🛠️ All DevTools

[Other] Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1

[Other] A light-weight and powerful meta-prompting, context engineering and spec-driven development system for Claude Code by TÂCHES.

[Testing] Painless E2E Automation for Mobile and Web

[CLI Tool] Cook: A simple CLI for orchestrating Claude Code

[Other] FastAPI-compatible Python framework with Zig HTTP core; 7x faster

[Other] Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training I replicated David Ng&#x27;s RYS method (<a href="https:&#x2F;&#x2F;dnhkng.github.io&#x2F;posts&#x2F;rys&#x2F;" rel="nofollow">https:&#x2F;&#x2F;dnhkng.github.io&#x2F;posts&#x2F;rys&#x2F;</a>) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn&#x27;t expect.<p>Transformers appear to have discrete &quot;reasoning circuits&quot; — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer.<p>The results on standard benchmarks (lm-evaluation-harness, n=50):<p>Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76 - GSM8K (strict): 0.48 → 0.64 - MBPP (code gen): 0.72 → 0.78 - Nothing degraded<p>Qwen2.5-Coder-32B, layers 7-9 duplicated once: - Reasoning probe: 76% → 94%<p>The weird part: different duplication patterns create different cognitive &quot;modes&quot; from the same weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling (13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing.<p>The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts. Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B).<p>Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo. The whole thing — sweep, discovery, validation — took one evening.<p>Happy to answer questions.

[IDE/Editor] Show HN: Tmux-IDE, OSS agent-first terminal IDE Hey HN,<p>Small OSS project that i created for myself and want to share with the community. It&#x27;s a declarative, scriptable, terminal-based IDE focussed on agentic engineering.<p>That&#x27;s a lot of jargon, but essentially its a multi-agent IDE that you start in your terminal.<p>Why is that relevant? Thanks to tmux and SSH, it means that you have a really simple and efficient way to create your own always-on coding setup.<p>Boot into your IDE through ssh, give a prompt to claude and close off your machine. In tmux-ide claude will keep working.<p>The tool is intentionally really lightweight, because I think the power should come from the harnesses that you are working with.<p>I&#x27;m hoping to share this with the community and get feedback and suggestions to shape this project! I think that &quot;remote work&quot; is directionally correct, because we can now have extremely long-running coding tasks. But I also think we should be able to control and orchstrate that experience according to what we need.<p>The project is 100% open-source, and i hope to shape it together with others who like to work in this way too!<p>Github: <a href="https:&#x2F;&#x2F;github.com&#x2F;wavyrai&#x2F;tmux-ide" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;wavyrai&#x2F;tmux-ide</a> Docs: <a href="https:&#x2F;&#x2F;tmux.thijsverreck.com&#x2F;docs" rel="nofollow">https:&#x2F;&#x2F;tmux.thijsverreck.com&#x2F;docs</a>

[Code Quality] Show HN: I got tired of print(x.shape) so I built runtime type hints for Python As a beginner learning to build ML models, I found it annoying to have to keep printing tensor shapes every other line, having to step through the debugger to check where did I mess up the shapes again.<p>So I built Trickle, it takes the data that flows through your code, caches the types and display them inline (as if you have type annotations).<p>The idea is: &quot;Let types trickle from runtime into your IDE&quot;. You get types in Python without having the write them manually.<p>It works by rewriting your Python AST at import time — after every variable assignment, it inserts a lightweight call that records the type and value. No decorators, no code changes. Just run your script through trickle run python train.py and every variable gets its type visible.<p>One cool feature is Error snapshots, by toggling it in VSCode status bar, you can see the exact data that is in each variable when the error happened.<p>For AI agents, trickle-cli outputs the inline runtime types together with the source code so agent can better diagnose the issue.<p>For Jupyter notebooks: %load_ext trickle as your first cell, and every cell after is traced.<p>Quick try: pip install trickle-observe npm install -g trickle-cli code --install-extension yiheinchai.trickle-vscode<p><pre><code> trickle run python demo&#x2F;demo.py trickle hints demo&#x2F;demo.py </code></pre> Limitations: - Expect 2-5x slowdown — designed for development, not production.<p>Also supports JavaScript&#x2F;TypeScript (Express, Fastify, Koa, Hono) though the Python&#x2F;ML side is where I&#x27;ve focused most effort.<p>In the future, I imagine there to be potential for using this as runtime observability in production via probabilistic sampling of types. Now, we know the code, we know the data, which is all the information we need to debug.<p>Happy to answer questions

[Other] Google Engineers Launch "Sashiko" for Agentic AI Code Review of the Linux Kernel

[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