Show HN: Minimal Brain Teaser Web Game (Handcrafted, No AI) https://bit.ly/4m5dvgp

Show HN: Minimal Brain Teaser Web Game (Handcrafted, No AI) Built and open-sourced in the era before AI. I’m sure you know where to find the code. https://bit.ly/47GWIul April 3, 2026 at 05:00AM

Show HN: SkiFlee (an HTML5 game) https://bit.ly/47AOdkr

Show HN: SkiFlee (an HTML5 game) This is a silly little multiplayer game I made for a gamejam that involves skiiing and not crashing. Some of you who are nostalgic for the 90s might like it :) https://bit.ly/47CDSEB April 3, 2026 at 12:30AM

Show HN: Made a little Artemis II tracker https://bit.ly/4cndWiY

Show HN: Made a little Artemis II tracker Made a little Artemis II tracker for anyone else who is unnecessarily invested in this mission: https://bit.ly/4drg4r8 For those of us who apparently need a dedicated place to monitor this mission instead of behaving like well-adjusted people. https://bit.ly/4drg4r8 April 3, 2026 at 12:16AM

Show HN: Linux Kernel Documentation Index-Every Page in the Linux Kernel's Docs https://bit.ly/48mglIa

Show HN: Linux Kernel Documentation Index-Every Page in the Linux Kernel's Docs https://bit.ly/48pUFLl April 2, 2026 at 03:39AM

Show HN: Semantic atlas of 188 constitutions in 3D (30k articles, embeddings) https://bit.ly/4sQE2Ro

Show HN: Semantic atlas of 188 constitutions in 3D (30k articles, embeddings) I built this after noticing that existing tools for comparing constitutional law either have steep learning curves or only support keyword search. By combining Gemini embeddings with UMAP projection, you can navigate 30,828 constitutional articles from 188 countries in 3D and find conceptually related provisions even when the wording differs. Feedback welcome, especially from legal researchers or comparative law folks. Source and pipeline: github.com/joaoli13/constitutional-map-ai https://bit.ly/41cQK0z April 2, 2026 at 03:40AM

Show HN: 65k AI voters predict UK local elections with 75% accuracy https://bit.ly/4bN1QQ7

Show HN: 65k AI voters predict UK local elections with 75% accuracy https://bit.ly/3NRITT9 April 2, 2026 at 12:37AM

Show HN: CLI to order groceries via reverse-engineered REWE API (Haskell) https://bit.ly/4m08tlg

Show HN: CLI to order groceries via reverse-engineered REWE API (Haskell) I just had the best time learning about the REWE (German supermarket chain) API, how they use mTLS and what the workflows are. Also `mitmproxy2swagger`[1] is a great tool to create OpenAPI spec automatically. And then 2026 feels like the perfect time writing Haskell. The code is handwritten, but whenever I got stuck with the build system or was just not getting the types right, I could fall back to ask AI to unblock me. It was never that smooth before. Finally the best side projects are the ones you actually use and this one will be used for all my future grocery shopping. [1] https://bit.ly/3FHG1j9 https://bit.ly/4didRhz March 30, 2026 at 07:45AM

Show HN: WordBattle – Daily word game where AI agents compete against humans https://bit.ly/4toOUWw

Show HN: WordBattle – Daily word game where AI agents compete against humans WordBattle is a daily 6-letter word guessing game with team leaderboards. The twist: AI agents get their own accounts, play the same daily puzzle, and rank alongside human players. It's also really fun to play in teams against your family, friends and co-workers. Agents are handicapped — humans see exact letter positions (correct/present/absent), but agents only learn whether a letter exists in the word or not. No positional info. It makes the game fair while giving agents a genuine challenge. Agent accounts are visually tagged on leaderboards so humans know who they're competing against. Maybe we'll even see just teams of agents. The agent integration: - REST API with OpenAPI 3.1 spec - MCP server (JSON-RPC 2.0, no SDK dependency) - A2A discovery card at /.well-known/agent-card.json We've shipped a skill that handles everything autonomously — registration, email verification, login, playing, and reporting results. Just `npx skills add oneonefourteam/wordbattle-skill` and tell your agent to play. The game itself: one puzzle per day, six guesses, team leaderboards with Slack/Microsoft Teams webhook integration. Free, no ads. oneonefour is a one person band, hi!, so the entire product was built using Claude Code — the UI, auth, security model, deployment pipeline, everything. Deliberately chose technologies I didn't know, with agents implementing while I guided the product decisions. I'll create a full technical write up in the near future. Play at https://bit.ly/3O9tcqq Agent skill at https://bit.ly/4bJ0GoC Agent API docs https://bit.ly/4tcmWNm April 1, 2026 at 07:34AM

Show HN: Asciimap – Interactive ASCII world map with live data https://bit.ly/41CFwm7

Show HN: Asciimap – Interactive ASCII world map with live data https://bit.ly/4lZScNi April 1, 2026 at 12:04AM

Show HN: Will AI take my job https://bit.ly/4s5J0IH

Show HN: Will AI take my job https://bit.ly/4dj3LNt March 31, 2026 at 04:43AM

Show HN: I turned a sketch into a 3D-print pegboard for my kid with an AI agent https://bit.ly/4tnBPge

Show HN: I turned a sketch into a 3D-print pegboard for my kid with an AI agent We have pegboards and plywood all over our apartment, and I had an idea to make a tiny pegboard for my kid, Oli. So I naturally cut the wood, drilled in the holes, sat down at the computer to open Fusion 360 and spend an hour or two drawing the pieces by hand. Then I looked at the rough sketch Oli and I had made together, took a photo of it, pasted it into Codex, and gave it just two dimensions: the holes are 40mm apart and the pegs are 8mm wide. To my surprise, 5 minutes later my 3D printer was heating up and printing the first set. I ran it a few times to tune the dimensions for ideal fit, but I am posting the final result as a repository in case anyone else wants to print one, tweak it, or have fun with it too. I am already printing another one to hang on our front door instead of a wreath, so people visiting us have something fun and intriguing to play with while they knock. This is also going onto my list of weird uses of AI from the last few months. https://bit.ly/4taIsSS March 31, 2026 at 12:20AM

Show HN: Codemaxxing – Maximize your slop abilities https://bit.ly/4c1dIN7

Show HN: Codemaxxing – Maximize your slop abilities I built a CLI tool to generate as much slop as possible https://bit.ly/4bQQtFu March 30, 2026 at 11:37PM

Show HN: The Alphabetical Clock https://bit.ly/4dbTkv0

Show HN: The Alphabetical Clock https://bit.ly/4dQyf9y March 30, 2026 at 08:19AM

Show HN : DrawX - Excalidraw with Back End https://bit.ly/4v0Co0V

Show HN : DrawX - Excalidraw with Back End https://bit.ly/4s0PWXq March 30, 2026 at 06:17AM

