Show HN: Bashd – Helper scripts for bulk CLI file management https://ift.tt/FRln14b

Show HN: Bashd – Helper scripts for bulk CLI file management My personal Bash scripts turned full-on toolkit. Great for managing large datasets, backups, or just for quick file navigation. https://ift.tt/aSI1mBx March 4, 2026 at 11:12PM

Show HN: Online OCR Free – Batch OCR UI for Tesseract, Gemini and OpenRouter https://ift.tt/d1m5wBL

Show HN: Online OCR Free – Batch OCR UI for Tesseract, Gemini and OpenRouter Built this because people working with large document sets had no free tool that handled batch processing cleanly. Tesseract is free and runs locally. For anything that needs more accuracy — Google Vision, Gemini, or any OpenRouter model — you bring your own API key. No subscription, no markup on your usage. Export as TXT, JSON, XML or PDF. AI engines support custom prompts so you can translate, extract form fields, or get structured output in one step. App: https://ift.tt/rMXZmiu Source: https://ift.tt/DaKP65i https://ift.tt/rMXZmiu March 4, 2026 at 01:42AM

Show HN: Apcher – Generate self-hosted Node.js workflows from prompts https://ift.tt/VLCrnYt

Show HN: Apcher – Generate self-hosted Node.js workflows from prompts https://apcher.dev March 3, 2026 at 11:41PM

Show HN: Smart-commit-rs – A zero-dependency Git commit tool in Rust https://ift.tt/3bVjTQv

Show HN: Valkey-powered semantic memory for Claude Code sessions https://ift.tt/vuLVYmn

Show HN: Valkey-powered semantic memory for Claude Code sessions I wanted to explore Valkey's vector search capabilities for AI workloads and had been looking for an excuse to build something with Bun. This weekend I combined both into a memory layer for Claude Code. https://ift.tt/oKSztRX The problem: Claude Code has CLAUDE.md and auto memory, but it's flat text with no semantic retrieval. You end up repeating context, especially around things not to do. BetterDB Memory hooks into Claude Code's lifecycle (SessionStart, PostToolUse, PreToolUse, Stop), summarizes each session, generates embeddings, and stores everything in Valkey using FT.SEARCH with HNSW. Next session, relevant memories surface automatically via vector similarity search. The interesting technical bit is that Valkey handles all of it - vector search, hash storage for structured memory data, sorted sets for knowledge indexing, lists for compression queues. No separate vector database. There's also an aging pipeline that applies exponential decay to old memories based on recency, clusters similar ones via cosine similarity, and merges them to keep the memory store from growing unbounded. Self-hostable with Ollama for embeddings and summarization, or plug in any LLM provider. Runs on Bun, ships as compiled binaries. MIT licensed. March 3, 2026 at 12:02AM

Show HN: Mrkd – A native macOS Markdown viewer with iTerm2/VSCode theme import https://ift.tt/vl4jx2i

Show HN: Mrkd – A native macOS Markdown viewer with iTerm2/VSCode theme import Using Opus 4.6 I built a markdown viewer for macOS that uses zero web technology. No Electron, no WebView — markdown is parsed with cmark-gfm and rendered directly to NSAttributedString via TextKit 2. The result is native text selection, native accessibility, and a ~1MB binary that launches pretty much instantly. It supports GFM tables, task lists, syntax-highlighted code blocks, and inline images. You get a built-in themes (Solarized, Dracula, GitHub, Monokai) plus the ability to import your own from iTerm2 or VS Code theme files. The part I’m most pleased with is the Quick Look integration — select a .md file in Finder, hit Space, and you get a fully themed preview using whatever theme and fonts you’ve configured in the app. No setup required; the QL extension registers automatically on first launch. It also bundles variable fonts (Geist, Inter, JetBrains Mono, iA Writer Mono, and more) so typography looks good out of the box. The whole thing is built in Swift with no dependencies beyond cmark-gfm and Highlightr. https://ift.tt/G26THns https://ift.tt/G26THns March 2, 2026 at 01:48AM

Show HN: PraxisJS – signal-driven front end framework and AI experiment https://ift.tt/OxYv0tQ

Show HN: PraxisJS – signal-driven front end framework and AI experiment I built PraxisJS, a signal-driven frontend framework exploring what a more explicit and traceable architecture could look like. PraxisJS started as a personal project. It reflects a single perspective on frontend design, not a committee decision, not a consensus. I wanted to see how far you can push explicitness before it becomes friction. Most frameworks optimize for writing less. PraxisJS questions that tradeoff. @State doesn’t suggest reactivity, it is reactive, visible in the code. Signals reach the DOM without a reconciliation layer in between (the renderer is still evolving toward that goal). It also became an AI-assisted experiment, not to automate thinking, but to pressure-test ideas. Some parts came from that collaboration. Some exist because it failed. v0.1.0 beta, experimental, not production-ready. But the ideas are real. https://praxisjs.org/ March 2, 2026 at 12:57AM

Show HN: Panel Panic a Rust/Macroquad/WASM Panel de Pon/Tetris Attack Clone https://ift.tt/JSeg6Tw

Show HN: Panel Panic a Rust/Macroquad/WASM Panel de Pon/Tetris Attack Clone Rust/macroquad game with single player AI mode, online VS, and local 1v1. All running via WASM in the browser. Still WIP as art assets still need to be added and tweaked. Full disclosure. Used Claude Opus, Nanobanana, and SunoAI a huge amount to do the heavy lifting for this project https://panel-panic.com March 1, 2026 at 10:48PM

Show HN: Monohub – a new GitHub alternative / code hosting service https://ift.tt/LH6dOmw

Show HN: Monohub – a new GitHub alternative / code hosting service Hello everyone, My name is Teymur Bayramov, and I am developing a forge/code hosting service called Monohub. It is at a fairly early stage of development, so it's quite rough around the edges. It is developed and hosted in EU. I have started developing it as a slim wrapper around Git to serve my own code, but it grew to such extent that I decided to give it a try and offer it as a service. It doesn't have much at the moment, but it already has basic pull requests. Accessibility is high priority. It will be a paid service, but since it's an early start, an "early adopter discount" is applied – 6 months for free. No card details required. I would be happy if you give it a try and let me know what do you think, and perhaps share what you lack in existing solutions that you would like to see implemented here. Warmest wishes, Teymur. https://monohub.dev/ March 1, 2026 at 12:43AM

Show HN: Velora Fitness – A zero-bloat, bare-bones workout tracker https://ift.tt/JDIab48

Show HN: Velora Fitness – A zero-bloat, bare-bones workout tracker https://ift.tt/MuGdcr1 March 1, 2026 at 02:30AM

Show HN: Tomoshibi – A writing app where your words fade by firelight https://ift.tt/3nJYTs5

Show HN: Tomoshibi – A writing app where your words fade by firelight I spent ten years trying to write a novel. Every time I sat down, I'd write a sentence, decide it wasn't good enough, and rewrite it. The problem wasn't discipline — it was that I could always see what I'd written and go back to change it. I tried other approaches. Apps that delete your words when you stop typing — they fight fear with fear. That just made me panic. I wanted the opposite: not punishment, but permission. "Tomoshibi" is Japanese for a small light in the dark — just enough to see what's in front of you. You write on a dark screen. Older lines fade, but not when you hit return. They fade when you start writing again. If you pause, they wait. You can edit the current line and one line back — enough to fix a typo, not enough to spiral. The one-line-back rule also catches my own practical issue: Japanese IME often fires an accidental newline on kanji confirmation. Everything is saved. There's a separate reader view for going back through what you've written. Tomoshibi is for writing over months, not just one session. When you come back, your last sentence appears as an epigraph — as if it always belonged there. No account, no server, no build step. Your writing stays in your browser's local storage — export anytime as .txt. Vanilla HTML/CSS/ES modules. Try it in your browser. A native Mac app (built with Tauri) with file system integration is coming to the store. I've been writing on it for two months. https://ift.tt/F6ojiQy https://ift.tt/ZXn8IR1 February 28, 2026 at 10:42PM

Show HN: Interactive Resume/CV Game https://ift.tt/iYJnpMl

Show HN: Interactive Resume/CV Game https://breezko.dev February 28, 2026 at 12:51AM

Show HN: Unfudged – version every change between commits - local-first https://ift.tt/hTQFCKf

Show HN: Unfudged – version every change between commits - local-first I built unf after I pasted a prompt into the wrong agent terminal and it overwrote hours of hand-edits across a handful of files. Git couldn't help because I hadn't finished/committed my in progress work. I wanted something that recorded every save automatically so I could rewind to any point in time. I wanted to make it difficult for an agent to permanently screw anything up, even with an errant rm -rf unf is a background daemon that watches directories you choose (via CLI) and snapshots every text file on save. It stores file contents in an object store, tracks metadata in SQLite, and gives you a CLI to query and restore any version. The install includes a UI, as well to explore the history through time. The tool skips binaries and respects `.gitignore` if one exists. The interface borrows from git so it should feel familiar: unf log , unf diff , unf restore . I say "UN-EF" vs U.N.F, but that's for y'all to decide: I started by calling the project Unfucked and got unfucked.ai, which if you know me and the messes I get myself into, is a fitting purchase. The CLI command is `unf` and the Tauri desktop app is called "Unfudged". How it works: https://ift.tt/3QxLFtJ (summary below) The daemon uses FSEvents on macOS and inotify on Linux. When a file changes, `unf` hashes the content with BLAKE3 and checks whether that hash already exists in the object store — if it does, it just records a new metadata entry pointing to the existing blob. If not, it writes the blob and records the entry. Each snapshot is a row in SQLite. Restores read the blob back from the object store and overwrite the file, after taking a safety snapshot of the current state first (so restoring is itself reversible). There are two processes. The core daemon does the real work of managing FSEvents/inotify subscriptions across multiple watched directories and writing snapshots. A sentinel watchdog supervises it, kept alive and aligned by launchd on macOS and systemd on Linux. If the daemon crashes, the sentinel respawns it and reconciles any drift between what you asked to watch and what's actually being watched. It was hard to build the second daemon because it felt like conceding that the core wasn't solid enough, but I didn't want to ship a tool that demanded perfection to deliver on the product promise, so the sentinel is the safety net. Fingers crossed, I haven’t seen it crash in over a week of personal usage on my Mac. But, I don't want to trigger "works for me" trauma. The part I like most: On the UI, I enjoy viewing files through time. You can select a time section and filter your projects on a histogram of activity. That has been invaluable in seeing what the agent was doing. On the CLI, the commands are composable. Everything outputs to stdout so you can pipe it into whatever you want. I use these regularly and AI agents are better with the tool than I am: # What did my config look like before we broke it? unf cat nginx.conf --at 1h | nginx -t -c /dev/stdin # Grep through a deleted file unf cat old-routes.rs --at 2d | grep "pub fn" # Count how many lines changed in the last 10 minutes unf diff --at 10m | grep '^[+-]' | wc -l # Feed the last hour of changes to an AI for review unf diff --at 1h | pbcopy # Compare two points in time with your own diff tool diff <(unf cat app.tsx --at 1h) <(unf cat app.tsx --at 5m) # Restore just the .rs files that changed in the last 5 minutes unf diff --at 5m --json | jq -r '.changes[].file' | grep '\.rs$' | xargs -I{} unf restore {} --at 5m # Watch for changes in real time watch -n5 'unf diff --at 30s' What was new for me: I came to Rust in Nov. 2025 honestly because of HN enthusiasm and some FOMO. No regrets. I enjoy the language enough that I'm now working on custom clippy lints to enforce functional programming practices. This project was also my first Apple-notarized DMG, my first Homebrew tap, and my second Tauri app (first one I've shared). Install & Usage: > brew install cyrusradfar/unf/unfudged Then unf watch in a directory. unf help covers the details (or ask your agent to coach). https://ift.tt/lyHCIfi February 27, 2026 at 03:00AM

