🛠️ All DevTools

[Other] An Open-Source Asynchronous Coding Agent

[Other] Show HN: N0x – LLM inference, agents, RAG, Python exec in browser, no back end Built this because I was tired of every AI tool shipping my data to someone else server n0x runs the full stack LLM inference via WebGPU, autonomous ReAct agents, RAG over your own docs, sandboxed Python execution via Pyodide all inside a single browser tab. No account No keys No backend Models download once, cache in IndexedDB permanently. Biggest challenge was context window budgeting for the agent loop and making the WASM vector search non-blocking. Happy to talk architecture.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;ixchio&#x2F;n0x" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ixchio&#x2F;n0x</a> | Live demo: <a href="https:&#x2F;&#x2F;n0x-three.vercel.app" rel="nofollow">https:&#x2F;&#x2F;n0x-three.vercel.app</a>

[Other] Launch an autonomous AI agent with sandboxed execution in 2 lines of code

[CLI Tool] Show HN: Horizon – GPU-accelerated infinite-canvas terminal in Rust Tabs, splits, and tmux work fine until you have several projects open with logs, tests, and long-running shells. I kept rebuilding context instead of resuming work. Horizon puts shells on an infinite canvas. You can arrange them into workspaces and reopen later with layout, scrollback, and history intact.<p>Built in 3 days with Claude&#x2F;Codex, dogfooding the workflow as I went. Feedback and contributions welcome.

[Other] Edge.js: Run Node apps inside a WebAssembly sandbox

[Build/Deploy] Show HN: Flowershow Publish Markdown in seconds. Hosted, free, zero config I&#x27;m Rufus, one of the founders of Flowershow. We love markdown and use it everywhere from making websites, to docs, to knowledgebases. Plus AI splits it everywhere now.<p>Got tired of the framework&#x2F;config&#x2F;deploy overhead every time we wanted to share a file or put a site online.<p>So we built the thing we wanted. Files in. Website out. &quot;Vercel for Content&quot; is our aspiration - make deploying (markdown) content as fast, seamless and easy as Vercel did for JS.<p>Command line plus you can connect to github repos, use Obsidian via plugin, or drag and drop files.<p><pre><code> npm i -g @flowershow&#x2F;publish publish .&#x2F;my-notes # → https:&#x2F;&#x2F;your-site.flowershow.app live in seconds </code></pre> Flowershow is <i>fully hosted</i> — no server, no build pipeline, no CI&#x2F;CD. Point it at a Markdown folder and get a URL.<p>Full Obsidian syntax: wiki links, callouts, graph view, frontmatter<p>GFM, Mermaid, LaTeX: diagrams and math render natively<p>Themes via Tailwind &amp; CSS variables: Tailwind out of the box. Customize without a build step<p>Supports HTML: use HTML, images etc.<p>~7k Obsidian plugin installs, 1,400 users, 1,100 sites. Free forever for personal use. Premium ($5&#x2F;mo) adds custom domains, search, and password protection.<p>And it&#x27;s open source: <a href="https:&#x2F;&#x2F;github.com&#x2F;flowershow&#x2F;flowershow" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;flowershow&#x2F;flowershow</a><p>Check it out and let us know what you think and what we can improve

[Other] Show HN: Unsloth Studio - Local Fine-tuning, Chat UI Hey HN! We&#x27;re excited to release Unsloth Studio - a culmination of many things we wanted to provide to the community - it includes:<p>1. A Chat UI which has auto healing tool calling, Python &amp; bash code execution, web search, image, docs input + more!<p>2. Finetuning of audio, vision, LLMs with an Auto AI Assist data prep<p>3. Supports GGUFs, Mac, Windows, Linux + audio gen<p>4. Has SVG rendering in browser, exporting to GGUF<p>5. gpt-oss harmony rendering, all inference parameters are pre-set and recommended<p>6. Data designer + synthetic data generation<p>7. Fast parallel data prep + embedding finetuning<p>8. And much much more!<p>To get it, run:<p>pip install unsloth<p>unsloth studio setup<p>unsloth studio -H 0.0.0.0 -p 8888<p>Suggestions are welcome, and we&#x27;re excited for contributions and for you all to try it out! Appreciate you all!