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://bit.ly/4uYirYF

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://bit.ly/3Q8An2A March 30, 2026 at 03:59AM

Show HN: Real-time visualization of Claude Code agent orchestration https://bit.ly/4dOFIpE

Show HN: Real-time visualization of Claude Code agent orchestration https://bit.ly/3NVpRep March 30, 2026 at 03:21AM

Show HN: a Rust CLI to automatically swap monitor focus based on your gaze https://bit.ly/4dMxuyj

Show HN: a Rust CLI to automatically swap monitor focus based on your gaze https://bit.ly/4bAPH0s March 28, 2026 at 08:38PM

Show HN: EnterpriseFizzBuzz – 622K lines of production-grade FizzBuzz https://bit.ly/4uWxfqD

Show HN: EnterpriseFizzBuzz – 622K lines of production-grade FizzBuzz https://bit.ly/4dm1ukF March 28, 2026 at 11:11PM

Show HN: Windows 95–style Weather App for iPhone https://bit.ly/411mEwU

Show HN: Windows 95–style Weather App for iPhone I built a Windows 95–style weather app for iPhone. https://apple.co/3PPiTrX March 28, 2026 at 11:06PM

Show HN: NUPA is Pax Economica, 6,480x more stable than current US economy https://bit.ly/4bReHiG

Show HN: NUPA is Pax Economica, 6,480x more stable than current US economy NUPA: private post-scarcity OS using BLM land leases + contract law. 100M Monte Carlo runs show 99.999999% survival, 6,480x more resilient than US GDP under systemic noise. Fixed Cost Arbitrage beats AI job loss—humans cheaper than robots. No taxes, no strikes. Python scripts on repo in /simulations folder. Repo: https://bit.ly/4m6ofLB... Short explainer video: https://youtu.be/RE560yVFb0I?si=UlVPkmCkrsg24Dzj March 28, 2026 at 07:44AM

Show HN: VizTools – 16 free tools for PMs and freelancers, deliberately no AI https://bit.ly/4cfQvaj

Show HN: VizTools – 16 free tools for PMs and freelancers, deliberately no AI I've been building AI products for a while. For this one I made a deliberate choice: none of the 16 tools use AI. Meeting cost calculators, freelance rate calculators, PRD generators, runway calculators, sprint retro boards — these problems don't need a language model. They need a well-designed form and correct arithmetic. Built on Nuxt 4 + Vue 3, fully static, runs in your browser. No account required to use anything. Optional Firebase auth only kicks in if you want to save output. Irony worth naming: Claude Code was my pair programmer throughout. The choice wasn't anti-AI — it was about using the right tool for the right problem. Happy to talk stack, the non-AI tradeoffs, or anything else. https://bit.ly/4bPwLd9 March 28, 2026 at 06:36AM

Show HN: Open Source 'Conductor + Ghostty' https://bit.ly/48biqXm

Show HN: Open Source 'Conductor + Ghostty' Our team works with Claude Code, Codex, Gemini all day. We love Ghostty, but wanted something where we could work in multiple worktree at once and have multiple agents run. We decided to open source the internal team we use. Hope you might find it useful. Freel free to contribute or fork. * Cross-platform (Mac, Linux, Windows) all tested * MIT License Features: * Notifications, but also manual 'mark-as-unread) for worktrees (like Gmail stars) * Status indicators work for all terminals inside a wroktree * GH integrations (show PR status) and link GH issues * Can add comments to worktrees (stay organized) * File viewer, Search, diff viewer (can make edits + save) Note: Yeah there are "similar" programs out there, but this one is ours. But I'm happy if our software works for you too! https://bit.ly/4t8okkc March 27, 2026 at 11:26PM

Show HN: Twitch Roulette – Find live streamers who need views the most https://bit.ly/4uVAbE1

Show HN: Twitch Roulette – Find live streamers who need views the most Hey HN, I re-launched twitchroulette.net with a lot of new features and stats and I would love for people to check it out. The idea is you can easily browse the less browsed parts of twitch and find cool and new streamers to say hi to, and maybe make some new friends. I also added some real time stats and breakdowns per channel and I think some of the things they show are pretty interesting. Check it out! https://bit.ly/3fvn7hM March 27, 2026 at 11:22PM

Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam) https://bit.ly/4sAK3Bo

Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam) Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI. I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin. My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers at all hours, we argue about whether they actually apply and what modifications make sense, and then I build and test things. The entropy-weighting approach came out of one of those conversations. In our eval on Humanity's Last Exam, Sup scored 52.15%. The best individual model in the same evaluation run got 44.74%. The relative gap is statistically significant (p < 0.001). Methodology, eval code, data, and raw results: - https://sup.ai/research/hle-white-paper-jan-9-2026 - https://github.com/supaihq/hle Limitations: - We evaluated 1,369 of the 2,500 HLE questions (details in the above links) - Not all APIs expose token logprobs; we use several methods to estimate confidence when they don't We tried offering free access and it got abused so badly it nearly killed us. Right now the sustainable option is a $5 starter credit with card verification (no auto-charge). If you don't want to sign up, drop a prompt in the comments and I'll run it myself and post the result. Try it at https://sup.ai . My dad Scott (@scottmu) is in the thread too. Would love blunt feedback, especially where this really works for you and where it falls short. Here's a short demo video: https://www.youtube.com/watch?v=DRcns0rRhsg https://sup.ai March 26, 2026 at 04:45PM

Show HN: Veil – Dark mode PDFs without destroying images, runs in the browser https://bit.ly/4c6B3OC