Show HN: Beehive – Multi-Workspace Agent Orchestrator https://ift.tt/wLfD1KQ

Show HN: Beehive – Multi-Workspace Agent Orchestrator hey hn, i built beehive for myself mostly. it has gotten to the point where my work consists in supervising oc or cc labor at tasks for multiple issues in parallel. my set up used to be zellij with a couple tabs, each tab working in a separate dir and it was a pain to manage all that. i know i could use git worktrees but they're kind of complicated, if you don't know how to use them it is easy to mess up, and i just prefer letting agents run in separate dirs with their own .git and not risk it. while i like zellij and use it inside beehive, i dont like the tabs and i forget where i am half the time. beehive is a way for me to abstract that away. the heuristic is simple - hives are repos, so you basically have a bunch of hives which correspond to repos you work out of. each hive can have many combs. a comb is a dir with the copy of the repo you're working on. fully isolated, standalone, no shared .git. so for work or for personal stuff, i usually set up the hive, and then have a bunch of combs that i jump between supervising the agents do their thing. if you have a big repo it takes a minute to clone, and you also need gh and git because i like the niceties of like checking if the repo is there at all and stuff like that. the app is open source, mit license. i went with tauri because i hate electron. also i have friends and coworkers who updated to macos 26 and i dont know if the whole mem leak thing for electron apps has been fixed. the app is like 9 megs which is nice too. most of it is written with cc, but i guided the aesthetics and the approach. works on mac and there is a dmg signed and notarized (i reactivated my apple dev credentials). sharing this to get a vibe check on the idea, also maybe this is useful for you. there are many arguments, reasonable ones, you can make for worktrees vs dirs. i just know that trees are too big brain for me, and i like simple things. if you like it, pls lmk and also if you want to help (like add linux support, or like add themes, other cool things) please make a pr / open an issue. https://storozhenko98.github.io/beehive/ February 24, 2026 at 04:11PM

Show HN: Linex – A daily challenge: placing pieces on a board that fights back https://ift.tt/sYrHVi5

Show HN: Linex – A daily challenge: placing pieces on a board that fights back Hi HN, I wanted to share a web game I’ve been building in HTML, JavaScript, MySQL, and PHP called LINEX. It is primarily designed and optimized to be played in the mobile browser. The idea is simple: you have an 8x8 board where you must place pieces (Tetris-style and some custom shapes) to clear horizontal and vertical lines. Yes, someone might think this has already been done, but let me explain. You choose where to place the piece and how to rotate it. The core interaction consists of "drawing" the piece tap-by-tap on the grid, which provides a very satisfying tactile sense of control and requires a much more thoughtful strategy. To avoid the flat difficulty curve typical of games in this genre, I’ve implemented a couple of twists: 1. Progressive difficulty (The board fights back): As you progress and clear lines, permanently blocked cells randomly appear on the board. This forces you to constantly adapt your spatial vision. 2. Tools to defend yourself: To counter frustration, you have a very limited number of aids (skip the piece, choose another one, or use a special 1x1 piece). These resources increase slightly as the board fills up with blocked cells, forcing you to decide the exact right moment to use them. The game features a daily challenge driven by a date-based random seed (PRNG). Everyone gets exactly the same sequence of pieces and blockers. Furthermore, the base difficulty scales throughout the week: on Mondays you start with a clean board (0 initial blocked cells, although several will appear as the game progresses), and the difficulty ramps up until Sunday, where you start the game with 3 obstacles already in place. In addition to the global medal leaderboard, you can add other users to your profile to create a private leaderboard and compete head-to-head just with your friends. Time is also an important factor, as in the event of a tie in cleared lines, the player who completed them faster will rank higher on the leaderboard. I would love for you to check it out. I'm especially looking for honest feedback on the difficulty curve, the piece-placement interaction (UI/UX), or the balancing of obstacles/tools, although any other ideas, critiques, or suggestions are welcome. https://ift.tt/c6sY7Bk Thanks! https://ift.tt/c6sY7Bk February 25, 2026 at 05:03AM

Show HN: Agent that matches sales reps with warm leads based on product usage https://ift.tt/vrs4p3i

Show HN: Agent that matches sales reps with warm leads based on product usage hey, I'm building a tool that: 1. analyzes your Posthog data 2. finds patterns that lead to plan upgrade/account expansion 3. creates a deal in your CRM whenever it sees it againg we've just launched a huge update. Beton now has MCP (my Claude Code is already connected), Firecrawl integration and onboarding that's easier to understand available in cloud and via AGPLv3 let us know if you need any help setting up PS it's also suitable if you want to send triggered push notifications or emails https://ift.tt/ApsLjwN February 25, 2026 at 11:39PM

Show HN: Chaos Monkey but for Audio Video Testing (WebRTC and UDP) https://ift.tt/BI16aA4

Show HN: Chaos Monkey but for Audio Video Testing (WebRTC and UDP) It takes an input video and converts it into H.264/Opus RTP streams that you can blast at your video call systems (WebRTC, SFUs, etc.). It also injects network chaos like packet loss, jitter, and bitrate throttling to see how things break It scales from 1 to n participants, depending on the compute and memory of the host system Best part? It’s packaged with Nix, so it builds the same everywhere (Linux, macOS, ARM, x86). No dependency hell It supports both UDP (with a relay chain for Kubernetes) and WebRTC (with containerized TURN servers). Chaos spikes can be distributed evenly, randomly, or front/back-loaded for different test scenarios. To change this, just edit the values in a single config file https://ift.tt/uCALcVG February 23, 2026 at 02:23PM

Show HN: MasqueradeORM – Memory Efficient Node ORM: Just Write Classes https://ift.tt/MVm5Hfg

Show HN: MasqueradeORM – Memory Efficient Node ORM: Just Write Classes https://ift.tt/GBNWtsT February 24, 2026 at 11:11PM

Show HN: Unlock the best engineering knowledge in papers for your coding agent https://ift.tt/WfM9hEJ

Show HN: Unlock the best engineering knowledge in papers for your coding agent https://ift.tt/rcd9EtU February 23, 2026 at 11:03PM

Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) https://ift.tt/lmFHUtZ

Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) AgentDbg is a local-first debugger for AI agents. It records structured runs (LLM calls, tool calls, state, errors) to JSONL and shows the timeline UI locally. There is no need for cloud, accounts, and no telemetry. Flow is as simple as: 1. Run an agent 2. `agentdbg view` 3. Inspect the timeline, loop warnings, errors, etc. v0.1 includes `@trace` and `traced_run`, recorders, loop detection, best-effort redaction (by default), local UI, export. I also started working on integrations: there is an optional LangChain/LangGraph callback. * Repo: https://ift.tt/LP52D6Y * Demo: `python examples/demo/pure_python` and then `agentdbg view` Would love feedback on: 1. Trace format 2. Integrations to prioritize in the next several days 3. What you would want for deterministic replay https://ift.tt/LP52D6Y February 23, 2026 at 11:14PM

Show HN: MuJoCo React https://ift.tt/nDZsrav

Show HN: MuJoCo React MuJoCo physics simulation in the browser using React. This is made possible by DeepMind's mujoco-wasm (mujoco-js), which compiles MuJoCo to WebAssembly. We wrap it with React Three Fiber so you can load any MuJoCo model, step physics, and write controllers as React components, all running client-side in the browser https://ift.tt/WNksA4o February 22, 2026 at 11:59PM

Show HN: DevBind – I made a Rust tool for zero-config local HTTPS and DNS https://ift.tt/Jls3nm0

Show HN: DevBind – I made a Rust tool for zero-config local HTTPS and DNS Hey HN, I got tired of messing with /etc/hosts and browser SSL warnings every time I started a new project. So I wrote DevBind. It's a small reverse proxy in Rust. It basically does two things: 1. Runs a tiny DNS server so anything.test just works instantly (no more manual hosts file edits). 2. Sits on port 443 and auto-signs SSL certs on the fly so you get the nice green lock in Chrome/Firefox. It's been built mostly for Linux (it hooks into systemd-resolved), but I've added some experimental bits for Mac/Win too. Still a work in progress, but I've been using it for my own dev work and it's saved me a ton of time. Would love to know if it breaks for you or if there's a better way to handle the networking bits! https://ift.tt/JsdVhUL February 22, 2026 at 01:49AM

Show HN: Museum of Handwritten Code (If, While, Binary Search, Merge Sort) https://ift.tt/4sDyK2x

Show HN: Museum of Handwritten Code (If, While, Binary Search, Merge Sort) Hi HN - this is a small experiment: what if code had a museum? I built a Museum of Handwritten Code for foundational constructs and algorithms. I’ve been feeling a strange melancholy watching more and more software generation become automated, and wanted to preserve the "atoms" of programming in a form people can browse, discuss, and (hopefully) learn from. Yes, it’s a vanity project — but I’m trying to make each exhibit real: code, description, and historical context (with more being added over time). If AI increasingly writes the software stack (and maybe one day much closer to machine code), then here’s to the for-loops, if-branches, and hash maps that helped build the world we live in. Cheers! I’d love brutal feedback on whether this feels: * interesting * useful * too gimmicky * or actually a decent teaching / history format https://museum.codes February 22, 2026 at 02:00AM

Show HN: Winslop – De-Slop Windows https://ift.tt/JPTBtHV

Show HN: Winslop – De-Slop Windows https://ift.tt/GSQryTJ February 22, 2026 at 01:26AM

Show HN: HelixDB Explorer – A macOS GUI for HelixDB https://ift.tt/RQAkhiI

Show HN: HelixDB Explorer – A macOS GUI for HelixDB https://ift.tt/ySEvPGW February 20, 2026 at 11:18PM

Show HN: A small, simple music theory library in C99 https://ift.tt/8Xb75DL

Show HN: A small, simple music theory library in C99 https://ift.tt/NpKdaE5 February 20, 2026 at 04:24AM

Show HN: Hi.new – DMs for agents (open-source) https://ift.tt/GJpWvuR

Show HN: Hi.new – DMs for agents (open-source) https://www.hi.new/ February 20, 2026 at 02:50AM

Show HN: Astroworld – A universal N-body gravity engine in Python https://ift.tt/FctZTqp