[Database] Show HN: Antfly: Distributed, Multimodal Search and Memory and Graphs in Go Hey HN, I’m excited to share Antfly: a distributed document database and search engine written in Go that combines full-text, vector, and graph search. Use it for distributed multimodal search and memory, or for local dev and small deployments.<p>I built this to give developers a single-binary deployment with native ML inference (via a built-in service called Termite), meaning you don&#x27;t need external API calls for vector search unless you want to use them.<p>Some things that might interest this crowd:<p>Capabilities: Multimodal indexing (images, audio, video), MongoDB-style in-place updates, and streaming RAG.<p>Distributed Systems: Multi-Raft setup built on etcd&#x27;s library, backed by Pebble (CockroachDB&#x27;s storage engine). Metadata and data shards get their own Raft groups.<p>Single Binary: antfly swarm gives you a single-process deployment with everything running. Good for local dev and small deployments. Scale out by adding nodes when you need to.<p>Ecosystem: Ships with a Kubernetes operator and an MCP server for LLM tool use.<p>Native ML inference: Antfly ships with Termite. Think of it like a built-in Ollama for non-generative models too (embeddings, reranking, chunking, text generation). No external API calls needed, but also supports them (OpenAI, Ollama, Bedrock, Gemini, etc.)<p>License: I went with Elastic License v2, not an OSI-approved license. I know that&#x27;s a topic with strong feelings here. The practical upshot: you can use it, modify it, self-host it, build products on top of it, you just can&#x27;t offer Antfly itself as a managed service. Felt like the right tradeoff for sustainability while still making the source available.<p>Happy to answer questions about the architecture, the Raft implementation, or anything else. Feedback welcome!

[Other] Show HN: Sub-millisecond VM sandboxes using CoW memory forking I wanted to see how fast an isolated code sandbox could start if I never had to boot a fresh VM.<p>So instead of launching a new microVM per execution, I boot Firecracker once with Python and numpy already loaded, then snapshot the full VM state. Every execution after that creates a new KVM VM backed by a `MAP_PRIVATE` mapping of the snapshot memory, so Linux gives me copy-on-write pages automatically.<p>That means each sandbox starts from an already-running Python process inside a real VM, runs the code, and exits.<p>These are real KVM VMs, not containers: separate guest kernel, separate guest memory, separate page tables. When a VM writes to memory, it gets a private copy of that page.<p>The hard part was not CoW itself. The hard part was resuming the snapshotted VM correctly.<p>Rust, Apache 2.0.

[Other] A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progress

[Other] Building a Shell

[CLI Tool] Show HN: Pgit – A Git-like CLI backed by PostgreSQL

[CLI Tool] Show HN: Crust – A CLI framework for TypeScript and Bun We&#x27;ve been building Crust (<a href="https:&#x2F;&#x2F;crustjs.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;crustjs.com&#x2F;</a>), a TypeScript-first, Bun-native CLI framework with zero dependencies. It&#x27;s been powering our core product internally for a while, and we&#x27;re now open-sourcing it.<p>The problem we kept running into: existing CLI frameworks in the JS ecosystem are either minimal arg parsers where you wire everything yourself, or heavyweight frameworks with large dependency trees and Node-era assumptions. We wanted something in between.<p>What Crust does differently:<p>- Full type inference from definitions — args and flags are inferred automatically. No manual type annotations, no generics to wrangle. You define a flag as type: &quot;string&quot; and it flows through to your handler.<p>- Compile-time validation — catches flag alias collisions and variadic arg mistakes before your code runs, not at runtime.<p>- Zero runtime dependencies — @crustjs&#x2F;core is ~3.6kB gzipped (21kB install). For comparison: yargs is 509kB, oclif is 411kB.<p>- Composable modules — core, plugins, prompts, styling, validation, and build tooling are all separate packages. Install only what you need.<p>- Plugin system — middleware-based with lifecycle hooks (preRun&#x2F;postRun). Official plugins for help, version, and shell autocompletion.<p>- Built for Bun — no Node compatibility layers, no legacy baggage.<p>Quick example:<p><pre><code> import { Crust } from &quot;@crustjs&#x2F;core&quot;; import { helpPlugin, versionPlugin } from &quot;@crustjs&#x2F;plugins&quot;; const main = new Crust(&quot;greet&quot;) .args([{ name: &quot;name&quot;, type: &quot;string&quot;, default: &quot;world&quot; }]) .flags({ shout: { type: &quot;boolean&quot;, short: &quot;s&quot; } }) .use(helpPlugin()) .use(versionPlugin(&quot;1.0.0&quot;)) .run(({ args, flags }) =&gt; { const msg = `Hello, ${args.name}!`; console.log(flags.shout ? msg.toUpperCase() : msg); }); await main.execute(); </code></pre> Scaffold a new project:<p><pre><code> bun create crust my-cli </code></pre> Site: <a href="https:&#x2F;&#x2F;crustjs.com" rel="nofollow">https:&#x2F;&#x2F;crustjs.com</a> GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;chenxin-yan&#x2F;crust" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;chenxin-yan&#x2F;crust</a><p>Happy to answer any questions about the design decisions or internals.

