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[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

[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] 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.

[Database] Show HN: KV and wide-column database with CDN-scale replication

[CLI Tool] Show HN: Interactive Common Lisp: An Enhanced REPL I created this because sometimes I want more than rlwrap but less than emacs. icl aims to hit that middle sweet spot.<p>It&#x27;s a terminal application with context-aware auto-complete, an interactive object inspector, auto-indentation, syntax colouring, persistent history, and much more. It uses sly to communicate with the child lisp process and aims to be compatible with any sly-supporting implementation. I hope others find it useful!

[Other] I created a publishing system for step-by-step coding guides in Typst

[Other] Building an efficient hash table in Java

[Other] I tried Gleam for Advent of Code

[Other] Show HN: UK Butchers Meat Price Tracker Hey all!<p>Long time lurker, thought I would contribute back something to the community (at least the meat eaters in the UK). One thing that&#x27;s been a pain for me to get a good understanding of is what the best price is for online butchers based around the UK. I like high quality meat (including some of the bigger cuts for kamado&#x2F;bbq), and I&#x27;m willing to pay a premium however there isn&#x27;t a single resource that would let me get an overview of what&#x27;s available and price movements over time.<p>For my Xmas meat I didn&#x27;t want to start building out a spreadsheet so I built a price tracker with quite a few convenience features to make it easy to search for certain cuts of meat across commonly mentioned butchers on Reddit and other communities.<p>If you find it useful let me know if there are any features that could help to make it even easier to find the cuts of meat that you&#x27;re looking for!<p>Here&#x27;s the tracker: <a href="https:&#x2F;&#x2F;offer-spider.onrender.com" rel="nofollow">https:&#x2F;&#x2F;offer-spider.onrender.com</a><p>Hosted on Render, built with Nextjs and SQLite. Spidering various custom e-com sites (WooCommerce, Shopify) on a daily basis.<p>My current TODOs: - Search is currently free-text OR. Add AND to allow focussing down a search, and select from facets&#x2F;categories&#x2F;taxonomy - Common search terms to pre-fill - Exposing price over time metrics - Further consolidation of SKUs to an internal taxonomy - More butchers - Expanding to other use cases outside of meat!

[Other] Hacking Google Chrome Source Code: Make Puppeteer work over Redis PubSub

[Other] Show HN: Browser4 – an open-source browser engine for agents and concurrency Hi HN,<p>I’d like to share an open-source project we’ve been working on for a while: <i>Browser4</i>.<p>The motivation came from a recurring frustration: most browser automation tools (Playwright, Selenium, Puppeteer) are excellent for <i>human-written scripts</i>, but start to show friction when used as a <i>core execution layer for AI agents</i> or at very high concurrency.<p>So instead of building “another wrapper around Playwright”, we experimented with a different direction: <i>designing a browser engine where AI agents are first-class citizens.</i><p>### What Browser4 is<p>Browser4 is a browser automation engine built on <i>native Chrome DevTools Protocol (CDP)</i>, with a focus on:<p>* <i>Coroutine-safe concurrency</i> (designed to run many browser sessions in parallel)<p>* <i>Agent-oriented APIs</i> (navigation, interaction, extraction as composable actions)<p>* <i>Hybrid extraction</i>: ML agent driven extraction + LLM extraction + structured selectors + an SQL-like DOM query language (X-SQL)<p>* <i>Low-level control</i> without Playwright-style abstraction overhead<p>It’s written in <i>Kotlin&#x2F;JVM</i>, mainly because we needed predictable concurrency behavior and long-running stability under load.<p>The project is fully open-source (Apache 2.0).<p>### What it’s <i>not</i><p>* It’s not a drop-in Playwright replacement.<p>* It’s not a no-code RPA tool.<p>* It’s not “LLM magic” — LLMs sit <i>outside</i> the browser engine.<p>Browser4 intentionally stays close to the browser execution layer and leaves planning&#x2F;reasoning to external agent loops.<p>### Current use cases we’re testing<p>* Large-scale web data extraction<p>* Agentic workflows (search → navigate → extract → summarize)<p>* Price &#x2F; content monitoring with frequent revisits<p>* High-concurrency crawling where browser startup and context switching are bottlenecks<p>On a single machine, we can sustain <i>very high daily page visits</i>, though we’re still validating benchmarks across different workloads.<p>### Open questions (where I’d love feedback)<p>* For agentic systems, does it make sense to bypass Playwright entirely and work closer to CDP?<p>* Where do you see the biggest pain points when combining LLMs with browser automation today?<p>* Is JVM a reasonable choice here, or is Python still the better tradeoff despite concurrency limits<p>* What abstractions would <i>you</i> want in a browser engine built for AI agents?<p>### Links<p>* GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;platonai&#x2F;browser4" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;platonai&#x2F;browser4</a><p>* Website (light overview): <a href="https:&#x2F;&#x2F;browser4.io" rel="nofollow">https:&#x2F;&#x2F;browser4.io</a><p>Happy to answer technical questions or hear criticism — especially from people running browser automation or agent systems in production.<p>Thanks for reading.