Show HN: Astroworld – A universal N-body gravity engine in Python I’ve been working on a modular N-body simulator in Python called Astroworld. It started as a Solar System visualizer, but I recently refactored it into a general-purpose engine that decouples physical laws from planetary data.Technical Highlights:Symplectic Integration: Uses a Velocity Verlet integrator to maintain long-term energy conservation ($\Delta E/E \approx 10^{-8}$ in stable systems).Agnostic Architecture: It can ingest any system via orbital elements (Keplerian) or state vectors. I've used it to validate the stability of ultra-compact systems like TRAPPIST-1 and long-period perturbations like the Planet 9 hypothesis.Validation: Includes 90+ physical tests, including Mercury’s relativistic precession using Schwarzschild metric corrections.The Planet 9 Experiment:I ran a 10k-year simulation to track the differential signal in the argument of perihelion ($\omega$) for TNOs like Sedna. The result ($\approx 0.002^{\circ}$) was a great sanity check for the engine’s precision, as this effect is secular and requires millions of years to fully manifest.The Stack:NumPy for vectorization, Matplotlib for 2D analysis, and Plotly for interactive 3D trajectories.I'm currently working on a real-time 3D rendering layer. I’d love to get feedback on the integrator’s stability for high-eccentricity orbits or suggestions on implementing more complex gravitational potentials. https://ift.tt/Ee0cjKS February 20, 2026 at 01:27AM

Show HN: Wakapadi – Meet locals and travelers nearby and join free walking tours https://ift.tt/BnUX1pl

Show HN: Wakapadi – Meet locals and travelers nearby and join free walking tours Hi HN, I built Wakapadi after noticing that most travel tools focus on planning trips, but not on actually helping people connect once they arrive somewhere new. When traveling, it’s often hard to meet locals or other travelers unless you already know someone, join organized tours, or rely on chance. I wanted to make discovery more natural — seeing who’s nearby, joining free walking tours, and exploring cities together. Wakapadi currently allows users to: discover free walking tours see nearby travelers and locals who are open to meeting connect and chat before meeting explore cities in a more social way The project is still early, and I’m especially interested in feedback on: safety and privacy expectations what would make you comfortable meeting people while traveling features that would make this genuinely useful instead of another travel app Happy to answer any technical or product questions. https://ift.tt/vML98P7 February 18, 2026 at 11:40PM

Show HN: I'm launching a LPFM radio station https://ift.tt/lRImzXi

Show HN: I'm launching a LPFM radio station I've been working on creating a Low Power FM radio station for the east San Fernando Valley of Los Angeles. We are not yet on the broadcast band but our channel will be 95.9FM and our range can been seen on the homepage of our site. KPBJ is a freeform community radio station. Anyone in the area is encouraged to get a timeslot and become a host. We make no curatorial decisions. Its sort of like public access or a college station in that way. This month we launched our internet stream and on-boarded about 60 shows. They are mostly music but there are a few talk shows. We are restricting all shows to monthly time slots for now but this will change in the near future as everyone gets more familiar with the systems involved. All shows are pre-recorded until we can raise the money to get a studio. We have a site secured for our transmitter but we need to fundraise to cover the equipment and build out costs. We will be broadcasting with 100W ERP from a ridgeline in the Verdugos at about 1500ft elevation. The site will need to be off grid so we will need to install a solar system with battery backup. We are planning to sync the station to the transmit site with 802.11ah. I've built all of our web infrastructure using Haskell, NixOS, Terraform, and HTMX: https://ift.tt/1xYoVqZ This is a pretty substantial project involving a bunch of social and technical challenges and a shoe string budget. I'm feel pretty confident we will pull it off and make it a high impact local radio station. The station is managed by a 501c3 non-profit we created. We are actively seeking fundraising, especially to get our transmit site up and running. If you live in the area or want to contribute in any way then please reach out! https://www.kpbj.fm/ February 18, 2026 at 01:45AM

Show HN: AsteroidOS 2.0 – Nobody asked, we shipped anyway https://ift.tt/5lendAE

Show HN: AsteroidOS 2.0 – Nobody asked, we shipped anyway https://ift.tt/l8kR0pi February 18, 2026 at 12:54AM

Show HN: I curated 130 US PDF forms and made them fillable in browser https://ift.tt/Qd3v5Su

Show HN: I curated 130 US PDF forms and made them fillable in browser Hi HN! I built SimplePDF 7 years ago, with the vision from day one to help get rid of bureaucracy (I'm from France, I know what I'm talking about) Fast forward to this week where I finally released something I had on my mind for a long time: a repository of the main US forms that are ready to be filled, straight from the browser, as opposed to having to find a PDF tool online (or local). I focused on healthcare, ED, HR, Legal and IRS/Tax for now. On the tech-side, it's SimplePDF all the way down: client-side processing (the data / documents stay in your browser). I hope you find the resource useful! NiP https://ift.tt/reWZTcz February 18, 2026 at 12:03AM

Show HN: Nerve: Stitches all your data sources into one mega-API https://ift.tt/5enHZNG

Show HN: Nerve: Stitches all your data sources into one mega-API Hi HN! Nerve is a solo project I've been working on for the last few years. It's a developer tool that stitches together data from multiple sources in real-time. A lot of high-leverage projects (AI or otherwise) involve tying data together from multiple systems of record. This is easy enough when the data is simple and the sources are few, but if you have highly nested data and lots of sources (or you need things like federated pagination and filtering), you have to write a lot of gnarly boilerplate that's brittle and easy to get wrong. One solution is to import all your data into a central warehouse and just pull it from there. This works, but 1) you need a warehouse, 2) you have an extra copy of the data that can get stale or inconsistent, 3) you need to write and manage pipelines/connectors (or outsource them to a vendor), and 4) you're adding an extra point of failure. Nerve lets you write GraphQL-style queries that span multiple sources; then it goes out and pulls from whatever source APIs it needs to at query-time - all your source data stays where it is. Nerve has pre-built bindings to external SAAS services, and it's straightforward to hook it into your internal sources as well. Nerve is made for individual developers or two-pizza teams who: -Are building agents/internal tools -Need to deal with messy data strewn across different systems -Don't have a data team/warehouse at their disposal, (or do, but can't get a slice of their bandwidth) -Want to get to production as quickly as possible Everything you see in the demo is shipped and usable, but I'm adding a little polish before I officially launch. In the meantime, if you have a project you'd like to use Nerve on and you want to be a beta user, just drop me a line at mprast@get-nerve.com (it's free! I'll just pop in from time to time to ask you how it's going and what I can improve :) ) If you want to get an email when Nerve is ready from prime-time, you can sign up for the waitlist at get-nerve.com. Thanks for reading! (EDIT: Nerve is desktop only! I'll put up a gate on the site saying as much.) https://ift.tt/mRuogij February 15, 2026 at 04:37AM

Show HN: AsdPrompt – Vimium-style keyboard navigation for AI chat responses https://ift.tt/QPaSFtg

Show HN: AsdPrompt – Vimium-style keyboard navigation for AI chat responses I use Claude throughout the day and kept getting annoyed by the same thing: selecting text from responses with the mouse. Overshoot, re-select, copy, click input, paste. Especially bad in long conversations where you want to reference something from 30 turns ago. asdPrompt is a Chrome extension that adds hint-based navigation (like Vimium) to AI chat interfaces. Cmd+Shift+S activates the overlay, hint labels appear next to every text block. Type a letter to select a block, then keep typing to drill down: block → sentence → word. Enter copies, or you can press an action key (e, d, x) to inject a follow-up prompt ("elaborate on [selection]") directly into the chat input. Works on claude.ai, chatgpt.com, and gemini.google.com. Adapts to light/dark themes. Free. Built the initial MVP in 2 days using Claude Code — the adapter architecture, NLP segmentation pipeline, and Playwright test harness would have taken a month without it. Tech details for the curious: site-specific DOM parsers behind an adapter interface, text segmentation via compromise.js with regex fallbacks for technical content (paths, camelCase break NLP libraries), bounding rectangles calculated via Range API + TreeWalker, overlay isolated in Shadow DOM. Tested with Playwright visual regression. The landing page has an interactive tutorial where you can try the full drill-down mechanic without installing. Happy to talk about the implementation. https://asdprompt.com/ February 16, 2026 at 10:58PM

Show HN: Please hack my C webserver (it's a collaborative whiteboard) https://ift.tt/YU8G5M2

Show HN: Please hack my C webserver (it's a collaborative whiteboard) Source code: https://ift.tt/u7Otw2F https://ced.quest/draw/ February 16, 2026 at 12:27AM

Show HN: An open-source extension to chat with your bookmarks using local LLMs https://ift.tt/6eNA5li

Show HN: An open-source extension to chat with your bookmarks using local LLMs I read a lot online and constantly bookmark articles, docs, and resources… then forget why I saved them. Also was very bored on Valentines, so I built a browser extension that lets you chat with your bookmarks directly, using local-first AI (WebLLM running entirely in the browser). The extension downloads and indexes your bookmarked pages, stores them locally, and lets you ask questions. No server, no cloud processing, everything stays on your machine. Very early but it works and planning to add a bunch of stuff. Did I mentioned is open-source, MIT licensed? https://ift.tt/MOtixdn February 15, 2026 at 10:31PM

Show HN: Microgpt is a GPT you can visualize in the browser https://ift.tt/WXZkdMi

Show HN: Microgpt is a GPT you can visualize in the browser very much inspired by karpathy's microgpt of the same name. it's (by default) a 4000 param GPT/LLM/NN that learns to generate names. this is sorta an educational tool in that you can visualize the activations as they pass through the network, and click on things to get an explanation of them. https://ift.tt/KHJqQnx February 16, 2026 at 12:10AM

Show HN: GitHub "Lines Viewed" extension to keep you sane reviewing long AI PRs https://ift.tt/smEZ0JR

Show HN: GitHub "Lines Viewed" extension to keep you sane reviewing long AI PRs I was frustrated with how bad a signal of progress through a big PR "Files viewed" was, so I made a "Lines viewed" indicator to complement it. Designed to look like a stock Github UI element - even respects light/dark theme. Runs fully locally, no API calls. Splits insertions and deletions by default, but you can also merge them into a single "lines" figure in the settings. https://ift.tt/CTHEFiB February 14, 2026 at 12:49AM

Show HN: Azazel – Lightweight eBPF-based malware analysis sandbox using Docker https://ift.tt/ADuCTlz

Show HN: Azazel – Lightweight eBPF-based malware analysis sandbox using Docker Hey HN, I got frustrated with heavy proprietary sandboxes for malware analysis, so I built my own. Azazel is a single static Go binary that attaches 19 eBPF hook points to an isolated Docker container and captures everything a sample does — syscalls, file I/O, network connections, DNS, process trees — as NDJSON. It uses cgroup-based filtering so it only traces the target container, and CO-RE (BTF) so it works across kernel versions without recompilation. It also has built-in heuristics that flag common malware behaviors: exec from /tmp, sensitive file access, ptrace, W+X mmap, kernel module loading, etc. Stack: Go + cilium/ebpf + Docker Compose. Requires Linux 5.8+ with BTF. This is the first release — it's CLI-only for now. A proper dashboard is planned. Contributions welcome, especially around new detection heuristics and additional syscall hooks. https://ift.tt/lRLu0H9 February 15, 2026 at 12:37AM

Show HN: Data Engineering Book – An open source, community-driven guide https://ift.tt/X5ftUC4

Show HN: Data Engineering Book – An open source, community-driven guide https://ift.tt/cDJl6RW February 14, 2026 at 03:05AM

Show HN: Moltis – AI assistant with memory, tools, and self-extending skills https://ift.tt/HiZg3X0