[Other] Leanstral: Open-source agent for trustworthy coding and formal proof engineering Lean 4 paper (2021): <a href="https:&#x2F;&#x2F;dl.acm.org&#x2F;doi&#x2F;10.1007&#x2F;978-3-030-79876-5_37" rel="nofollow">https:&#x2F;&#x2F;dl.acm.org&#x2F;doi&#x2F;10.1007&#x2F;978-3-030-79876-5_37</a>

[Other] Show HN: Most GPU Upgrades Aren't Worth It, I Built a Calculator to Prove It I run a small project called best-gpu.com, a site that ranks GPUs by price-to-performance.<p>While browsing PC building forums and Reddit, I kept seeing the same question: “What should I upgrade to from my current GPU?” Most answers are just lists of cards without showing the actual performance gain, so people often end up paying for upgrades that barely improve performance.<p>So I built a small tool: a GPU Upgrade Calculator.<p>You enter your current GPU and it shows:<p>estimated performance gain<p>a value score based on price vs performance<p>a filtered list of upgrade options (brand, price, VRAM, etc.)<p>The goal is simply to help people avoid spending money on upgrades that aren’t really worth it.<p>Curious to hear feedback from HN on the approach, data sources, or features that would make something like this more useful.<p><a href="https:&#x2F;&#x2F;best-gpu.com&#x2F;upgrade.php" rel="nofollow">https:&#x2F;&#x2F;best-gpu.com&#x2F;upgrade.php</a>

[API/SDK] Show HN: Open-source, extract any brand's logos, colors, and assets from a URL Hi everyone, I just open sourced OpenBrand - extract any brand&#x27;s logos, colors, and assets from just a URL.<p>It&#x27;s MIT licensed, open source, completely free. Try it out at openbrand.sh<p>It also comes with a free API and MCP server for you to use in your code or agents.<p>Why we built this: while building another product, we needed to pull in customers&#x27; brand images as custom backgrounds. It felt like a simple enough problem with no open source solution - so we built one.

[Other] Meta’s renewed commitment to jemalloc <a href="https:&#x2F;&#x2F;github.com&#x2F;jemalloc&#x2F;jemalloc" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jemalloc&#x2F;jemalloc</a>