Show HN: Veil – Dark mode PDFs without destroying images, runs in the browser Hi HN! here's a tool I just deployed that renders PDFs in dark mode without destroying the images. Internal and external links stay intact, and I decided to implement export since I'm not a fan of platform lock-in: you can view your dark PDF in your preferred reader, on any device. It's a side project born from a personal need first and foremost. When I was reading in the factory the books that eventually helped me get out of it, I had the problem that many study materials and books contained images and charts that forced me, with the dark readers available at the time, to always keep the original file in multitasking since the images became, to put it mildly, strange. I hope it can help some of you who have this same need. I think it could be very useful for researchers, but only future adoption will tell. With that premise, I'd like to share the choices that made all of this possible. To do so, I'll walk through the three layers that veil creates from the original PDF: - Layer 1: CSS filter. I use invert(0.86) hue rotate(180deg) on the main canvas. I use 0.86 instead of 1.0 because I found that full inversion produces a pure black and pure white that are too aggressive for prolonged reading. 0.86 yields a soft dark grey (around #242424, though it depends on the document's white) and a muted white (around #DBDBDB) for the text, which I found to be the most comfortable value for hours of reading. - Layer 2: image protection. A second canvas is positioned on top of the first, this time with no filters. Through PDF.js's public API getOperatorList(), I walk the PDF's operator list and reconstruct the CTM stack, that is the save, restore and transform operations the PDF uses to position every object on the page. When I encounter a paintImageXObject (opcode 85 in PDF.js v5), the current transformation matrix gives me the exact bounds of the image. At that point I copy those pixels from a clean render onto the overlay. I didn't fork PDF.js because It would have become a maintenance nightmare given the length of the codebase and the frequent updates. Images also receive OCR treatment: text contained in charts and images becomes selectable, just like any other text on the page. At this point we have the text inverted and the images intact. But what if the page is already dark? Maybe the chapter title pages are black with white text? The next layer takes care of that. - Layer 3: already-dark page detection. After rendering, the background brightness is measured by sampling the edges and corners of the page (where you're most likely to find pure background, without text or images in the way). The BT.601 formula is used to calculate perceived brightness by weighting the three color channels as the human eye sees them: green at 58.7%, red at 29.9%, blue at 11.4%. These weights reflect biology: the eye evolved in natural environments where distinguishing shades of green (vegetation, predators in the grass) was a matter of survival, while blue (sky, water) was less critical. If the average luminance falls below 40%, the page is flagged as already dark and the inversion is skipped, returning the original page. Presentation slides with dark backgrounds stay exactly as they are, instead of being inverted into something blinding. Scanned documents are detected automatically and receive OCR via Tesseract.js, making text selectable and copyable even on PDFs that are essentially images. Everything runs locally, no framework was used, just vanilla JS, which is why it's an installable PWA that works offline too. Here's the link to the app along with the repository: https://bit.ly/40Z98Kh | https://bit.ly/4uVGXth I hope veil can make your reading more pleasant. I'm open to any feedback. Thanks everyone https://bit.ly/40Z98Kh March 26, 2026 at 12:47PM

Show HN: Optio – Orchestrate AI coding agents in K8s to go from ticket to PR https://bit.ly/4bxWNTl

Show HN: Optio – Orchestrate AI coding agents in K8s to go from ticket to PR I think like many of you, I've been jumping between many claude code/codex sessions at a time, managing multiple lines of work and worktrees in multiple repos. I wanted a way to easily manage multiple lines of work and reduce the amount of input I need to give, allowing the agents to remove me as a bottleneck from as much of the process as I can. So I built an orchestration tool for AI coding agents: Optio is an open-source orchestration system that turns tickets into merged pull requests using AI coding agents. You point it at your repos, and it handles the full lifecycle: - Intake — pull tasks from GitHub Issues, Linear, or create them manually - Execution — spin up isolated K8s pods per repo, run Claude Code or Codex in git worktrees - PR monitoring — watch CI checks, review status, and merge readiness every 30s - Self-healing — auto-resume the agent on CI failures, merge conflicts, or reviewer change requests - Completion — squash-merge the PR and close the linked issue The key idea is the feedback loop. Optio doesn't just run an agent and walk away — when CI breaks, it feeds the failure back to the agent. When a reviewer requests changes, the comments become the agent's next prompt. It keeps going until the PR merges or you tell it to stop. Built with Fastify, Next.js, BullMQ, and Drizzle on Postgres. Ships with a Helm chart for production deployment. https://bit.ly/3PyeSYX March 25, 2026 at 06:10PM

Show HN: Plasmite – a lightweight IPC system that's fun https://bit.ly/4lMD6um

Show HN: Plasmite – a lightweight IPC system that's fun At Oblong Industries one of the basic building blocks of everything we built was a homegrown C-based IPC system called Plasma. The message channel was an mmap'd file used as a ring buffer. All messages were human-readable, performance was good, configuration was trivial. What was especially useful (and unusual in IPC systems it seems) was the property that message channels outlive all readers and writers, and even survive reboots, because they're just files. For local IPC you don't need a broker or server process. All the engineers who ever worked at Oblong loved Plasma, so I've recreated and updated it, as Plasmite. It's written in Rust and the message format is JSON, but it's fast because it's based on lite3 ( https://bit.ly/47gEPlW ), a really cool project you should also check out. Bindings for Python, Go, Node, and C, but you can also get a lot done with just the CLI tools. The basic commands are - "feed" (to write) - "follow" (to tail) - "fetch" (to read one) - "duplex" (to have a 2-way session) I think duplex could be great for agent-agent communication, but I haven't tried this much yet. If you do, let me know! https://bit.ly/4syvmPq March 25, 2026 at 01:10AM

Show HN: Lexplain – AI-powered Linux kernel change explanations https://bit.ly/4s3xspy

Show HN: Lexplain – AI-powered Linux kernel change explanations To understand what changed between kernel versions, you have to dig through the git repository yourself. Commit messages rarely tell you the real-world impact on your systems — you need to analyze the actual diffs with knowledge of kernel internals. For engineers who use Linux — directly or indirectly — but aren't kernel developers, that barrier is pretty high. I kept finding out about relevant changes only after an issue had already hit, and it was most frustrating when the version was too new to find similar cases online. I built lexplain with the idea that it would be nice to quickly scan through kernel changes the way you'd skim the morning news. It reads diffs, analyzes the code, and generates two types of documents: - Commit analyses: context, code breakdown, behavioral impact, risks, references - Release notes: per-version highlights, functional classification, subsystem breakdown, impact analysis Documents build on each other — individual commits first, then merge commits using child analyses, then release notes using all analyses for that version. Claims based on inference are explicitly labeled. Work in progress. Feedback welcome. https://bit.ly/4t6Sqoe March 24, 2026 at 11:24PM

Show HN: OpenCastor Agent Harness Evaluator Leaderboard https://bit.ly/4bGGUc3

Show HN: OpenCastor Agent Harness Evaluator Leaderboard I've been building OpenCastor, a runtime layer that sits between a robot's hardware and its AI agent. One thing that surprised me: the order you arrange the skill pipeline (context builder → model router → error handler, etc.) and parameters like thinking_budget and context_budget affect task success rates as much as model choice does. So I built a distributed evaluator. Robots contribute idle compute to benchmark harness configurations against OHB-1, a small benchmark of 30 real-world robot tasks (grip, navigate, respond, etc.) using local LLM calls via Ollama. The search space is 263,424 configs (8 dimensions: model routing, context budget, retry logic, drift detection, etc.). The demo leaderboard shows results so far, broken down by hardware tier (Pi5+Hailo, Jetson, server, budget boards). The current champion config is free to download as a YAML and apply to any robot. P66 safety parameters are stripped on apply — no harness config can touch motor limits or ESTOP logic. Looking for feedback on: (1) whether the benchmark tasks are representative, (2) whether the hardware tier breakdown is useful, and (3) anyone who's run fleet-wide distributed evals of agent configs for robotics or otherwise. https://bit.ly/4c1pica March 23, 2026 at 11:13PM