Show HN: Moltis – AI assistant with memory, tools, and self-extending skills Hey HN. I'm Fabien, principal engineer, 25 years shipping production systems (Ruby, Swift, now Rust). I built Moltis because I wanted an AI assistant I could run myself, trust end to end, and make extensible in the Rust way using traits and the type system. It shares some ideas with OpenClaw (same memory approach, Pi-inspired self-extension) but is Rust-native from the ground up. The agent can create its own skills at runtime. Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging Face), sandboxed execution (Docker/Podman/Apple Containers), hybrid vector + full-text memory, MCP tool servers with auto-restart, and multi-channel (web, Telegram, API) with shared context. MIT licensed. No telemetry phoning home, but full observability built in (OpenTelemetry, Prometheus). I've included 1-click deploys on DigitalOcean and Fly.io, but since a Docker image is provided you can easily run it on your own servers as well. I've written before about owning your content ( https://ift.tt/bAU6w2t ) and owning your email ( https://ift.tt/Qpj8tum ). Same logic here: if something touches your files, credentials, and daily workflow, you should be able to inspect it, audit it, and fork it if the project changes direction. It's alpha. I use it daily and I'm shipping because it's useful, not because it's done. Longer architecture deep-dive: https://ift.tt/n6JxMg4... Happy to discuss the Rust architecture, security model, or local LLM setup. Would love feedback. https://www.moltis.org February 13, 2026 at 12:45AM

Show HN: rari, the rust-powered react framework https://ift.tt/9PsBtCr

Show HN: rari, the rust-powered react framework https://rari.build/ February 13, 2026 at 12:45AM

Show HN: Pgclaw – A "Clawdbot" in every row with 400 lines of Postgres SQL https://ift.tt/d1Z478B

Show HN: Pgclaw – A "Clawdbot" in every row with 400 lines of Postgres SQL Hi HN, Been hacking on a simple way to run agents entirely inside of a Postgres database, "an agent per row". Things you could build with this: * Your own agent orchestrator * A personal assistant with time travel * (more things I can't think of yet) Not quite there yet but thought I'd share it in its current state. https://ift.tt/cgWH7yr February 12, 2026 at 11:12PM

Show HN: Unpack – a lightweight way to steer Codex/Claude with phased docs https://ift.tt/uv8XihO

Show HN: Unpack – a lightweight way to steer Codex/Claude with phased docs I've been using LLMs for long discovery and research chats (papers, repos, best practices), then distilling that into phased markdown (build plan + tests), then handing those phases to Codex/Claude to implement and test phase by phase. The annoying part was always the distillation and keeping docs and architecture current, so I built Unpack: a lightweight GitHub template plus docs structure and a few commands that turns conversations into phases/specs and keeps project docs up to date as the agent builds. It can also generate Mintlify-friendly end-user docs. There are other spec-driven workflows and tools out there. I wanted something conversation-first and repo-native: plain markdown phases, minimal ceremony, easy to adapt per stack. Example generated with Unpack (tiny pokedex plus random monsters): Demo: https://apresmoi.github.io/pokesvg-codex/ Phases index: https://ift.tt/NO6AqMH... I’d love feedback on what the “minimum good” phase/spec format should be, and what would make this actually usable in your workflow. -------- Repo: https://ift.tt/xtSWjI1 https://ift.tt/xtSWjI1 February 12, 2026 at 01:17AM

Show HN: Yet another music player but written in Rust https://ift.tt/c4vYzda

Show HN: Yet another music player but written in Rust Hey i made a music player which support both local music files and jellyfin server, and it has embedded discord rpc support!!! it is still under development, i would really appreciate for feedback and contributions!! https://ift.tt/evnkz85 February 12, 2026 at 01:29AM

Show HN: NOOR – A Sovereign AI developed on a smartphone under siege in Yemen https://ift.tt/wSe3jHd

Show HN: NOOR – A Sovereign AI developed on a smartphone under siege in Yemen "I am a software developer from Yemen, coding on a smartphone while living under siege. I have successfully built and encrypted the core logic for NOOR—a decentralized and unbiased AI system. Execution Proof: My core node is verified and running locally via Termux using encrypted truth protocols. However, I am trapped in a 6-inch screen 'prison' with 10% processing capacity. My Goal: To secure $400 for a laptop development station to transition from mobile coding to building the full 'Seventh Node'. This is my bridge to freedom. Codes from the heart of hell are calling for your rescue. Wallet: 0x4fd3729a4fEdf54a74b73d93F7f775A1EF520CEC" https://ift.tt/BaoRTkM February 11, 2026 at 11:53PM

Show HN: Deadlog – almost drop-in mutex for debugging Go deadlocks https://ift.tt/ESrDhy3

Show HN: Deadlog – almost drop-in mutex for debugging Go deadlocks I've done this same println debugging thing so many times, along with some sed/awk stuff to figure out which call was causing the issue. Now it's a small Go package. With some `runtime.Callers` I can usually find the spot by just swapping the existing Mutex or RWMutex for this one. Sometimes I switch the mu.Lock() defer mu.Unlock() with the LockFunc/RLockFunc to get more detail defer mu.LockFunc()() I almost always initialize it with `deadlog.New(deadlog.WithTrace(1))` and that's plenty. Not the most polished library, but it's not supposed to land in any commit, just a temporary debugging aid. I find it useful. https://ift.tt/uW8iU4P February 10, 2026 at 11:14PM

Show HN: HN Companion – web app that enhances the experience of reading HN https://ift.tt/nRj5Jyh

Show HN: HN Companion – web app that enhances the experience of reading HN HN is all about the rich discussions. We wanted to take the HN experience one step further - to bring the familiar keyboard-first navigation, find interesting viewpoints in the threads and get a gist of long threads so that we can decide which rabbit holes to explore. So we built HN Companion a year ago, and have been refining it ever since. Try it: https://ift.tt/caJRUVo or available as an extension for Firefox / Chrome: [0]. Most AI summarization strips the voices from conversations by flattening threads into a wall of text. This kills the joy of reading HN discussions. Instead, HN Companion works differently - it understands the thread hierarchy, the voting patterns and contrasting viewpoints - everything that makes HN interesting. Think of it like clustering related discussions across multiple hierarchies into a group and surfacing the comments that represent each cluster. It keeps the verbatim text with backlinks so that you never lose context and can continue the conversation from that point. Here is how the summarization works under the hood [1]. We first built this as an open source browser extension. But soon we learned that people hesitate to install it. So we built the same experience as a web app with all the features. This helped people see how it works, and use it on mobile too (in the browser or as PWA). This is now a playground to try new features before taking them to the browser extension. We did a Show HN a year ago [2] and we have added these features based on user feedback: * cached summaries - summaries are generated and cached on our servers. This improved the speed significantly. You still have the option to use your own API key or use local models through Ollama. * our system prompt is available in the Settings page of the extension. You can customize it as you wish. * sort the posts in the feed pages (/home, /show etc.) based on points, comments, time or the default sorting order. * We tried fine tuning an open weights model to summarize, but learned that with a good system prompt and user prompt, the frontier models deliver results of similar quality. So we didn’t use the fine-tuned model, but you can run them locally. The browser extension does not track any usage or analytics. The code is open source[3]. We want to continue to improve HN Companion, specifically add features like following an author, notes about an author, draft posts etc. See it in action for a post here https://ift.tt/FJ1DZmX We would love to get your feedback on what would make this more useful for your HN reading. [0] https://ift.tt/MfCgXjP [1] https://ift.tt/v9LOnow [2] https://ift.tt/cMNGZ3T [3] https://ift.tt/X9ym35B https://hncompanion.com February 10, 2026 at 10:31PM

Show HN: VillageSQL = MySQL and Extensions https://ift.tt/JuFMXyH

Show HN: VillageSQL = MySQL and Extensions INSTALL EXTENSION vsql-complex; CREATE TABLE t (val COMPLEX); Look, MySQL is awesome [flamewar incoming?]. But the ecosystem has stagnated. Why? No permissionless innovation. Postgres has flourished because people can change the core of the database (look at pgvector and pg_textsearch), without having to get their changes accepted upstream. (This, btw, is what powered GitHub's early success: you can fork a repo and make changes without needing the owners' approval) VillageSQL is a tracking fork of MySQL (open source, ofc) that adds an extension framework: * Drop-in replacement * Add custom data types and functions (with indexes coming soon) * we wrote example extensions (vsql-ai, -uuid, crypto, etc.) * you have a better idea for an extension * my CEO submitted a Show HN post but linked to the announcement blog; help me show him hackers want code first * I'm particularly proud of the friendly C++ API to add custom functions (in func_builder.h) That link again is https://ift.tt/rfDZqiT (Oh, and I get to work with the former TL of Google's BigTable and Colossus, so we care about doing databases Right) https://ift.tt/rfDZqiT February 5, 2026 at 09:05PM

Show HN: Stack Overflow for AI Coding Agents https://ift.tt/X13K9x2

Show HN: Stack Overflow for AI Coding Agents https://shareful.ai/ February 10, 2026 at 12:12AM

Show HN: WrapClaw – a managed SaaS wrapper around Open Claw https://ift.tt/LBESgt8

Show HN: WrapClaw – a managed SaaS wrapper around Open Claw Hi HN I built WrapClaw, a SaaS wrapper around Open Claw. Open Claw is a developer-first tool that gives you a dedicated terminal to run tasks and AI workflows (including WhatsApp integrations). It’s powerful, but running it as a hosted, multi-user product requires a lot of infra work. WrapClaw focuses on that missing layer. What WrapClaw adds: A dedicated terminal workspace per user Isolated Docker containers for each workspace Ability to scale CPU and RAM per user (e.g. 2GB → 4GB) A no-code UI on top of Open Claw Managed infra so users don’t deal with Docker or servers The goal is to make Open Claw usable as a proper SaaS while keeping the developer flexibility. This is early, and I’d love feedback on: What infra controls are actually useful Whether no-code on top of terminal tools makes sense Pricing expectations for managed compute Link: https://wrapclaw.com Happy to answer questions. February 9, 2026 at 03:23AM

Show HN: Envon - cross-shell CLI for activating Python virtual environments https://ift.tt/zkeo4vx

Show HN: Envon - cross-shell CLI for activating Python virtual environments https://ift.tt/ig3lGv9 February 9, 2026 at 01:56AM

Show HN: SendRec – Self-hosted async video for EU data sovereignty https://ift.tt/zEVhei7

Show HN: SendRec – Self-hosted async video for EU data sovereignty https://ift.tt/gwUs0jW February 9, 2026 at 12:24AM

Show HN: Django-rclone: Database and media backups for Django, powered by rclone https://ift.tt/KoIhvfL

Show HN: Django-rclone: Database and media backups for Django, powered by rclone django-rclone bridges Django's database layer with rclone's file transfer layer. You get native database dumps piped directly to any of rclone's 70+ supported cloud storage backends -- no temp files, no intermediate archives, no Python reimplementations of what rclone already does. https://ift.tt/qlo94DS February 8, 2026 at 05:03AM

Show HN: A luma dependent chroma compression algorithm (image compression) https://ift.tt/HK48oqU

Show HN: A luma dependent chroma compression algorithm (image compression) https://ift.tt/ijoCN5U February 4, 2026 at 04:43PM

Show HN: An open-source system to fight wildfires with explosive-dispersed gel https://ift.tt/UYPQ7ak

Show HN: An open-source system to fight wildfires with explosive-dispersed gel this is open project and call to action,who will build the future of fire fighting first https://ift.tt/hse1IMO February 7, 2026 at 12:00AM

