re adding noise to canvas pixels or audio pipelines.<p>- It runs on your local device with a CLI or Playwright API accessible to Claude, Codex, or whatever your harness-de-jure today looks like.<p>- Patches modern Firefox (150) with an agentic harness to keep this updated over time<p>MPL-2.0 on GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;monkeysee-ai&#x2F;rotunda" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;monkeysee-ai&#x2F;rotunda</a><p>Longer writeup on the design choices: <a href="https:&#x2F;&#x2F;pierce.dev&#x2F;notes&#x2F;a-browser-for-agents" rel="nofollow">https:&#x2F;&#x2F;pierce.dev&#x2F;notes&#x2F;a-browser-for-agents</a><p>Also check out the demo on the site! <a href="https:&#x2F;&#x2F;www.rotunda.sh&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.rotunda.sh&#x2F;</a><p>Pretty excited by how this turned out but we’re still super early. Give it a try and please flag any issues!