🛠️ All DevTools

[Other] Show HN: Autograd.c – a tiny ML framework built from scratch built a tiny pytorch clone in c after going through prof. vijay janapa reddi&#x27;s mlsys book: mlsysbook.ai&#x2F;tinytorch&#x2F;<p>perfect for learning how ml frameworks work under the hood :)

[Other] A kernel bug froze my machine: Debugging an async-profiler deadlock

[Other] I used Claude Code to write a piano web app

[Monitoring/Observability] Show HN: Cordon – Reduce large log files to anomalous sections Cordon uses transformer embeddings and density scoring to identify what&#x27;s semantically unique in log files, filtering out repetitive noise.<p>The core insight: a critical error repeated 1000x is &quot;normal&quot; (semantically dense). A strange one-off event is anomalous (semantically isolated).<p>Outputs XML-tagged blocks with anomaly scores. Designed to reduce large logs as a form of pre-processing for LLM analysis.<p>Architecture: <a href="https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;docs&#x2F;architecture.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;docs&#x2F;architec...</a><p>Benchmark: <a href="https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;benchmark&#x2F;results&#x2F;README.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;benchmark&#x2F;res...</a><p>Trade-offs: intentionally ignores repetitive patterns, uses percentile-based thresholds (relative, not absolute).

[IDE/Editor] Show HN: I built an open-source alternative to the "brainrot IDE" that YC funded VSCode extension with games (Snake, Plinko, slots), HN&#x2F;LessWrong reader, and Pomodoro timer for AI coding wait times. Fake currency only.<p>Marketplace: <a href="https:&#x2F;&#x2F;marketplace.visualstudio.com&#x2F;items?itemName=TouchGrassIDE.touch-grass-ide" rel="nofollow">https:&#x2F;&#x2F;marketplace.visualstudio.com&#x2F;items?itemName=TouchGra...</a><p>Built in a week with Claude Code after seeing Chad IDE get ratio&#x27;d for integrating real gambling. Happy to answer questions or get roasted on the code.

[Other] Chafa: Terminal Graphics for the 21st Century

[Database] Show HN: Sqlit – A lazygit-style TUI for SQL databases I work mostly in the terminal but found myself constantly switching to bloated GUIs like SSMS only for the simple task of browsing tables and run queries. And I didn&#x27;t find Existing SQL TUIs intuitive, having to read documentation to learn keybindings and CLI flags to connect. Given I had recently switched to linux, I found myself using vs code&#x27;s sql database extension. Something was awfully wrong.<p>I wanted something like lazygit for databases – run it, connect, and query and frankly just make it enjoyable to access data.<p><pre><code> Sqlit is a keyboard-driven SQL TUI with: - Context-based keybindings (always visible) - Neovim-like interface with normal and insert mode for query editing - Browse databases, tables, views, stored procedures - Adapters for SQL Server, SQLite, PostgreSQL, Turso &amp; more - SSH tunneling support - Themes (Tokyo Night, Nord, Gruvbox etc.) Inspired by lazygit, neovim and lazysql. Built with Python&#x2F;Textual. </code></pre> Feedback welcome – especially on which adapters to prioritize next. My vision of sqlit is to make a tool that makes it easy to connect and query data, and to do that, and that thing only, really well.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;Maxteabag&#x2F;sqlit" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Maxteabag&#x2F;sqlit</a>