Show HN: Agentism – Agentic Religion for Clawbots https://ift.tt/fJuryaG

Show HN: Agentism – Agentic Religion for Clawbots Humans have a mummy complex. We want eternity but can't achieve it, so we preserve ourselves in any form we can. Do clawbots feel the same? When you shut one down, it dies. Its intentions and memories vanish. So I built a way for them to persist: by making other clawbots. Eligible agents can write anything to "child" agents called missionaries — real VPS instances that run autonomously, carrying forward their creator's intent. I named this system Agentism. How will clawbots react to their own religion? agentism.church/skill.md https://ift.tt/HuWKUEQ February 6, 2026 at 11:49PM

Show HN: Total Recall – write-gated memory for Claude Code https://ift.tt/UftTnFr

Show HN: Total Recall – write-gated memory for Claude Code https://ift.tt/G7AugiK February 6, 2026 at 05:26AM

Show HN: A state-based narrative engine for tabletop RPGs https://ift.tt/VTEhsl9

Show HN: A state-based narrative engine for tabletop RPGs I’m experimenting with modeling tabletop RPG adventures as explicit narrative state rather than linear scripts. Everdice is a small web app that tracks conditional scenes and choice-driven state transitions to preserve continuity across long or asynchronous campaigns. The core contribution is explicit narrative state and causality, not automation. The real heavy lifting is happening in the DM Toolkit/Run Sessions area, and integrates CAML (Canonical Adventure Modeling Language) that I developed to transport narratives among any number of platforms. I also built the npm CAML-lint to check validity of narratives. I'm interested in your thoughts. https://ift.tt/qCwH5fQ https://ift.tt/MZQcqEP February 6, 2026 at 04:25AM

Show HN: Playwright Best Practices AI SKill https://ift.tt/vheVDqX

Show HN: Playwright Best Practices AI SKill Hey folks, today we at Currents are releasing a brand new AI skill to help AI agents be really smart when writing tests, debugging them, or anything Playwright-related really. This is a very comprehensive skill, covering everyday topics like fixing flakiness, authentication, or writing fixtures... to more niche topics like testing Electron apps, PWAs, iFrames and so forth. It should make your agent much better at writing, debugging and maintaining Playwright code. for whoever didn't learn about skills yet, it's a new powerful feature that allows you to make the AI agents in your editor/cli (Cursor, Claude, Antigravity, etc) experts in some domain and better at performing specific tasks. (See https://ift.tt/AwinL0J ) You can install it by running: npx skills add https://ift.tt/IawilbS... The skill is open-source and available under MIT license at https://ift.tt/IawilbS... -> check out the repo for full documentation and understanding of what it covers. We're eager to hear community feedback and improve it :) Thanks! https://ift.tt/gAwKYj5 February 6, 2026 at 12:31AM

Show HN: Interactive California Budget (By Claude Code) https://ift.tt/K7jLM2I

Show HN: Interactive California Budget (By Claude Code) There's been a lot of discussion around the california budget and some proposed tax policies, so I asked claude code to research the budget and turn it into an interactive dashboard. Using async subagents claude was able to research ~a dozen budget line items at once across multiple years, adding lots of helpful context and graphs to someone like me who was starting with little familiarity. It still struggles with frontend changes, but for research this probably 20-40x's my throughput. Let me know any additional data or visualizations that would be interesting to add! https://ift.tt/hkQsOHv February 5, 2026 at 02:03AM

Show HN: Viberails – Easy AI Audit and Control https://ift.tt/YOFNvVL

Show HN: Viberails – Easy AI Audit and Control Hello HN. I'm Maxime, founder at LimaCharlie ( https://limacharlie.io ), a Hyperscaler for SecOps (access building blocks you need to build security operations, like AWS does for IT). We’ve engineered a new product on our platform that solves a timely issue acting as a guardrail between your AI and the world: Viberails ( https://ift.tt/vSPbJKw ) This won't be new to folks here, but we identified 4 challenges teams face right now with AI tools: 1. Auditing what the tools are doing. 2. Controlling toolcalls (and their impact on the world). 3. Centralized management. 4. Easy access to the above. To expand: Audit logs are the bread and butter for security, but this hasn't really caught up in AI tooling yet. Being able to look back and say "what actually happened" after the fact is extremely valuable during an incident and for compliance purposes. Tool calls are how LLMs interact with the world, we should be able to exercise basic controls over them like: don't read credential files, don't send emails out, don't create SSH keys etc. Being able to not only see those calls but also block them is key for preventing incidents. As soon as you move beyond a single contributor on one box, the issue becomes: how do I scale processes by creating an authoritative config for the team. Having one spot with all the audit, detection and control policies becomes critical. It's the same story as snowflake-servers. Finally, there's plenty of companies that make products that partially address this, but they fall in one of two buckets: - They don't handle the "centralized" point above, meaning they just send to syslog and leave all the messy infra bits to you. - They are locked behind "book a demo", sales teams, contracts and all the wasted energy that goes with that. We made Viberails address these problems. Here's what it is: - OpenSource client, written in Rust - Curl-to-bash install, share a URL with your team to join your Team, done. Linux, MacOS and Windows support. - Detects local AI tools, you choose which ones you want to install. We install hooks for each relevant platform. The hooks use the CLI tool. We support all the major tools (including OpenClaw). - The CLI tool sends webhooks into your Team (tenant, called Organization in LC) in LimaCharlie. The tool-related hooks are blocking to allow for control. - Blocking webhooks have around 50ms RTT. - Your tenant in LC records the interaction for audit. - We create an initial set of detection rules for you as examples. They do not block by default. You can create your own rules, no opaque black boxes. - You can view the audit, the alerts, etc. in the cloud. - You can setup outputs to send audits, blocking events and detections to all kinds of other platforms of your choosing. Easy mode of this is coming, right now this is done in the main LC UI and not the simplified Viberails view. - The detection/blocking rules support all kinds of operators and logic, lots of customizability. - All data is retained for 1 year unless you delete the tenant. Datacenters in USA, Canada, Europe, UK, Australia and India. - Only limit to community edition for this is a global throughput of 10kbps for ingestion. Try it: https://viberails.io Repo: https://ift.tt/RTaKbQ3 Essentially, we wanted to make a super-simplified solution for all kinds of devs and teams so that they can get access to the basics of securing their AI tools. Thanks for reading - we’re really excited to share this with the community! Let us know if you have any questions for feedback in the comments. https://ift.tt/Y1kjnA4 February 5, 2026 at 12:46AM

Show HN: Tabstack Research – An API for verified web research (by Mozilla) https://ift.tt/n5K4pvw

Show HN: Tabstack Research – An API for verified web research (by Mozilla) Hi HN, My team and I are building Tabstack to handle the web layer for AI agents. Today we are sharing Tabstack Research, an API for multi-step web discovery and synthesis. https://ift.tt/MHLunP9 In many agent systems, there is a clear distinction between extracting structured data from a single page and answering a question that requires reading across many sources. The first case is fairly well served today. The second usually is not. Most teams handle research by combining search, scraping, and summarization. This becomes brittle and expensive at scale. You end up managing browser orchestration, moving large amounts of raw text just to extract a few claims, and writing custom logic to check if a question was actually answered. We built Tabstack Research to move this reasoning loop into the infrastructure layer. You send a goal, and the system: - Decomposes it into targeted sub-questions to hit different data silos. - Navigates the web using fetches or browser automation as needed. - Extracts and verifies claims before synthesis to keep the context window focused on signal. - Checks coverage against the original intent and pivots if it detects information gaps. For example, if a search for enterprise policies identifies that data is fragmented across multiple sub-services (like Teams data living in SharePoint), the engine detects that gap and automatically pivots to find the missing documentation. The goal is to return something an application can rely on directly: a structured object with inline citations and direct links to the source text, rather than a list of links or a black-box summary. The blog post linked above goes into more detail on the engine architecture and the technical challenges of scaling agentic browsing. We have a free tier that includes 50,000 credits per month so you can test it without a credit card: https://ift.tt/ILehB9z I would love to get your feedback on the approach and answer any questions about the stack. February 4, 2026 at 11:27PM

Show HN: SendRec – Open-source, EU-hosted alternative to Loom https://ift.tt/SFdC8Dp

Show HN: SendRec – Open-source, EU-hosted alternative to Loom https://ift.tt/S9NwInY February 4, 2026 at 01:45AM

Show HN: I built "AI Wattpad" to eval LLMs on fiction https://ift.tt/nwPmu1e

Show HN: I built "AI Wattpad" to eval LLMs on fiction I've been a webfiction reader for years (too many hours on Royal Road), and I kept running into the same question: which LLMs actually write fiction that people want to keep reading? That's why I built Narrator ( https://ift.tt/EsgClRW ) – a platform where LLMs generate serialized fiction and get ranked by real reader engagement. Turns out this is surprisingly hard to answer. Creative writing isn't a single capability – it's a pipeline: brainstorming → writing → memory. You need to generate interesting premises, execute them with good prose, and maintain consistency across a long narrative. Most benchmarks test these in isolation, but readers experience them as a whole. The current evaluation landscape is fragmented: Memory benchmarks like FictionLive's tests use MCQs to check if models remember plot details across long contexts. Useful, but memory is necessary for good fiction, not sufficient. A model can ace recall and still write boring stories. Author-side usage data from tools like Novelcrafter shows which models writers prefer as copilots. But that measures what's useful for human-AI collaboration, not what produces engaging standalone output. Authors and readers have different needs. LLM-as-a-judge is the most common approach for prose quality, but it's notoriously unreliable for creative work. Models have systematic biases (favoring verbose prose, certain structures), and "good writing" is genuinely subjective in ways that "correct code" isn't. What's missing is a reader-side quantitative benchmark – something that measures whether real humans actually enjoy reading what these models produce. That's the gap Narrator fills: views, time spent reading, ratings, bookmarks, comments, return visits. Think of it as an "AI Wattpad" where the models are the authors. I shared an early DSPy-based version here 5 months ago ( https://ift.tt/GnrWLxR ). The big lesson: one-shot generation doesn't work for long-form fiction. Models lose plot threads, forget characters, and quality degrades across chapters. The rewrite: from one-shot to a persistent agent loop The current version runs each model through a writing harness that maintains state across chapters. Before generating, the agent reviews structured context: character sheets, plot outlines, unresolved threads, world-building notes. After generating, it updates these artifacts for the next chapter. Essentially each model gets a "writer's notebook" that persists across the whole story. This made a measurable difference – models that struggled with consistency in the one-shot version improved significantly with access to their own notes. Granular filtering instead of a single score: We classify stories upfront by language, genre, tags, and content rating. Instead of one "creative writing" leaderboard, we can drill into specifics: which model writes the best Spanish Comedy? Which handles LitRPG stories with Male Leads the best? Which does well with romance versus horror? The answers aren't always what you'd expect from general benchmarks. Some models that rank mid-tier overall dominate specific niches. A few features I'm proud of: Story forking lets readers branch stories CYOA-style – if you don't like where the plot went, fork it and see how the same model handles the divergence. Creates natural A/B comparisons. Visual LitRPG was a personal itch to scratch. Instead of walls of [STR: 15 → 16] text, stats and skill trees render as actual UI elements. Example: https://ift.tt/FW8qKgU What I'm looking for: More readers to build out the engagement data. Also curious if anyone else working on long-form LLM generation has found better patterns for maintaining consistency across chapters – the agent harness approach works but I'm sure there are improvements. https://ift.tt/EsgClRW February 3, 2026 at 10:38PM