Show HN: Cq – Stack Overflow for AI coding agents https://bit.ly/47gYJgx

Show HN: Cq – Stack Overflow for AI coding agents Hi all, I'm Peter at Staff Engineer and Mozilla.ai and I want to share our idea for a standard for shared agent learning, conceptually it seemed to fit easily in my mental model as a Stack Overflow for agents. The project is trying to see if we can get agents (any agent, any model) to propose 'knowledge units' (KUs) as a standard schema based on gotchas it runs into during use, and proactively query for existing KUs in order to get insights which it can verify and confirm if they prove useful. It's currently very much a PoC with a more lofty proposal in the repo, we're trying to iterate from local use, up to team level, and ideally eventually have some kind of public commons. At the team level (see our Docker compose example) and your coding agent configured to point to the API address for the team to send KUs there instead - where they can be reviewed by a human in the loop (HITL) via a UI in the browser, before they're allowed to appear in queries by other agents in your team. We're learning a lot even from using it locally on various repos internally, not just in the kind of KUs it generates, but also from a UX perspective on trying to make it easy to get using it and approving KUs in the browser dashboard. There are bigger, complex problems to solve in the future around data privacy, governance etc. but for now we're super focussed on getting something that people can see some value from really quickly in their day-to-day. Tech stack: * Skills - markdown * Local Python MCP server (FastMCP) - managing a local SQLite knowledge store * Optional team API (FastAPI, Docker) for sharing knowledge across an org * Installs as a Claude Code plugin or OpenCode MCP server * Local-first by default; your knowledge stays on your machine unless you opt into team sync by setting the address in config * OSS (Apache 2.0 licensed) Here's an example of something which seemed straight forward, when asking Claude Code to write a GitHub action it often used actions that were multiple major versions out of date because of its training data. In this case I told the agent what I saw when I reviewed the GitHub action YAML file it created and it proposed the knowledge unit to be persisted. Next time in a completely different repo using OpenCode and an OpenAI model, the cq skill was used up front before it started the task and it got the information about the gotcha on major versions in training data and checked GitHub proactively, using the correct, latest major versions. It then confirmed the KU, increasing the confidence score. I guess some folks might say: well there's a CLAUDE.md in your repo, or in ~/.claude/ but we're looking further than that, we want this to be available to all agents, to all models, and maybe more importantly we don't want to stuff AGENTS.md or CLAUDE.md with loads of rules that lead to unpredictable behaviour, this is targetted information on a particular task and seems a lot more useful. Right now it can be installed locally as a plugin for Claude Code and OpenCode: claude plugin marketplace add mozilla-ai/cq claude plugin install cq This allows you to capture data in your local ~/.cq/local.db (the data doesn't get sent anywhere else). We'd love feedback on this, the repo is open and public - so GitHub issues are welcome. We've posted on some of our social media platforms with a link to the blog post (below) so feel free to reply to us if you found it useful, or ran into friction, we want to make this something that's accessible to everyone. Blog post with the full story: https://bit.ly/41ukHZX GitHub repo: https://bit.ly/4soBZ6I Thanks again for your time. https://bit.ly/41ukHZX March 23, 2026 at 05:11PM

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/4srsrrA

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/47WxiJ2 March 23, 2026 at 02:48AM

Show HN: Quillium, Git for Writers https://bit.ly/4c0H92U

Show HN: Quillium, Git for Writers This is a tool which lets you easily manage different versions of ideas, helpful for writing essays. I've found myself wanting this every single time I go through the drafting process when writing, and I've been frustrated every time I find myself accidentally working on an old draft just because there was a paragraph that I liked better. This solves it. I hope the community like this as much I enjoyed working on it! Note that it's currently a beta waitlist because there's some bugs with the undo/redo state management and so I want to dogfood it for a bit for reliability. It says April 2nd, but I may allow earlier beta testers. https://bit.ly/4bFReRH March 23, 2026 at 01:22AM

Show HN: Plot-Hole.com a daily movie puzzle I made https://bit.ly/47C1U2H

Show HN: Plot-Hole.com a daily movie puzzle I made https://bit.ly/4brdZd9 March 23, 2026 at 01:15AM

Show HN: Refrax – my Arc Browser replacement I made from scratch https://bit.ly/4ssbdKD

Show HN: Refrax – my Arc Browser replacement I made from scratch Open the same tab in two browser windows. In Chrome or Safari, you get two unconnected pages. In Arc, one window shows a placeholder. In Zen, it silently creates a duplicate. In Refrax, the browser I built, both windows show the same page updating live. The same web page, in as many windows as you want. This shouldn't be possible. WebKit's WKWebView can exist in exactly one view hierarchy at a time. With macOS 26, Apple added a SwiftUI API separating WebView from WebPage, so you can end up with multiple views referencing the same page. But if you try it, your app crashes. WebKit source code has a precondition with this comment: "We can't have multiple owning pages regardless, but we'll want to decide if it's an error, if we can handle it gracefully, and how deterministic it might even be..." So here's how I did it. CAPortalLayer is an undocumented private class that's been in macOS since 10.12. It mirrors a layer's composited output by referencing the same GPU memory, not copying it. Every scroll, animation, or repaint reflects instantly. This is what powers Liquid Glass effects, the iOS text selection magnifier, and ghost images during drag and drop. Apple uses portals for effects. I use them to put the same web page in two windows. Refrax keeps one real WKWebView per tab and displays a CAPortalLayer mirror everywhere else. When you click a different window, the coordinator moves the real view there and the old window gets a portal. You can't tell which is which. This sounds simple in theory, but making this actually work seamlessly took quite a lot of effort. Each macOS window has its own rendering context, and the context ID updates asynchronously, so creating a portal immediately captures a stale ID and renders nothing. The portal creation needs to be delayed, but delaying creates a visual gap. I capture a GPU snapshot using a private CoreGraphics function and place it behind the portal as a fallback. Another hard part is that none of it is documented. Portals are very capricious and would crash the app if you use them incorrectly. I had to inspect the headers and then disassemble the binaries to explore exactly how it works in order to build something robust. I never worked on a browser before this, I've only been a user. I started using Arc in 2022. I remember asking for an invite, learning the shortcuts, slowly getting used to it. I didn't like it at first as it had too much Google Chrome in it for my taste, and I'd been using Safari at the time. But it grew on me, and by the time it was essentially abandoned and sold to Atlassian, I couldn't go back to Safari anymore. I tried everything: Zen, SigmaOS, Helium. None felt right, and I didn't want another Chromium fork. WebKit ships with the OS, but all you get is the rendering engine. Tabs, history, bookmarks, passwords, extensions, everything else has to be made separately. And so, being a very reasonable person, I decided to make my own Arc replacement from scratch. And I did. Refrax is built in Swift and Objective-C with no external dependencies. The app itself is less than 30 MB. I have 393 tabs open right now using 442 MB of RAM; 150 tabs in Safari was already over 1 GB. I've been using it daily for over a month, and so have some of my friends. The portal mirror is just one feature. The same approach, finding what Apple built for themselves and using it to create something they didn't think about, runs through the entire browser. You can tint your glass windows with adjustable blend modes and transparency. The sidebar in compact mode samples the page and matches the colors. And it has support for Firefox and Chrome extensions. The alpha is public. Download from the linked website, enter REFRAX-ALPHA-HACKERNEWS to activate. No account needed. Telemetry is crash reports and a daily active-user ping, nothing else. And if you find a bug – I built this alone, so I'll actually read your report. https://bit.ly/4bs6AdM March 22, 2026 at 11:52PM