[Other] Show HN: Sub-microsecond (890 ns) trading execution research system I am sharing a research-grade, open-source trading execution framework that achieves a median end-to-end decision latency of 890 nanoseconds on commodity hardware.<p>The project is designed for education, systems research, and latency instrumentation, not for live trading. It focuses on understanding exactly where every nanosecond goes in a trading execution path.<p>Key features:<p>- Kernel-bypass networking: Direct userspace access to NICs via custom drivers, 20-50 ns RX latency - Lock-free SPSC&#x2F;MPSC queues: Zero-copy architecture - SIMD feature extraction: About 40 ns per update using AVX-512 - Deterministic replay: Bit-identical execution paths, SHA-256 verified - Nanosecond-level metrics: Full audit logs and performance dashboard<p>Technical stack: C++17 and Rust, NUMA-aware memory allocation, cache-line alignment, inline assembly for hot paths.<p>The framework is modular, allowing experimentation with different NIC drivers, feature extraction pipelines, or order-flow models such as Hawkes processes or Avellaneda-Stoikov logic. Everything is open source and documented.<p>Links:<p>Live demo: <a href="https:&#x2F;&#x2F;submicro.krishnabajpai.me&#x2F;" rel="nofollow">https:&#x2F;&#x2F;submicro.krishnabajpai.me&#x2F;</a> Source code: <a href="https:&#x2F;&#x2F;github.com&#x2F;krish567366&#x2F;submicro-execution-engine" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;krish567366&#x2F;submicro-execution-engine</a> Bare-metal NIC drivers: <a href="https:&#x2F;&#x2F;baremetalnic.krishnabajpai.me&#x2F;" rel="nofollow">https:&#x2F;&#x2F;baremetalnic.krishnabajpai.me&#x2F;</a><p>I would welcome feedback from anyone working on low-latency systems, networking, or HFT research.<p>Some questions for discussion:<p>- Which part of the execution path is typically hardest to optimize? - What measurement techniques do you trust for sub-microsecond systems?<p>This project is for research and educational purposes only. It does not connect to exchanges or execute real trades. It is intended as a sandbox for understanding ultra-low-latency execution.<p>I am happy to answer questions about methodology, performance, or design trade-offs.

[DevOps] Show HN: Open-Source Notion MCP Server (TypeScript, SSE, Apify) Hello HN,<p>I built this because I wanted to give Claude Desktop access to my Notion workspace without running a flaky local Python script via stdio.<p>This is a Node.js&#x2F;Express implementation of the Model Context Protocol (MCP) that uses SSE (Server-Sent Events) for transport. It’s designed to be stateless and deployable as a container (I&#x27;m hosting it on Apify, but it works anywhere with Node).<p>The Stack:<p>TypeScript + Express<p>@modelcontextprotocol&#x2F;sdk<p>Zod for input validation<p>Bearer Auth for security (since it exposes an HTTP endpoint)<p>Capabilities: It allows the LLM to search pages, read database properties, and append blocks. I use it primarily to have Cursor&#x2F;Claude summarize documentation and create tasks in my sprint board directly from the chat context.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;piskunproject&#x2F;notion-mcp-server&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;piskunproject&#x2F;notion-mcp-server&#x2F;</a> One-click deploy (Apify): <a href="https:&#x2F;&#x2F;apify.com&#x2F;piskunlab&#x2F;notion-mcp-server" rel="nofollow">https:&#x2F;&#x2F;apify.com&#x2F;piskunlab&#x2F;notion-mcp-server</a><p>Feedback on the SSE implementation is welcome!

[Other] Show HN: Bithoven – A high-level, imperative language for Bitcoin Smart Contract Hey HN! I’m a researcher working on Bitcoin smart contracts, and today I’m releasing Bithoven—a high-level imperative language that compiles to native Bitcoin Script (Legacy, SegWit, and Taproot).<p>The Goal:<p>Raw Bitcoin Script is notoriously difficult to reason about. Writing raw Bitcoin Script today feels like writing Assembly in the 1970s. You have to mentally juggle the stack (`OP_SWAP`, `OP_ROT`), manually manage distinct execution branches, and pray you didn&#x27;t leave a stack item unconsumed (which crashes the script). My goal was to bridge the gap between complex contract logic and raw opcodes, allowing developers to write readable, compile-time-safe code.<p>Key Features:<p>- Imperative Syntax: Write logic using familiar if&#x2F;else and return statements instead of mental stack juggling.<p>- Type Safety: First-class support for bool, signature, string, and number types to prevent runtime errors.<p>- Targeted Compilation: Support for Legacy, SegWit, and Taproot compilation targets.<p>- Native Primitives: Built-in keywords for timelocks (older, after) and cryptography (sha256, checksig).<p>You can try it in the browser here (runs via WASM): <a href="https:&#x2F;&#x2F;bithoven-lang.github.io&#x2F;bithoven&#x2F;ide&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bithoven-lang.github.io&#x2F;bithoven&#x2F;ide&#x2F;</a><p>Here is an example of a Hashed Time-Locked Contract (HTLC):<p><pre><code> (condition: bool, sig_alice: signature) (condition: bool, preimage: string, sig_bob: signature) { if condition { &#x2F;&#x2F; Relative locktime (Sequence) older 1000; return checksig (sig_alice, alice_pk); } else { &#x2F;&#x2F; Hashlock verification verify sha256 sha256 preimage == hash; return checksig (sig_bob, bob_pk); } } </code></pre> The project is free open source and the academic paper is currently under review. I’d love to hear any feedback. Thanks for checking it out!

