🛠️ All DevTools

[Other] Show HN: MoebiusXBIN – ASCII and text-mode art editor with custom font support

[Other] Blog series on creating an OS in Rust

[CLI Tool] Show HN: Claude Code in the Browser – Webcode.sh I wanted to be able to code with Claude anytime, anywhere — walking, commuting, or at a café. So I built a browser-based terminal for Claude Code!<p>- Zero-config, instant REPL<p>- Works on mobile and tablets (Chrome, Safari)<p>- WASM-powered performance<p>Let me know what you think.

[Other] Track your starred GitHub repos and issues — stay updated. DevNotify is an open-source platform that helps developers stay updated on their starred GitHub projects. Monitor your open issues Discover new contribution opportunities Perfect for open-source enthusiasts, contributors, and maintainers.

[Other] Mini EHR for Clinicians AI-native clinical workspace built in 20 hours using Next.js, Firebase & Genkit. Features patient onboarding, SOAP notes, scheduling & 6 AI agents for diagnosis, MEAT audit, gaps, meds, CEA tracking & survivorship. Built from curiosity, not for scale.

[DevOps] AI-powered Autonomous Infrastructure Platform StackGen's AI-powered Autonomous Infrastructure Platform combines automation with enterprise-grade reliability to deliver self-building, self-governing, self-healing, and self-optimizing infrastructure capabilities.

[Other] A simple mapping tool right in your browser A lightweight Chrome extension that lets you draw, import, edit, and export geospatial data directly on interactive maps. Supports GeoJSON, KML, KMZ, and GPX formats. Ideal for planners, GIS users, and anyone working with map-based data.

[Other] Fixing Ctrl+C in Rust terminal apps: Child process management

[Other] Show HN: A GitHub Action that quizzes you on a pull request A little idea I got from playing with AI SWE Agents. Can AI help make sure we understand the code that our AIs write?<p>PR Quiz uses AI to generate a quiz from a pull request and blocks you from merging until the quiz is passed. You can configure various options like the LLM model to use, max number of attempts to pass the quiz or min diff size to generate a quiz for. I found that the reasoning models, while more expensive, generated better questions from my limited testing.<p>Privacy: This GitHub Action runs a local webserver and uses ngrok to serve the quiz through a temporary url. Your code is only sent to the model provider (OpenAI).

[Other] Pseudo, a Common Lisp macro for pseudocode expressions

[Other] Show HN: The easiest accessibility (a11y) checker for VSCode

[Other] Show HN: Xorq – open compute catalog for AI Hi HN, Hussain and Dan from Xorq here.<p>After years of struggling with scaling compute that worked in notebooks but failed in production, we decided to do something about it. Data has standards like Iceberg and Delta. But compute is still a mess—trapped in notebooks, duplicated effort across teams, or baked into custom Airflow DAGs. We think of Xorq as the missing analog to Apache Iceberg, but for compute.<p>We’ve spent the last year building Xorq, an *compute catalog* that helps teams *reuse, ship, and observe* transformations, features, models, and pipelines across engines.<p>Xorq is built on:<p>- *Arrow Flight* (`do_exchange`) for high-speed data transport - *Ibis* for cross-engine expression trees, serialized to YAML - A portable UDF engine that compiles pipelines to SQL or Python - `uv` to make Python environments fully reproducible<p>Xorq features:<p>- pandas-style declarative transformations, backed by Ibis - Multi-engine execution (e.g., DuckDB, Snowflake) - UDFs as portable Flight endpoints - Serveable transforms by way of flight_udxf operator - Built-in caching and lineage tracking - Diff-able YAML artifacts, great for CI&#x2F;CD<p>Xorq use cases:<p>Since our last major release, it’s been exciting to see the first Xorq use-cases show up in the wild. All with *Python simplicity and SQL-scale performance*.<p>- Feature Stores (<a href="https:&#x2F;&#x2F;www.xorq.dev&#x2F;blog&#x2F;featurestore-to-featurehouse" rel="nofollow">https:&#x2F;&#x2F;www.xorq.dev&#x2F;blog&#x2F;featurestore-to-featurehouse</a>) - Semantic Layers (e.g. <a href="https:&#x2F;&#x2F;github.com&#x2F;boringdata&#x2F;boring-semantic-layer">https:&#x2F;&#x2F;github.com&#x2F;boringdata&#x2F;boring-semantic-layer</a>) - MCP + ML Integration (<a href="https:&#x2F;&#x2F;docs.xorq.dev&#x2F;vignettes&#x2F;mcp_flight_server" rel="nofollow">https:&#x2F;&#x2F;docs.xorq.dev&#x2F;vignettes&#x2F;mcp_flight_server</a>)<p>We’re open source and learning fast. Would love feedback on what’s useful or missing. Thanks in advance for trying it out!<p>Check out the demo of the Xorq CLI tool in action: <a href="https:&#x2F;&#x2F;asciinema.org&#x2F;a&#x2F;730484" rel="nofollow">https:&#x2F;&#x2F;asciinema.org&#x2F;a&#x2F;730484</a><p>---<p>Get Started<p>- Github: <a href="https:&#x2F;&#x2F;github.com&#x2F;xorq-labs&#x2F;xorq">https:&#x2F;&#x2F;github.com&#x2F;xorq-labs&#x2F;xorq</a> - Xorq docs: <a href="https:&#x2F;&#x2F;docs.xorq.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;docs.xorq.dev&#x2F;</a> ---<p>Sneak peak - Xorq Compute Catalog UI Console:<p>Check out this interactive Claude demo showing how the Xorq compute catalog can be visualized to accelerate composition, reuse, and troubleshooting of AI compute: <a href="https:&#x2F;&#x2F;claude.ai&#x2F;public&#x2F;artifacts&#x2F;d2f00d2a-a3f9-4032-884e-d22f620a0ccf?fullscreen=true" rel="nofollow">https:&#x2F;&#x2F;claude.ai&#x2F;public&#x2F;artifacts&#x2F;d2f00d2a-a3f9-4032-884e-d...</a>