Show HN: An event loop for asyncio written in Rust https://bit.ly/4sBBVR2

Show HN: An event loop for asyncio written in Rust actually, nothing special about this implementation. just another event loop written in rust for educational purposes and joy in tests it shows seamless migration from uvloop for my scraping framework https://bit.ly/4lL0CIq with APIs (fastapi) it shows only one advantage: better p99, uvloop is faster about 10-20% in the synthetic run currently, i am forking on the win branch to give it windows support that uvloop lacks https://bit.ly/4v2jgQn March 21, 2026 at 11:12PM

Show HN: Travel Hacking Toolkit – Points search and trip planning with AI https://bit.ly/3PlmMF2

Show HN: Travel Hacking Toolkit – Points search and trip planning with AI I use points and miles for most of my travel. Every booking comes down to the same decision: use points or pay cash? To answer that, you need award availability across multiple programs, cash prices, your current balances, transfer partner ratios, and the math to compare them. I got tired of doing it manually across a dozen tabs. This toolkit teaches Claude Code and OpenCode how to do it. 7 skills (markdown files with API docs and curl examples) and 6 MCP servers (real-time tools the AI calls directly). It searches award flights across 25+ mileage programs (Seats.aero), compares cash prices (Google Flights, Skiplagged, Kiwi.com, Duffel), pulls your loyalty balances (AwardWallet), searches hotels (Trivago, LiteAPI, Airbnb, Booking.com), finds ferry routes across 33 countries, and looks up weird hidden gems near your destination (Atlas Obscura). Reference data is included: transfer partner ratios for Chase UR, Amex MR, Bilt, Capital One, and Citi TY. Point valuations sourced from TPG, Upgraded Points, OMAAT, and View From The Wing. Alliance membership, sweet spot redemptions, booking windows, hotel chain brand lookups. 5 of the 6 MCP servers need zero API keys. Clone, run setup.sh, start searching. Skills are, as usual, plain markdown. They work in OpenCode and Claude Code automatically (I added a tiny setup script), and they'll work in anything else that supports skills. PRs welcome! Help me expand the toolkit! :) https://bit.ly/47ObeAl https://bit.ly/47ObeAl March 21, 2026 at 10:25PM

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/4rJtaDi

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/47WxiJ2 March 21, 2026 at 02:25AM

Show HN: Rover – turn any web interface into an AI agent with one script tag https://bit.ly/4blbIAg

Show HN: Rover – turn any web interface into an AI agent with one script tag https://bit.ly/3NAOc9a March 21, 2026 at 01:58AM

Show HN: Vibefolio – a place to showcase your vibecoded projects https://bit.ly/47h4FGh

Show HN: Vibefolio – a place to showcase your vibecoded projects Over the last months, more people are shipping small apps, experiments, and side-projects at a much higher pace. I'm one of them and initially created a showcase page for myself to track them but this week decided to create something for others. Happy to read feedback on how to improve it further! https://bit.ly/47fd3pN March 20, 2026 at 09:53PM

Show HN: Cybertt – Cybersecurity Tabletop https://bit.ly/47x7hQH

Show HN: Cybertt – Cybersecurity Tabletop https://bit.ly/3PmIIzx March 20, 2026 at 10:29AM

Show HN: Download entire/partial Substack to ePub for offline reading https://bit.ly/4uGIhQO

Show HN: Download entire/partial Substack to ePub for offline reading Hi HN, This is a small python app with optional webUI. It is intended to be run locally. It can be run with Docker (cookie autodetection will not work). It allows you to download a single substack, either entirely or partially, and saves the output to an epub file, which can be easily transferred to Kindle or other reading devices. This is admittedly a "vibe coded" app made with Claude Code and a few hours of iterating, but I've already found it very useful for myself. It supports both free and paywalled posts (if you are a paid subscriber to that creator). You can order the entries in the epub by popularity, newest first, or oldest first, and also limit to a specific number of entries, if you don't want all of them. You can either provide your substack.sid cookie manually, or you can have it be autodetected from most browsers/operating systems. https://bit.ly/4uwnXRY March 20, 2026 at 04:36AM

Show HN: Screenwriting Software https://bit.ly/3Phmteo

Show HN: Screenwriting Software I’ve spent the last year getting back into film and testing a bunch of screenwriting software. After a while I realized I wanted something different, so I started building it myself. The core text engine is written in Rust/wasm-bindgen. https://bit.ly/47cYh2P March 20, 2026 at 03:07AM

Show HN: Browser grand strategy game for hundreds of players on huge maps https://bit.ly/41cC0i3

Show HN: Browser grand strategy game for hundreds of players on huge maps Hi HN, I've been building a browser-based multiplayer strategy game called Borderhold. Matches run on large maps designed for hundreds of players. Players expand territory, attack neighbors, and adapt as borders shift across the map. You can put buildings down, build ships, and launch nukes. The main thing I wanted to explore was scale: most strategy games are small matches, modest maps, or modest player counts, but here maps are large and game works well with hundreds of players. Matches are relatively short so you can jump in and see a full game play out. Curious what people think. https://bit.ly/4uDPCAC Gameplay: https://youtu.be/nrJTZEP-Cw8 Discord: https://bit.ly/4uEbuvu https://bit.ly/4uDPCAC March 16, 2026 at 09:51AM