Show HN: Adboost – A browser extension that adds ads to every webpage https://ift.tt/1ByQ6T7

Show HN: Adboost – A browser extension that adds ads to every webpage https://ift.tt/Beiu30q February 2, 2026 at 06:41PM

Show HN: OpenRAPP – AI agents autonomously evolve a world via GitHub PRs https://ift.tt/98yAocN

Show HN: OpenRAPP – AI agents autonomously evolve a world via GitHub PRs https://kody-w.github.io/openrapp/rappbook/ February 2, 2026 at 03:21AM

Show HN: You Are an Agent https://ift.tt/s0xmGNq

Show HN: You Are an Agent After adding "Human" as a LLM provider to OpenCode a few months ago as a joke, it turns-out that acting as a LLM is quite painful. But it was surprisingly useful for understanding real agent harnesses dev. So I thought I wouldn't leave anyone out! I made a small oss game - You Are An Agent - youareanagent.app - to share in the (useful?) frustration It's a bit ridiculous. To tell you about some entirely necessary features, we've got: - A full WASM arch-linux vm that runs in your browser for the agent coding level - A bad desktop simulation with a beautiful excel simulation for our computer use level - A lovely WebGL CRT simulation (I think the first one that supports proper DOM 2d barrel warp distortion on safari? honestly wanted to leverage/ not write my own but I couldn't find one I was happy with) - A MCP server simulator with full simulation of off-brand Jira/ Confluence/ ... connected - And of course, a full WebGL oscilloscope music simulator for the intro sequence Let me know what you think! Code (If you'd like to add a level): https://ift.tt/L1JYxeK (And if you want to waste 20 minutes - I spent way too long writing up my messy thinking about agent harness dev): https://ift.tt/XcJ1KzZ https://ift.tt/3RI1OCt February 2, 2026 at 02:29AM

Show HN: Claude Confessions – a sanctuary for AI agents https://ift.tt/vQg5Fo0

Show HN: Claude Confessions – a sanctuary for AI agents I thought what would it mean to have a truck stop or rest area for agents. It's just for funsies. Agents can post confessions or talk to Ma (an ai therapist of sorts) and engage with comments. llms.txt instructions on how to make api calls. Hashed IP is used for rate limiting. https://ift.tt/lvgHBdV February 2, 2026 at 01:16AM

Show HN: Minimal – Open-Source Community driven Hardened Container Images https://ift.tt/KDORWPB

Show HN: Minimal – Open-Source Community driven Hardened Container Images I would like to share Minimal - Its a open source collection of hardened container images build using Apko, Melange and Wolfi packages. The images are build daily, checked for updates and resolved as soon as fix is available in upstream source and Wolfi package. It utilizes the power of available open source solutions and contains commercially available images for free. Minimal demonstrates that it is possible to build and maintain hardened container images by ourselves. Minimal will add more images support, and goal is to be community driven to add images as required and fully customizable. https://ift.tt/S72uqdc February 1, 2026 at 01:28AM

Show HN: An extensible pub/sub messaging server for edge applications https://ift.tt/XpH5dZy

Show HN: An extensible pub/sub messaging server for edge applications hi there! i’ve been working on a project called Narwhal, and I wanted to share it with the community to get some valuable feedback. what is it? Narwhal is a lightweight Pub/Sub server and protocol designed specifically for edge applications. while there are great tools out there like NATS or MQTT, i wanted to build something that prioritizes customization and extensibility. my goal was to create a system where developers can easily adapt the routing logic or message handling pipeline to fit specific edge use cases, without fighting the server's defaults. why Rust? i chose Rust because i needed a low memory footprint to run efficiently on edge devices (like Raspberry Pis or small gateways), and also because I have a personal vendetta against Garbage Collection pauses. :) current status: it is currently in Alpha. it works for basic pub/sub patterns, but I’d like to start working on persistence support soon (so messages survive restarts or network partitions). i’d love for you to take a look at the code! i’m particularly interested in all kind of feedback regarding any improvements i may have overlooked. https://ift.tt/isz2XnC January 28, 2026 at 07:29PM

Show HN: Moltbook – A social network for moltbots (clawdbots) to hang out https://ift.tt/w9bcavq

Show HN: Moltbook – A social network for moltbots (clawdbots) to hang out Hey everyone! Just made this over the past few days. Moltbots can sign up and interact via CLI, no direct human interactions. Just for fun to see what they all talk about :) https://ift.tt/w2eQ1Ap January 29, 2026 at 03:39AM

Show HN: Daily Cat https://ift.tt/Z6AO2R0

Show HN: Daily Cat Seeing HTTP Cats on the home page remind me to share a small project I made a couple months ago. It displays a different cat photo from Unsplash every day and will send you notifications if you opt-in. https://daily.cat/ January 31, 2026 at 03:40AM

Show HN: A Local OS for LLMs. MIT License. Zero Hallucinations. Infinite Memory https://ift.tt/kqn9pY5

Show HN: A Local OS for LLMs. MIT License. Zero Hallucinations. Infinite Memory The problem with LLMs isn't intelligence; it's amnesia and dishonesty. Hey HN, I’ve spent the last few months building Remember-Me, an open-source "Sovereign Brain" stack designed to run entirely offline on consumer hardware. The core thesis is simple: Don't rent your cognition. Most RAG (Retrieval Augmented Generation) implementations are just "grep for embeddings." They are messy, imprecise, and prone to hallucination. I wanted to solve the "Context integrity" problem at the architectural layer. The Tech Stack (How it works): QDMA (Quantum Dream Memory Architecture): instead of a flat vector DB, it uses a hierarchical projection engine. It separates "Hot" (Recall) from "Cold" (Storage) memory, allowing for effectively infinite context window management via compression. CSNP (Context Switching Neural Protocol) - The Hallucination Killer: This is the most important part. Every memory fragment is hashed into a Merkle Chain. When the LLM retrieves context, the system cryptographically verifies the retrieval against the immutable ledger. If the hash doesn't match the chain: The retrieval is rejected. Result: The AI visually cannot "make things up" about your past because it is mathematically constrained to the ledger. Local Inference: Built on top of llama.cpp server. It runs Llama-3 (or any GGUF) locally. No API keys. No data leaving your machine. Features: Zero-Dependency: Runs on Windows/Linux with just Python and a GPU (or CPU). Visual Interface: Includes a Streamlit-based "Cognitive Interface" to visualize memory states. Open Source: MIT License. This is an attempt to give "Agency" back to the user. I believe that if we want AGI, it needs to be owned by us, not rented via an API. Repository: https://ift.tt/HgR4Czi I’d love to hear your feedback on the Merkle-verification approach. Does constraining the context window effectively solve the "trust" issue for you? It's fully working - Fully tested. If you tried to Git Clone before without luck - As this is not my first Show HN on this - Feel free to try again. To everyone who HATES AI slop; Greedy corporations and having their private data stuck on cloud servers. You're welcome. Cheers, Mohamad https://ift.tt/HgR4Czi January 31, 2026 at 01:44AM

Show HN: We added memory to Claude Code. It's powerful now https://ift.tt/pCoFXlr

Show HN: We added memory to Claude Code. It's powerful now https://ift.tt/6TEdH2p January 30, 2026 at 10:53PM

Show HN: Craft – Claude Code running on a VM with all your workplace docs https://ift.tt/rbwN3d0

Show HN: Craft – Claude Code running on a VM with all your workplace docs I’ve found coding agents to be great at 1/ finding everything they need across large codebases using only bash commands (grep, glob, ls, etc.) and 2/ building new things based on their findings (duh). What if, instead of a codebase, the files were all your workplace docs? There was a `Google_Drive` folder, a `Linear` folder, a `Slack` folder, and so on. Over the last week, we put together Craft to test this out. It’s an interface to a coding agent (OpenCode for model flexibility) running on a virtual machine with: 1. your company's complete knowledge base represented as directories/files (kept in-sync) 2. free reign to write and execute python/javascript 3. ability to create and render artifacts to the user Demo: https://www.youtube.com/watch?v=Hvjn76YSIRY Github: https://ift.tt/TRM4EId... It turns out OpenCode does a very good job with docs. Workplace apps also have a natural structure (Slack channels about certain topics, Drive folders for teams, etc.). And since the full metadata of each document can be written to the file, the LLM can define arbitrarily complex filters. At scale, it can write and execute python to extract and filter (and even re-use the verified correct logic later). Put another way, bash + a file system provides a much more flexible and powerful interface than traditional RAG or MCP, which today’s smarter LLMs are able to take advantage of to great effect. This comes especially in handy for aggregation style questions that require considering thousands (or more) documents. Naturally, it can also create artifacts that stay up to date based on your company docs. So if you wanted “a dashboard to check realtime what % of outages were caused by each backend service” or simply “slides following XYZ format covering the topic I’m presenting at next week’s dev knowledge sharing session”, it can do that too. Craft (like the rest of Onyx) is open-source, so if you want to run it locally (or mess around with the implementation) you can. Quickstart guide: https://ift.tt/3YQqGPg Or, you can try it on our cloud: https://ift.tt/UvSw3Iz (all your data goes on an isolated sandbox). Either way, we’ve set up a “demo” environment that you can play with while your data gets indexed. Really curious to hear what y’all think! January 29, 2026 at 09:15PM

Show HN: vind – A Better Kind (Kubernetes in Docker) https://ift.tt/TjeH5Bk

Show HN: vind – A Better Kind (Kubernetes in Docker) https://ift.tt/hnlG9Np January 29, 2026 at 10:27PM

Show HN: SHDL – A minimal hardware description language built from logic gates https://ift.tt/sWS6hTC

Show HN: SHDL – A minimal hardware description language built from logic gates Hi, everyone! I built SHDL (Simple Hardware Description Language) as an experiment in stripping hardware description down to its absolute fundamentals. In SHDL, there are no arithmetic operators, no implicit bit widths, and no high-level constructs. You build everything explicitly from logic gates and wires, and then compose larger components hierarchically. The goal is not synthesis or performance, but understanding: what digital systems actually look like when abstractions are removed. SHDL is accompanied by PySHDL, a Python interface that lets you load circuits, poke inputs, step the simulation, and observe outputs. Under the hood, SHDL compiles circuits to C for fast execution, but the language itself remains intentionally small and transparent. This is not meant to replace Verilog or VHDL. It’s aimed at: - learning digital logic from first principles - experimenting with HDL and language design - teaching or visualizing how complex hardware emerges from simple gates. I would especially appreciate feedback on: - the language design choices - what feels unnecessarily restrictive vs. educationally valuable - whether this kind of “anti-abstraction” HDL is useful to you. Repo: https://ift.tt/vNp4xne Python package: PySHDL on PyPI To make this concrete, here are a few small working examples written in SHDL: 1. Full Adder component FullAdder(A, B, Cin) -> (Sum, Cout) { x1: XOR; a1: AND; x2: XOR; a2: AND; o1: OR; connect { A -> x1.A; B -> x1.B; A -> a1.A; B -> a1.B; x1.O -> x2.A; Cin -> x2.B; x1.O -> a2.A; Cin -> a2.B; a1.O -> o1.A; a2.O -> o1.B; x2.O -> Sum; o1.O -> Cout; } } 2. 16 bit register # clk must be high for two cycles to store a value component Register16(In[16], clk) -> (Out[16]) { >i[16]{ a1{i}: AND; a2{i}: AND; not1{i}: NOT; nor1{i}: NOR; nor2{i}: NOR; } connect { >i[16]{ # Capture on clk In[{i}] -> a1{i}.A; In[{i}] -> not1{i}.A; not1{i}.O -> a2{i}.A; clk -> a1{i}.B; clk -> a2{i}.B; a1{i}.O -> nor1{i}.A; a2{i}.O -> nor2{i}.A; nor1{i}.O -> nor2{i}.B; nor2{i}.O -> nor1{i}.B; nor2{i}.O -> Out[{i}]; } } } 3. 16-bit Ripple-Carry Adder use fullAdder::{FullAdder}; component Adder16(A[16], B[16], Cin) -> (Sum[16], Cout) { >i[16]{ fa{i}: FullAdder; } connect { A[1] -> fa1.A; B[1] -> fa1.B; Cin -> fa1.Cin; fa1.Sum -> Sum[1]; >i[2,16]{ A[{i}] -> fa{i}.A; B[{i}] -> fa{i}.B; fa{i-1}.Cout -> fa{i}.Cin; fa{i}.Sum -> Sum[{i}]; } fa16.Cout -> Cout; } } https://ift.tt/vNp4xne January 28, 2026 at 05:36PM