[Other] Show HN: ELF Injector The ELF Injector allows you to &quot;inject&quot; arbitrary-sized relocatable code chunks into ELF executables. The code chunks will run before the original entry point of the executable runs.<p>Included in the project are sample chunks as well as a step-by-step tutorial on how it works.<p>It&#x27;s a mix of C and assembly and currently runs on 32-bit ARM though it&#x27;s easy to port to other architectures.

[Other] Structuring large Clojure codebases with Biff

[Other] Show HN: Wush-Action – SSH into GitHub Actions over WireGuard

[Other] Show HN: Same prompt tested across Replit, Bolt, v0, Lovable and Raq.com Hi HN,<p>I built Raq.com – a platform that uses Claude Code to build working internal tools directly in the browser.<p>Claude Code is great at self correcting when given the right tools.<p>I&#x27;ve found that the popular web-based AI coding tools look great in demos but fail on real API integrations or require a lot of error back and forth. They don&#x27;t appear to do much research or self-correcting, likely to reduce spend. I wanted to see the current state of these tools, so I ran the same prompt on five platforms (Replit, Bolt, v0, Lovable, and Raq.com) to build a tool that requires 3 different APIs (Companies House, FinUK and OpenRouter) working together.<p>Four platforms produced broken prototypes or needed manual fixes. Raq.com delivered a complete working solution from a single prompt (that can be deployed to live with one click).<p>Full test with videos: <a href="https:&#x2F;&#x2F;raq.com&#x2F;real-world-test" rel="nofollow">https:&#x2F;&#x2F;raq.com&#x2F;real-world-test</a><p>We&#x27;re in early access (requires Claude Pro&#x2F;Max for free usage) - we&#x27;re looking non-coders who would like to build internal tools for their team.<p>Some technical info:<p>- Raq.com provisions isolated dev and prod Docker environments for each company (companyname.raq.com and companyname-dev.raq.com).<p>- The dev site includes a persistent terminal streamed to the browser, so the session continues even while tab is closed.<p>- CLAUDE.md file provides best practices, known pitfalls, and coding patterns for the Laravel + Filament stack.<p>- Self-Correction Loop: Claude can test and debug its own work. It has direct shell access to a custom script that bundles PHPUnit, syntax checks, and cache clearing. Plus a Playwright wrapper to check for errors and take screenshots.<p>- A single click runs a script that rsync&#x27;s the dev workspace to the prod container, runs migrations, and clears caches.

[IDE/Editor] Lightning-fast and Powerful Code Editor written in Rust

[Other] The ultimate LLM/AI application development framework in Golang.

[Other] Show HN: Terminal-Bench-RL: Training Long-Horizon Terminal Agents with RL After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal&#x2F;coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford&#x27;s Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good!<p>*What I did*:<p>- Created a Claude Code-inspired agent (system msg + tools)<p>- Built Docker-isolated GRPO training where each rollout gets its own container<p>- Developed a multi-agent synthetic data pipeline to generate &amp; validate training data with Opus-4<p>- Implemented a hybrid reward signal of unit test verifiers &amp; a behavioural LLM judge.<p>*Key results*:<p>- My untrained Qwen3-32B agent achieved 13.75% on Terminal-Bench (#19, beats Stanford&#x27;s Qwen3-235B MoE)<p>- I tested training to work stably on 32x H100s distributed across 4 bare metal nodes<p>- I created a mini-eval framework for LLM-judge performance. Sonnet-4 won.<p>- ~£30-50k needed for full training run of 1000 epochs (I could only afford testing )<p>*Technical details*:<p>- The synthetic dataset ranges from easy to extremely hard tasks. An example hard task&#x27;s prompt:<p>&quot;I found this mystery program at `&#x2F;app&#x2F;program` and I&#x27;m completely stumped. It&#x27;s a stripped binary, so I have no idea what it does or how to run it properly. The program seems to expect some specific input and then produces an output, but I can&#x27;t figure out what kind of input it needs. Could you help me figure out what this program requires?&quot;<p>- Simple config presets allow training to run on multiple hardware setups with minimal effort.<p>- GRPO used with 16 rollouts per task, up to 32k tokens per rollout.<p>- Agent uses XML&#x2F;YAML format to structure tool calls<p>*More details*:<p>My Github repos open source it all (agent, data, code) and has way more technical details if you are interested!:<p>- Terminal Agent RL repo<p>- Multi-agent synthetic data pipeline repo<p>I thought I would share this because I believe long-horizon RL is going to change everybody&#x27;s lives, and so I feel it is important (and super fun!) for us all to share knowledge around this area, and also have enjoy exploring what is possible.<p>Thanks for reading!<p>Dan<p>(Built using rLLM RL framework which was brilliant to work with, and evaluated and inspired by the great Terminal Bench benchmark)

[Other] Show HN: Rewindtty – Record and replay terminal sessions as structured JSON