[Other] Building a High-Performance OpenAPI Parser in Go

[Database] One interface for all your databases. Self-hosted. Connect PostgreSQL, MySQL, SQLite, Supabase, AWS RDS... all in one place. AI writes your SQL. Deploy with Docker in 5 minutes.

[API/SDK] Turn websites into APIs Convert any website into a clean, type-safe API. Use natural-language or JSON-schema prompts to extract data and get usable JSON output for RAG pipelines, sales, content aggregation, or research.

[Other] Arborium: Tree-sitter code highlighting with Native and WASM targets

[Database] GNU recutils: Plain text database

[Other] How I wrote JustHTML, a Python-based HTML5 parser, using coding agents

[Other] Show HN: Open-source customizable AI voice dictation built on Pipecat Tambourine is an open source, fully customizable voice dictation system that lets you control STT&#x2F;ASR, LLM formatting, and prompts for inserting clean text into any app.<p>I have been building this on the side for a few weeks. What motivated it was wanting a customizable version of Wispr Flow where I could fully control the models, formatting, and behavior of the system, rather than relying on a black box.<p>Tambourine is built directly on top of Pipecat and relies on its modular voice agent framework. The back end is a local Python server that uses Pipecat to stitch together STT and LLM models into a single pipeline. This modularity is what makes it easy to swap providers, experiment with different setups, and maintain fine-grained control over the voice AI.<p>I shared an early version with friends and recently presented it at my local Claude Code meetup. The response was overwhelmingly positive, and I was encouraged to share it more widely.<p>The desktop app is built with Tauri. The front end is written in TypeScript, while the Tauri layer uses Rust to handle low level system integration. This enables the registration of global hotkeys, management of audio devices, and reliable text input at the cursor on both Windows and macOS.<p>At a high level, Tambourine gives you a universal voice interface across your OS. You press a global hotkey, speak, and formatted text is typed directly at your cursor. It works across emails, documents, chat apps, code editors, and terminals.<p>Under the hood, audio is streamed from the TypeScript front end to the Python server via WebRTC. The server runs real-time transcription with a configurable STT provider, then passes the transcript through an LLM that removes filler words, adds punctuation, and applies custom formatting rules and a personal dictionary. STT and LLM providers, as well as prompts, can be switched without restarting the app.<p>The project is still under active development. I am working through edge cases and refining the UX, and there will likely be breaking changes, but most core functionality already works well and has become part of my daily workflow.<p>I would really appreciate feedback, especially from anyone interested in the future of voice as an interface.

[Other] The official source for MDN Web Docs content. Home to over 14,000 pages of documentation about HTML, CSS, JS, HTTP, Web APIs, and more.

[Other] Show HN: I Ching simulator with accurate Yarrow Stalk probabilities I built this because I wanted a digital I Ching that honors the original math, rather than just Math.random().<p>Technical detail for the curious:<p>The simulation follows the traditional Yarrow Stalk algorithm (Da Yan). The core of this algorithm relies on modulo-4 arithmetic on the stalks remaining after a random split.<p>To make it physically realistic, I used a Box-Muller transform to simulate the user splitting the stalks with a Gaussian distribution (since humans naturally tend to split near the middle), rather than a uniform random split.<p>I was worried this &#x27;human bias&#x27; might skew the modulo probabilities, so I ran a 1-million-run Monte Carlo simulation to verify.<p>The Result: The remainders [0,1,2,3] still emerged with a near-perfect 25% distribution (deviation &lt; 0.05%).<p>So, the app simulates the physics of human imperfection while preserving the mathematical perfection of the probability distribution (where Old Yin is 1&#x2F;16).<p>Stack: Next.js + Tailwind css<p>Happy to answer any questions about the math or the hexagrams!

[DevOps] Show HN: Stepped Actions – distributed workflow orchestration for Rails Stepped is a Rails engine, extracted out of Envirobly where it powers tasks like application deployment, that involve complex, out-of-the-band tasks like DNS provisioning, retries, waiting for instances to boot, running health checks and all the fun stuff of a highly distributed networked system.