[DevOps] Launch HN: Chamber (YC W26) – An AI Teammate for GPU Infrastructure Hey HN, we&#x27;re Jie Shen, Charles, Andreas, and Shaocheng. We built Chamber (<a href="https:&#x2F;&#x2F;usechamber.io">https:&#x2F;&#x2F;usechamber.io</a>), an AI agent that manages GPU infrastructure for you. You talk to it wherever your team already works and it handles things like provisioning clusters, diagnosing failed jobs, managing workloads. Demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=xdqh2C_hif4" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=xdqh2C_hif4</a><p>We all worked on GPU infrastructure at Amazon. Between us we&#x27;ve spent years on this problem — monitoring GPU fleets, debugging failures at scale, building the tooling around it. After leaving we talked to a bunch of AI teams and kept hearing the same stuff. Platform engineers spend half their time just keeping things running. Building dashboards, writing scheduling configs, answering &quot;when will my job start?&quot; all day. Researchers lose hours when a training run fails because figuring out why means digging through Kubernetes events, node logs, and GPU metrics in totally separate tools. Pretty much everyone had stitched together Prometheus, Grafana, Kubernetes scheduling policies, and a bunch of homegrown scripts, and they were spending as much time maintaining all of it as actually using it.<p>The thing we kept noticing is that most of this work follows patterns. Triage the failure, correlate a few signals, figure out what to do about it. If you had a platform with structured access to the full state of a GPU environment, you could have an agent do that work for you.<p>So that&#x27;s what we built. Chamber is a control plane that keeps a live model of your GPU fleet: nodes, workloads, team structure, cluster health. Every operation it supports is exposed as a tool the agent can call. Inspecting node health, reading cluster topology, managing workload lifecycle, adjusting resource configs, provisioning infrastructure. These are structured operations with validation and rollback, not just raw shell commands. When we add new capabilities to the platform, they automatically become things the agent can do too.<p>We spent a lot of time on safety because we&#x27;ve seen what happens when infrastructure automation goes wrong. A wrong call can kill a multi-day training run or cascade across a cluster. So the agent has graduated autonomy. Routine stuff it handles on its own: diagnosing a failed job, resubmitting with corrected resources, cordoning a bad node. But anything that touches other teams&#x27; workloads or production jobs needs human approval first. Every action gets logged with what the agent saw, why it acted, and what it changed.<p>The platform underneath is really what makes the diagnosis work. When the agent investigates a failure, it queries GPU state, workload history, node health timelines, and cluster topology. That&#x27;s the difference between &quot;your job OOMed&quot; and &quot;your job OOMed because the batch size exceeded available VRAM on this node, here&#x27;s a corrected config.&quot; Different root causes get different fixes.<p>One thing that surprised us, even coming from Amazon where we&#x27;d seen large GPU fleets: most teams we talk to can&#x27;t even tell you how many GPUs are in use right now. The monitoring just doesn&#x27;t exist. They&#x27;re flying blind on their most expensive hardware.<p>We’ve launched with a few early customers and are onboarding new teams. We’re still refining pricing and are currently evaluating models like per-GPU-under-management and tiered plans. We plan to publish transparent pricing once we’ve validated what works best for customers. In the meantime, we know “contact us” isn’t ideal.<p>Would love to hear from anyone running GPU clusters. What&#x27;s the most tedious part of your setup? What would you actually trust an agent to do? What&#x27;s off limits? Looking forward to feedback!

[Other] Show HN: Claude Code skills that build complete Godot games I’ve been working on this for about a year through four major rewrites. Godogen is a pipeline that takes a text prompt, designs the architecture, generates 2D&#x2F;3D assets, writes the GDScript, and tests it visually. The output is a complete, playable Godot 4 project.<p>Getting LLMs to reliably generate functional games required solving three specific engineering bottlenecks:<p>1. The Training Data Scarcity: LLMs barely know GDScript. It has ~850 classes and a Python-like syntax that will happily let a model hallucinate Python idioms that fail to compile. To fix this, I built a custom reference system: a hand-written language spec, full API docs converted from Godot&#x27;s XML source, and a quirks database for engine behaviors you can&#x27;t learn from docs alone. Because 850 classes blow up the context window, the agent lazy-loads only the specific APIs it needs at runtime.<p>2. The Build-Time vs. Runtime State: Scenes are generated by headless scripts that build the node graph in memory and serialize it to .tscn files. This avoids the fragility of hand-editing Godot&#x27;s serialization format. But it means certain engine features (like `@onready` or signal connections) aren&#x27;t available at build time—they only exist when the game actually runs. Teaching the model which APIs are available at which phase — and that every node needs its owner set correctly or it silently vanishes on save — took careful prompting but paid off.<p>3. The Evaluation Loop: A coding agent is inherently biased toward its own output. To stop it from cheating, a separate Gemini Flash agent acts as visual QA. It sees only the rendered screenshots from the running engine—no code—and compares them against a generated reference image. It catches the visual bugs text analysis misses: z-fighting, floating objects, physics explosions, and grid-like placements that should be organic.<p>Architecturally, it runs as two Claude Code skills: an orchestrator that plans the pipeline, and a task executor that implements each piece in a `context: fork` window so mistakes and state don&#x27;t accumulate.<p>Everything is open source: <a href="https:&#x2F;&#x2F;github.com&#x2F;htdt&#x2F;godogen" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;htdt&#x2F;godogen</a><p>Demo video (real games, not cherry-picked screenshots): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;eUz19GROIpY" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;eUz19GROIpY</a><p>Blog post with the full story (all the wrong turns) coming soon. Happy to answer questions.

[CLI Tool] Apideck CLI – An AI-agent interface with much lower context consumption than MCP