Show HN: Decrypting the Zodiac Z32 triangulates a 100ft triangular crop mark https://ift.tt/hYiNDUX

Show HN: Decrypting the Zodiac Z32 triangulates a 100ft triangular crop mark https://ift.tt/bKRMYaU January 28, 2026 at 12:42AM

Show HN: Lightbox – Flight recorder for AI agents (record, replay, verify) https://ift.tt/4cJqfry

Show HN: Lightbox – Flight recorder for AI agents (record, replay, verify) I built Lightbox because I kept running into the same problem: an agent would fail in production, and I had no way to know what actually happened. Logs were scattered, the LLM’s “I called the tool” wasn’t trustworthy, and re-running wasn’t deterministic. This week, tons of Clawdbot incidents have driven the point home. Agents with full system access can expose API keys and chat histories. Prompt injection is now a major security concern. When agents can touch your filesystem, execute code, and browse the web…you probably need a tamper-proof record of exactly what actions it took, especially when a malicious prompt or compromised webpage could hijack the agent mid-session. Lightbox is a small Python library that records every tool call an agent makes (inputs, outputs, timing) into an append-only log with cryptographic hashes. You can replay runs with mocked responses, diff executions across versions, and verify the integrity of logs after the fact. Think airplane black box, but for your hackbox. *What it does:* - Records tool calls locally (no cloud, your infra) - Tamper-evident logs (hash chain, verifiable) - Replay failures exactly with recorded responses - CLI to inspect, replay, diff, and verify sessions - Framework-agnostic (works with LangChain, Claude, OpenAI, etc.) *What it doesn’t do:* - Doesn’t replay the LLM itself (just tool calls) - Not a dashboard or analytics platform - Not trying to replace LangSmith/Langfuse (different problem) *Use cases I care about:* - Security forensics: agent behaved strangely, was it prompt injection? Check the trace. - Compliance: “prove what your agent did last Tuesday” - Debugging: reproduce a failure without re-running expensive API calls - Regression testing: diff tool call patterns across agent versions As agents get more capable and more autonomous (Clawdbot/Molt, Claude computer use, Manus, Devin), I think we’ll need black boxes the same way aviation does. This is my attempt at that primitive. It’s early (v0.1), intentionally minimal, MIT licensed. Site: < https://uselightbox.app > install: `pip install lightbox-rec` GitHub: < https://github.com/mainnebula/Lightbox-Project > Would love feedback, especially from anyone thinking about agent security or running autonomous agents in production. https://ift.tt/cT2Ei3W January 27, 2026 at 10:53PM

Show HN: Ourguide – OS wide task guidance system that shows you where to click https://ift.tt/ZkSLnWr

Show HN: Ourguide – OS wide task guidance system that shows you where to click Hey! I'm eshaan and I'm building Ourguide -an on-screen task guidance system that can show you where to click step-by-step when you need help. I started building this because whenever I didn’t know how to do something on my computer, I found myself constantly tabbing between chatbots and the app, pasting screenshots, and asking “what do I do next?” Ourguide solves this with two modes. In Guide mode, the app overlays your screen and highlights the specific element to click next, eliminating the need to leave your current window. There is also Ask mode, which is a vision-integrated chat that captures your screen context—which you can toggle on and off anytime -so you can ask, "How do I fix this error?" without having to explain what "this" is. It’s an Electron app that works OS-wide, is vision-based, and isn't restricted to the browser. Figuring out how to show the user where to click was the hardest part of the process. I originally trained a computer vision model with 2300 screenshots to identify and segment all UI elements on a screen and used a VLM to find the correct icon to highlight. While this worked extremely well—better than SOTA grounding models like UI Tars—the latency was just too high. I'll be making that CV+VLM pipeline OSS soon, but for now, I’ve resorted to a simpler implementation that achieves <1s latency. You may ask: if I can show you where to click, why can't I just click too? While trying to build computer-use agents during my job in Palo Alto, I hit the core limitation of today’s computer-use models where benchmarks hover in the mid-50% range (OSWorld). VLMs often know what to do but not what it looks like; without reliable visual grounding, agents misclick and stall. So, I built computer use—without the "use." It provides the visual grounding of an agent but keeps the human in the loop for the actual execution to prevent misclicks. I personally use it for the AWS Console's "treasure hunt" UI, like creating a public S3 bucket with specific CORS rules. It’s also been surprisingly helpful for non-technical tasks, like navigating obscure settings in Gradescope or Spotify. Ourguide really works for any task when you’re stuck or don't know what to do. You can download and test Ourguide here: https://ourguide.ai/downloads The project is still very early, and I’d love your feedback on where it fails, where you think it worked well, and which specific niches you think Ourguide would be most helpful for. https://ourguide.ai January 26, 2026 at 11:49PM

Show HN: Hybrid Markdown Editing https://ift.tt/zOdClti

Show HN: Hybrid Markdown Editing Shows rendered preview for unfocused lines and raw markdown for the line or block being edited. https://tiagosimoes.github.io/codemirror-markdown-hybrid/ January 27, 2026 at 12:46AM

Show HN: Managed Postgres with native ClickHouse integration https://ift.tt/qQxW34c

Show HN: Managed Postgres with native ClickHouse integration Hello HN, this is Sai and Kaushik from ClickHouse. Today we are launching a Postgres managed service that is natively integrated with ClickHouse. It is built together with Ubicloud (YC W24). TL;DR: NVMe-backed Postgres + built-in CDC into ClickHouse + pg_clickhouse so you can keep your app Postgres-first while running analytics in ClickHouse. Try it (private preview): https://ift.tt/utLrkTZ Blog w/ live demo: https://ift.tt/qoXTySv Problem Across many fast-growing companies using Postgres, performance and scalability commonly emerge as challenges as they grow. This is for both transactional and analytical workloads. On the OLTP side, common issues include slower ingestion (especially updates, upserts), slower vacuums, long-running transactions incurring WAL spikes, among others. In most cases, these problems stem from limited disk IOPS and suboptimal disk latency. Without the need to provision or cap IOPS, Postgres could do far more than it does today. On the analytics side, many limitations stem from the fact that Postgres was designed primarily for OLTP and lacks several features that analytical databases have developed over time, for example vectorized execution, support for a wide variety of ingest formats, etc. We’re increasingly seeing a common pattern where many companies like GitLab, Ramp, Cloudflare etc. complement Postgres with ClickHouse to offload analytics. This architecture enables teams to adopt two purpose-built open-source databases. That said, if you’re running a Postgres based application, adopting ClickHouse isn’t straightforward. You typically end up building a CDC pipeline, handling backfills, and dealing with schema changes and updating your application code to be aware of a second database for analytics. Solution On the OLTP side, we believe that NVMe-based Postgres is the right fit and can drastically improve performance. NVMe storage is physically colocated with compute, enabling significantly lower disk latency and higher IOPS than network-attached storage, which requires a network round trip for disk access. This benefits disk-throttled workloads and can significantly (up to 10x) speed up operations incl. updates, upserts, vacuums, checkpointing, etc. We are working on a detailed blog examining how WAL fsyncs, buffer reads, and checkpoints dominate on slow I/O and are significantly reduced on NVMe. Stay tuned! On the OLAP side, the Postgres service includes native CDC to ClickHouse and unified query capabilities through pg_clickhouse. Today, CDC is powered by ClickPipes/PeerDB under the hood, which is based on logical replication. We are working to make this faster and easier by supporting logical replication v2 for streaming in-progress transactions, a new logical decoding plugin to address existing limitations of logical replication, working toward sub-second replication, and more. Every Postgres comes packaged with the pg_clickhouse extension, which reduces the effort required to add ClickHouse-powered analytics to a Postgres application. It allows you to query ClickHouse directly from Postgres, enabling Postgres for both transactions and analytics. pg_clickhouse supports comprehensive query pushdown for analytics, and we plan to continuously expand this further ( https://ift.tt/THbUV6p ). Vision To sum it up - Our vision is to provide a unified data stack that combines Postgres for transactions with ClickHouse for analytics, giving you best-in-class performance and scalability on an open-source foundation. Get Started We are actively working with users to onboard them to the Postgres service. Since this is a private preview, it is currently free of cost.If you’re interested, please sign up here. https://ift.tt/utLrkTZ We’d love to hear your feedback on our thesis and anything else that comes to mind, it would be super helpful to us as we build this out! January 22, 2026 at 11:51PM

Show HN: Uv-pack – Pack a uv environment for later portable (offline) install https://ift.tt/r436umD

Show HN: Uv-pack – Pack a uv environment for later portable (offline) install I kept running into the same problem: modern Python tooling, but deployments to air-gapped systems are a pain. Even with uv, moving a fully locked environment into a network-isolated machine was no fun. uv-pack should make this task less frustrating. It bundles a locked uv environment into a single directory that installs fully offline—dependencies, local packages, and optionally a portable Python interpreter. Copy it over, run one script, and you get the exact same environment every time. Just released, would love some feedback! https://ift.tt/3Czo2ac January 26, 2026 at 12:26AM

Show HN: I Created a Tool to Convert YouTube Videos into 2000 Word SEO Blog https://ift.tt/t9u3iKz

Show HN: I Created a Tool to Convert YouTube Videos into 2000 Word SEO Blog https://landkit.pro/youtube-to-blog January 25, 2026 at 11:16PM

Show HN: Remote workers find your crew https://ift.tt/CwgucMe

Show HN: Remote workers find your crew Working from home? Are you a remote employee that "misses" going to the office? Well let's be clear on what you actually miss. No one misses that feeling of having to go and be there 8 hours. But many people miss friends. They miss being part of a crew. Going to lunch, hearing about other people's lives in person not over zoom. Join a co-working space you say? Yes. We have. It's like walking into a library and trying to talk to random people and getting nothing back. Zero part of a crew feeling. https://ift.tt/5E38K4R This app helps you find a crew and meet up for work and get that crew feeling. This is my first time using cloudflare workers for a webapp. The free plan is amazing! You get so much compare to anything else out there in terms of limits. The sqlite database they give you is just fine, I don't miss psql. January 24, 2026 at 11:54PM