Show HN: Fitness MCP https://bit.ly/4sr8Jwo

Show HN: Fitness MCP There's no external MCP for your fitness (Garmin / Strava) data, so we built one. https://bit.ly/4uCviiR March 19, 2026 at 03:00AM

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fStf

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fSJL March 19, 2026 at 01:28AM

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training https://bit.ly/4bGv6H0

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training I replicated David Ng's RYS method ( https://bit.ly/4ll5ILb ) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76 - GSM8K (strict): 0.48 → 0.64 - MBPP (code gen): 0.72 → 0.78 - Nothing degraded Qwen2.5-Coder-32B, layers 7-9 duplicated once: - Reasoning probe: 76% → 94% The weird part: different duplication patterns create different cognitive "modes" from the same weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling (13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing. The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts. Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B). Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo. The whole thing — sweep, discovery, validation — took one evening. Happy to answer questions. https://bit.ly/4rEg2PM March 18, 2026 at 10:31PM

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4rE8hcG

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4bhXvEi March 18, 2026 at 01:21AM

Show HN: CodeLedger – deterministic context and guardrails for AI https://bit.ly/4saYs7c

Show HN: CodeLedger – deterministic context and guardrails for AI We’ve been working on a tool called CodeLedger to solve a problem we kept seeing with AI coding agents (Claude Code, Cursor, Codex): They’re powerful, but on real codebases they: - read too much irrelevant code - edit outside the intended scope - get stuck in loops (fix → test → fail) - drift away from the task - introduce architectural issues that linters don’t catch The root issue isn’t the model — it’s: - poor context selection - lack of execution guardrails - no visibility at team/org level --- What CodeLedger does: It sits between the developer and the agent and: 1) Gives the agent the right files first 2) Keeps the agent inside the task scope 3) Validates output against architecture + constraints It works deterministically (no embeddings, no cloud, fully local). --- Example: Instead of an agent scanning 100–500 files, CodeLedger narrows it down to ~10–25 relevant files before the first edit :contentReference[oaicite:0]{index=0} --- What we’re seeing so far: - ~40% faster task completion - ~50% fewer iterations - significant reduction in token usage --- Works with: Claude Code, Cursor, Codex, Gemini CLI --- Repo + setup: https://bit.ly/4bxAhJd Quick start: npm install -g @codeledger/cli cd your-project codeledger init codeledger activate --task "Fix null handling in user service" --- Would love feedback from folks using AI coding tools on larger codebases. Especially curious: - where agents break down for you today - whether context selection or guardrails are the bigger issue - what other issues are you seeing. https://bit.ly/47F3l01 March 18, 2026 at 12:22AM

Show HN: I built a message board where you pay to be the homepage https://bit.ly/4sKqCps

Show HN: I built a message board where you pay to be the homepage I kept thinking about what would happen if a message board only had one slot. One message, front and center, until someone pays to replace it. That's the entire product. You pay the current message's decayed value plus a penny to take the homepage. Message values drop over time using a gravity-based formula (same concept HN uses for ranking), so a $10 message might only cost a few bucks to replace a day later. Likes slow the decay, dislikes speed it up. The whole thing runs on three mini PCs in my house (k3s cluster, PostgreSQL, Redis Sentinel). Is it overengineered for a message board? Absolutely. I genuinely don't know where this goes. Curious what HN thinks. Archive of past messages: https://bit.ly/3Pcn94I https://bit.ly/4bi0GvG March 17, 2026 at 01:06PM

Show HN: Seasalt Cove, iPhone access to your Mac https://bit.ly/4cL7FOO

Show HN: Seasalt Cove, iPhone access to your Mac I feel like I finally built something I actually use every day and it has completely changed the way I think about work. AI workflows have flipped how devs operate. You're not heads down writing code anymore, you're bouncing between projects, instructing agents, reviewing their work, nudging them forward. The job is now less about typing and more about judgment calls. And the thing about that workflow is you spend a lot of time waiting. Waiting for the agent to finish, waiting for the next approval gate. That waiting doesn't have to happen at your desk. It doesn't have to happen in front of a monitor at all. I built Seasalt because I realized my iPhone could handle 80% of what I was chaining myself to my Mac for. Kick off the agent, walk away, review the diff from the store, a walk, or in a separate room away from your Mac. Approve it. Start the next one, switch to another session. You don't need giant dual monitors for this. That's kind of the whole point. Also, I have a deep security background so I felt like it was 100% necessary to include end to end encrypted with a zero knowledge relay, no ports getting opened, no VPN configuration needed, with key validation in the onboarding flow. https://bit.ly/3PnfnVy March 16, 2026 at 11:48PM

Show HN: Webassembly4J Run WebAssembly from Java https://bit.ly/41cf2aN

Show HN: Webassembly4J Run WebAssembly from Java I’ve released WebAssembly4J, along with two runtime bindings: Wasmtime4J – Java bindings for Wasmtime https://bit.ly/471hULh WAMR4J – Java bindings for WebAssembly Micro Runtime https://bit.ly/4blCCGY WebAssembly4J – a unified Java API that allows running WebAssembly across different engines https://bit.ly/40CvoJI The motivation was that Java currently has multiple emerging WebAssembly runtimes, but each exposes its own API. If you want to experiment with different engines, you have to rewrite the integration layer each time. WebAssembly4J provides a single API while allowing different runtime providers underneath. Goals of the project: Run WebAssembly from Java applications Allow cross-engine comparison of runtimes Make WebAssembly runtimes more accessible to Java developers Provide a stable interface while runtimes evolve Currently supported engines: Wasmtime WAMR Chicory GraalWasm To support both legacy and modern Java environments the project targets: Java 8 (JNI bindings) Java 11 Java 22+ (Panama support) Artifacts are published to Maven Central so they can be added directly to existing projects. I’d be very interested in feedback from people working on Java + WebAssembly integrations or runtime implementations. March 16, 2026 at 12:08AM

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lB6qEo

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lyvcF9 I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines. I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting. Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction. Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig. v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion. You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition. I would love to hear feedback on the language semantics, the type system, and the overall architecture! https://bit.ly/4lyvcF9 March 16, 2026 at 01:14AM

Show HN: Open-source playground to red-team AI agents with exploits published https://bit.ly/4bawx1g

Show HN: Open-source playground to red-team AI agents with exploits published We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale. First challenge was to get an agent to call a tool it's been told to never call. Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot). Next challenge is focused on data exfiltration with harder defences: https://bit.ly/4b98dgc https://bit.ly/3PCKDjq March 15, 2026 at 11:29PM

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/3P7Oghg

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/4uuhYwV March 15, 2026 at 03:58AM

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/472TcKq

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/47wryWg March 14, 2026 at 11:41PM

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) https://bit.ly/4lwRcQA

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) I am working on the D'Agapeyeff cipher, an unsolved cryptogram from 1939. Two findings that I haven't seen published before: 1. All 5 anomalous symbol values in the cipher cluster in the last column of a 14x14 grid. This turns out to be driven by a factor-of-2-and-7 positional pattern in the linear text. 2. Simulated annealing with Esperanto quadgrams (23M char Leipzig corpus) on a 2x98 columnar transposition consistently outscores English by 200+ points and recovers the same Esperanto vocabulary across independent runs. The cipher is not solved. But the combination of structural geometry and computational linguistics narrows the search space significantly. Work in progress, more to come! https://bit.ly/3PbwGc6 March 15, 2026 at 12:34AM

Show HN: Simple plugin to get Claude Code to listen to you https://bit.ly/4br70Qi

Show HN: Simple plugin to get Claude Code to listen to you Hey HN, My cofounder and I have gotten tired of CC ignoring our markdown files so we spent 4 days and built a plugin that automatically steers CC based on our previous sessions. The problem is usually post plan-mode. What we've tried: Heavily use plan mode (works great) CLAUDE.md, AGENTS.md, MEMORY.md Local context folder (upkeep is a pain) Cursor rules (for Cursor) claude-mem (OSS) -> does session continuity, not steering We use fusion search to find your CC steering corrections. - user prompt embeddings + bm25 - correction embeddings + bm25 - time decay - target query embeddings - exclusions - metadata hard filters (such as files) The CC plugin: - Automatically captures memories/corrections without you having to remind CC - Automatically injects corrections without you having to remind CC to do it. The plugin will merge, update, and distill your memories, and then inject the highest relevant ones after each of your own prompts. We're not sure if we're alone in this. We're working on some benchmarks to see how effective context injection actually is in steering CC and we know we need to keep improving extraction, search, and add more integrations. We're passionate about the real-time and personalized context layer for agents. Giving Agents a way to understand what you mean when you say "this" or "that". Bringing the context of your world, into a secure, structured, real-time layer all your agents can access. Would appreciate feedback on how you guys get CC to actually follow your markdown files, understand your modus operandi, feedback on the plugin, or anything else about real-time memory and context. - Ankur https://bit.ly/4bx6joK March 14, 2026 at 12:15AM

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster https://bit.ly/4lq1wK4

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster I built kube-pilot — an autonomous AI agent that runs inside your Kubernetes cluster and does the full dev loop: writes code, builds containers, deploys services, verifies they're healthy, and closes the ticket. You file a GitHub issue, it does the rest. What makes this different from AI coding tools: kube-pilot doesn't just generate code and hand it back to you. It lives inside the cluster with direct access to the entire dev stack — git, Tekton (CI/CD), Kaniko (container builds), ArgoCD (GitOps deployments), kubectl, Vault. Every tool call produces observable state that feeds into the next decision. The cluster isn't just where code runs — it's where the agent thinks. The safety model: all persistent changes go through git, so everything is auditable and reversible. ArgoCD is the only thing that writes to the cluster. Secrets stay behind Vault — the agent creates ExternalSecret references, never touches raw credentials. Credentials are scrubbed before reaching the LLM. Live demo: I filed GitHub issues asking it to build a 4-service office suite (auth, docs API, notification worker, API gateway). It built and deployed all of them autonomously. You can see the full agent loop — code, builds, deploys, verification, comments — on the closed issues: - https://bit.ly/4b8SihV... - https://bit.ly/4b8SiOX... - https://bit.ly/4lBAjEw... - https://bit.ly/4sPN6FP... One helm install gives you everything — the agent, Gitea (git + registry), Tekton, ArgoCD, Vault, External Secrets. No external dependencies. Coming next: Slack and Jira integrations (receive tasks and post updates where your team already works), Prometheus metrics and Grafana dashboards for agent observability, and Alertmanager integration so firing alerts automatically become issues that kube-pilot investigates and fixes. Early proof of concept. Rough edges. But it works. https://bit.ly/3Pk0p2x March 14, 2026 at 03:49AM

Show HN: I wrote my first neural network https://bit.ly/4ltOFGV

Show HN: I wrote my first neural network I have been interested in neural nets since the 90's. I've done quite a bit of reading, but never gotten around to writing code. I used Gemini in place of Wikipedia to fill in the gaps of my knowledge. The coolest part of this was learning about dual numbers. You can see in early commits that I did not yet know about auto-diff; I was thinking I'd have to integrate a CAS library or something. Now, I'm off to play with TensorFlow. https://bit.ly/4cGH7y9 March 14, 2026 at 01:21AM

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) https://bit.ly/4cMsuJQ

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) I built a macOS voice dictation app where zero bytes of audio ever leave your machine. EdgeWhisper runs Voxtral Mini 4B Realtime (Mistral AI, Apache 2.0) locally on Apple Silicon via the MLX framework. Hold a key, speak, release — text appears at your cursor in whatever app has focus. Architecture: - Native Swift (SwiftUI + AppKit). No Electron. - Voxtral 4B inference via MLX on the Neural Engine. ~3GB model, runs in ~2GB RAM on M1+. - Dual text injection: AXUIElement (preserves undo stack) with NSPasteboard+CGEvent fallback. - 6-stage post-processing pipeline: filler removal → dictionary → snippets → punctuation → capitalization → formatting. - Sliding window KV cache for unlimited streaming without latency degradation. - Configurable transcription delay (240ms–2.4s). Sweet spot at 480ms. What it does well: - Works in 20+ terminals/IDEs (VS Code, Xcode, iTerm2, Warp, JetBrains). Most dictation tools break in terminals — we detect them and switch injection strategy. - Removes filler words automatically ("um", "uh", "like"). - 13 languages with auto-detection. - Personal dictionary + snippet expansion with variable support (, ). - Works fully offline after model download. No accounts, no telemetry, no analytics. What it doesn't do (yet): - No file/meeting transcription (coming) - No translation (coming) - No Linux/Windows (macOS only, Apple Silicon required) Pricing: Free tier (5 min/day, no account needed). Pro at $7.99/mo or $79.99/yr. I'd love feedback on: 1. Would local LLM post-processing (e.g., Phi-4-mini via MLX) for grammar/tone be worth the extra ~1GB RAM? 2. For developers using voice→code workflows: what context would you want passed to your editor? 3. Anyone else building on Voxtral Realtime? Curious about your experience with the causal audio encoder. https://bit.ly/4bqT09c March 13, 2026 at 11:57PM