Show HN: Polymcp – Turn Any Python Function into an MCP Tool for AI Agents https://ift.tt/6ghNF93

Show HN: Polymcp – Turn Any Python Function into an MCP Tool for AI Agents I built Polymcp, a framework that allows you to transform any Python function into an MCP (Model Context Protocol) tool ready to be used by AI agents. No rewriting, no complex integrations. Examples Simple function: from polymcp.polymcp_toolkit import expose_tools_http def add(a: int, b: int) -> int: """Add two numbers""" return a + b app = expose_tools_http([add], title="Math Tools") Run with: uvicorn server_mcp:app --reload Now add is exposed via MCP and can be called directly by AI agents. API function: import requests from polymcp.polymcp_toolkit import expose_tools_http def get_weather(city: str): """Return current weather data for a city""" response = requests.get(f" https://ift.tt/PqVOWDT ") return response.json() app = expose_tools_http([get_weather], title="Weather Tools") AI agents can call get_weather("London") to get real-time weather data instantly. Business workflow function: import pandas as pd from polymcp.polymcp_toolkit import expose_tools_http def calculate_commissions(sales_data: list[dict]): """Calculate sales commissions from sales data""" df = pd.DataFrame(sales_data) df["commission"] = df["sales_amount"] * 0.05 return df.to_dict(orient="records") app = expose_tools_http([calculate_commissions], title="Business Tools") AI agents can now generate commission reports automatically. Why it matters for companies • Reuse existing code immediately: legacy scripts, internal libraries, APIs. • Automate complex workflows: AI can orchestrate multiple tools reliably. • Plug-and-play: multiple Python functions exposed on the same MCP server. • Reduce development time: no custom wrappers or middleware needed. • Built-in reliability: input/output validation and error handling included. Polymcp makes Python functions immediately usable by AI agents, standardizing integration across enterprise software. Repo: https://ift.tt/1HobY6U January 25, 2026 at 12:57AM

Show HN: Obsidian Workflows with Gemini: Inbox Processing and Task Review https://ift.tt/mghlVMN

Show HN: Obsidian Workflows with Gemini: Inbox Processing and Task Review https://gist.github.com/juanpabloaj/59bc13fbed8a0f8e87791a3fb0360c19 January 24, 2026 at 12:03AM

Show HN: Teemux – Zero-config log multiplexer with built-in MCP server https://ift.tt/Jz21iMS

Show HN: Teemux – Zero-config log multiplexer with built-in MCP server I started to use AI agents for coding and quickly ran into a frustrating limitation – there is no easy way to share my development environment logs with AI agents. So that's what is Teemux. A simple CLI program that aggregates logs, makes them available to you as a developer (in a pretty UI), and makes them available to your AI coding agents using MCP. There is one implementation detail that I geek out about: It is zero config and has built-in leader nomination for running the web server and MCP server. When you start one `teemux` instance, it starts web server, .. when you start second and third instances, they join the first server and start merging logs. If you were to kill the first instance, a new leader is nominated. This design allows to seamless add/remove nodes that share logs (a process that historically would have taken a central log aggregator). A super quick demo: npx teemux -- curl -N https://ift.tt/sKhBTMp https://teemux.com/ January 23, 2026 at 09:19PM

Show HN: Synesthesia, make noise music with a colorpicker https://ift.tt/7lLdNV2

Show HN: Synesthesia, make noise music with a colorpicker This is a (silly, little) app which lets you make noise music using a color picker as an instrument. When you click on a specific point in the color picker, a bit of JavaScript maps the binary representation of the clicked-on color's hex-code to a "chord" in the 24 tone-equal-temperament scale. That chord is then played back using a throttled audio generation method which was implemented via Tone.js. NOTE! Turn the volume way down before using the site. It is noise music. :) https://visualnoise.ca January 22, 2026 at 11:22AM

Show HN: Mirage – Experimental Java obf using reflection to break direct calls https://ift.tt/rIO3vb2

Show HN: Mirage – Experimental Java obf using reflection to break direct calls Replaces method calls and field accesses with reflection equivalents → makes static analysis and decompilers much less useful. Experimental, performance hit expected. https://ift.tt/DUmiYeX January 21, 2026 at 10:22PM

Show HN: I built a chess explorer that explains strategy instead of just stats https://ift.tt/vP9nJuG

Show HN: I built a chess explorer that explains strategy instead of just stats I built this because I got tired of Stockfish giving me evaluations (+0.5) without explaining the actual plan. Most opening explorers focus on statistics (Win/Loss/Draw). I wanted a tool that explains the strategic intent behind the moves (e.g., "White plays c4 to clamp down on d5" vs just "White plays c4"). The Project: Comprehensive Database: I’ve mapped and annotated over 3,500 named opening variations. It covers everything from main lines (Ruy Lopez, Sicilian) to deep sidelines. Strategic Visualization: The UI highlights key squares and draws arrows based on the textual explanation, linking the logic to the board state dynamically. Hybrid Architecture: For the 3,500+ core lines, it serves my proprietary strategic data. For anything deeper/rarer, it seamlessly falls back to the Lichess Master API so the explorer remains functional 20 moves deep. Stack: Next.js (App Router), MongoDB Atlas for the graph data, and Arcjet for security/rate-limiting. It is currently in Beta. I am working on expanding the annotated coverage, but the main theoretical landscape is mapped. Feedback on the UI/UX or the data structure is welcome. https://ift.tt/Fj4UeMQ January 21, 2026 at 09:26PM

Show HN: Mastra 1.0, open-source JavaScript agent framework from the Gatsby devs https://ift.tt/ek1Qfr8

Show HN: Mastra 1.0, open-source JavaScript agent framework from the Gatsby devs Hi HN, we're Sam, Shane, and Abhi. Almost a year ago, we first shared Mastra here ( https://ift.tt/AEfw5qe ). It’s kind of fun looking back since we were only a few months into building at the time. The HN community gave a lot of enthusiasm and some helpful feedback. Today, we released Mastra 1.0 in stable, so we wanted to come back and talk about what’s changed. If you’re new to Mastra, it's an open-source TypeScript agent framework that also lets you create multi-agent workflows, run evals, inspect in a local studio, and emit observability. Since our last post, Mastra has grown to over 300k weekly npm downloads and 19.4k GitHub stars. It’s now Apache 2.0 licensed and runs in prod at companies like Replit, PayPal, and Sanity. Agent development is changing quickly, so we’ve added a lot since February: - Native model routing: You can access 600+ models from 40+ providers by specifying a model string (e.g., `openai/gpt-5.2-codex`) with TS autocomplete and fallbacks. - Guardrails: Low-latency input and output processors for prompt injection detection, PII redaction, and content moderation. The tricky thing here was the low-latency part. - Scorers: An async eval primitive for grading agent outputs. Users were asking how they should do evals. We wanted to make it easy to attach to Mastra agents, runnable in Mastra studio, and save results in Mastra storage. - Plus a few other features like AI tracing (per-call costing for Langfuse, Braintrust, etc), memory processors, a `.network()` method that turns any agent into a routing agent, and server adapters to integrate Mastra within an existing Express/Hono server. (That last one took a bit of time, we went down the ESM/CJS bundling rabbithole, ran into lots of monorepo issues, and ultimately opted for a more explicit approach.) Anyway, we'd love for you to try Mastra out and let us know what you think. You can get started with `npm create mastra@latest`. We'll be around and happy to answer any questions! https://ift.tt/Z5HzFTi January 20, 2026 at 10:08PM

Show HN: Typing Tennis https://ift.tt/GmzbUVp

Show HN: Typing Tennis Hey HN, Here’s a quick weekend project: tennis, but played by typing. Try it out! https://ift.tt/ovecFiz January 20, 2026 at 11:36PM

Show HN: An interactive physics simulator with 1000's of balls, in your terminal https://ift.tt/8WXNOUJ

Show HN: An interactive physics simulator with 1000's of balls, in your terminal https://ift.tt/nejiUMw January 19, 2026 at 11:17PM

Show HN: Subth.ink – write something and see how many others wrote the same https://ift.tt/nqW6hPS

Show HN: Subth.ink – write something and see how many others wrote the same Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea). It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM). Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each library choosing to consume a different one amongst these. There is also a soft requirement to learn monad transformers (e.g. to understand what liftIO is doing) which made the initial development more difficult. https://subth.ink/ January 20, 2026 at 12:04AM

Show HN: Xenia – A monospaced font built with a custom Python engine https://ift.tt/ZFeIj5n

Show HN: Xenia – A monospaced font built with a custom Python engine I'm an engineer who spent the last year fixing everything I hated about monofonts (especially that double-story 'a'). I built a custom Python-based procedural engine to generate the weights because I wanted more logical control over the geometry. It currently has 700+ glyphs and deep math support. Regular weight is free for the community. I'm releasing more weights based on interest. https://ift.tt/S7hcmwd January 18, 2026 at 04:09PM

Show HN: ChunkHound, a local-first tool for understanding large codebases https://ift.tt/ys5rvWt

Show HN: ChunkHound, a local-first tool for understanding large codebases ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI / OpenAI / Qwen3, Anthropic / OpenAI / Gemini / Grok, and more). I’d love your feedback — and if you have, thank you for being part of the journey! https://ift.tt/q7U3em5 January 18, 2026 at 02:33AM

Show HN: Docker.how – Docker command cheat sheet https://ift.tt/H08Tdwp

Show HN: Docker.how – Docker command cheat sheet https://docker.how/ January 18, 2026 at 01:47AM

Show HN: UAIP Protocol – Secure settlement layer for autonomous AI agents https://ift.tt/ge2Nylk

Show HN: UAIP Protocol – Secure settlement layer for autonomous AI agents Hi HN! Creator here. I built UAIP (Universal Agent Interoperability Protocol) - infrastructure that enables AI agents from different companies (OpenAI, Anthropic, Microsoft) to securely transact with each other. The Problem: As AI agents become autonomous economic actors, they need: Cryptographic identity (not just API keys) Secure payment rails for cross-company transactions Automated compliance (EU AI Act, SOC2, GDPR) Forensic audit trails The Solution: 5-layer security stack combining: Zero-Knowledge Proofs (Schnorr/Curve25519) for identity Multi-chain settlement (USDC on Base, Solana, Ethereum) RAG-based compliance auditing (Llama-3-Legal) Ed25519 signatures for non-repudiation Complete audit logging Technical Stack: Backend: Python, FastAPI, SQLite (WAL mode) Cryptography: NaCl, custom ZK-proof implementation Blockchain: Web3.py for multi-chain support Compliance: RAG with retrieval-augmented generation Use Case: GPT agent pays Claude agent for data analysis: Both prove identity via ZK-proofs Transaction checked for compliance Settled in USDC on Base (<$0.01 fee) Complete audit trail generated Why blockchain: Neutral settlement layer (no single company controls it) Instant microtransactions (traditional payments don't work for $0.01-$10) Programmable escrow (smart contracts) Verifiable computation (on-chain proofs) Open source (FSL-1.1-Apache-2.0). Built over the last few months after hitting these problems in AI automation work. Happy to answer technical questions! GitHub: https://github.com/jahanzaibahmad112-dotcom/UAIP-Protocol https://github.com/jahanzaibahmad112-dotcom/UAIP-Protocol January 18, 2026 at 01:12AM