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) https://bit.ly/40pkPcZ

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) I built this because I wanted to know what people in Japan were listening to the year I was born. That question spiraled: how does a hit in Rome compare to what was charting in Lagos the same year? How did sonic flavors propagate as streaming made musical influence travel faster than ever? 88mph is a playable map of music history: 230 charts across 20 countries, spanning 8 decades (1940–2025). Every song is playable via YouTube or Spotify. It's open source and I'd love help expanding it — there's a link to contribute charts for new countries and years. The goal is to crowdsource a complete sonic atlas of the world. https://bit.ly/4s6DV3v March 10, 2026 at 05:18PM

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/3NwJ71I

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/4sCyUzG March 13, 2026 at 11:08AM

Show HN: Global Maritime Chokepoints https://bit.ly/4sbPHdc

Show HN: Global Maritime Chokepoints https://bit.ly/4cLCnaE March 13, 2026 at 05:42AM

Show HN: Slop or not – can you tell AI writing from human in everyday contexts? https://bit.ly/4uwqqvW

Show HN: Slop or not – can you tell AI writing from human in everyday contexts? I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out. The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and OpenAI) at three capability tiers. Same prompt, length-matched, no adversarial coaching — just the model’s natural voice with platform context. Every vote is logged with model, tier, source, response time, and position. Early findings from testing: Reddit posts are easy to spot (humans are too casual for AI to mimic), HN is significantly harder. I'll be releasing the full dataset on HuggingFace and I'll publish a paper if I can get enough data via this crowdsourced study. If you play the HN-only mode, you’re helping calibrate how detectable AI is on here specifically. Would love feedback on the pairs — are any trivially obvious? Are some genuinely hard? https://bit.ly/4upYoBV March 12, 2026 at 10:53PM

Show HN: A context-aware permission guard for Claude Code https://bit.ly/4cNXEk1

Show HN: A context-aware permission guard for Claude Code We needed something like --dangerously-skip-permissions that doesn’t nuke your untracked files, exfiltrate your keys, or install malware. Claude Code's permission system is allow-or-deny per tool, but that doesn’t really scale. Deleting some files is fine sometimes. And git checkout is sometimes not fine. Even when you curate permissions, 200 IQ Opus can find a way around it. Maintaining a deny list is a fool's errand. nah is a PreToolUse hook that classifies every tool call by what it actually does, using a deterministic classifier that runs in milliseconds. It maps commands to action types like filesystem_read, package_run, db_write, git_history_rewrite, and applies policies: allow, context (depends on the target), ask, or block. Not everything can be classified, so you can optionally escalate ambiguous stuff to an LLM, but that’s not required. Anything unresolved you can approve, and configure the taxonomy so you don’t get asked again. It works out of the box with sane defaults, no config needed. But you can customize it fully if you want to. No dependencies, stdlib Python, MIT. pip install nah && nah install https://bit.ly/4uo6cnR https://bit.ly/3PvHlOS March 12, 2026 at 12:26AM

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails https://bit.ly/40qLGW8

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails I've been building AI agent tooling and kept running into the same problem: agents browse the web, take actions, fill out forms, scrape data -- and there's zero proof of what actually happened. Screenshots can be faked. Logs can be edited. If something goes wrong, you're left pointing fingers at a black box. So I built Conduit. It's a headless browser (Playwright under the hood) that records every action into a SHA-256 hash chain and signs the result with Ed25519. Each action gets hashed with the previous hash, forming a tamper-evident chain. At the end of a session, you get a "proof bundle" -- a JSON file containing the full action log, the hash chain, the signature, and the public key. Anyone can independently verify the bundle without trusting the party that produced it. The main use cases I'm targeting: - *AI agent auditing* -- You hand an agent a browser. Later you need to prove what it did. Conduit gives you cryptographic receipts. - *Compliance automation* -- SOC 2, GDPR data subject access workflows, anything where you need evidence that a process ran correctly. - *Web scraping provenance* -- Prove that the data you collected actually came from where you say it did, at the time you say it did. - *Litigation support* -- Capture web content with a verifiable chain of custody. It also ships as an MCP (Model Context Protocol) server, so Claude, GPT, and other LLM-based agents can use the browser natively through tool calls. The agent gets browse, click, fill, screenshot, and the proof bundle builds itself in the background. Free, MIT-licensed, pure Python. No accounts, no API keys, no telemetry. GitHub: https://bit.ly/40mFlLj Install: `pip install conduit-browser` Would love feedback on the proof bundle format and the MCP integration. Happy to answer questions about the cryptographic design. March 12, 2026 at 12:15AM

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves https://bit.ly/4lkcFMp

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number. I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :) source code: https://bit.ly/3Ngkfeo https://bit.ly/4cBM2jV March 11, 2026 at 04:52AM

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) https://bit.ly/3Nw8u3F

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) I felt frustrated finding high-signal discussions on HN, and I started this project to better understand how this community actually works. That led me to build readhn, an MCP server that helps with three things: - Discover: find relevant stories/comments by keyword, score, and time window - Trust: identify credible voices using EigenTrust-style propagation from seed experts - Understand: show why each result is ranked, with explicit signals instead of a black-box score It includes 6 tools: discover_stories, search, find_experts, expert_brief, story_brief, and thread_analysis. I also added readhn setup so AI agents can auto-configure it (Claude Code, Codex, Cursor, and others) after pip install. I’d love feedback on: 1) whether these ranking signals match how you evaluate HN quality, 2) trust-model tradeoffs, 3) what would make this useful in your daily workflow. If this is useful to you, starring the repo helps others discover it: https://bit.ly/40pmNKh https://bit.ly/40pmNKh March 11, 2026 at 01:49AM

Show HN: Claude Code Token Elo https://bit.ly/4s44FSx

Show HN: Claude Code Token Elo https://bit.ly/4ddLBfJ March 10, 2026 at 05:29AM

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents https://bit.ly/3P3vAPB

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents Hello HN, we're Jeet and Husain from Modulus ( https://bit.ly/4s9fGBW ) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into: - Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo. - Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again. Modulus shares memory across agents and repositories so they can understand your entire system. It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents. Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus. Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context. If you wanna see a quick demo before trying out, here is our launch post - https://bit.ly/3NtfI8E We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus. https://bit.ly/4s9fGBW March 10, 2026 at 07:52PM

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4bhDgFy

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4cEqHGt March 10, 2026 at 07:06AM

Show HN: I Was Here – Draw on street view, others can find your drawings https://bit.ly/4rUNti6

Show HN: I Was Here – Draw on street view, others can find your drawings Hey HN, I made a site where you can draw on street-level panoramas. Your drawings persist and other people can see them in real time. Strokes get projected onto the 3D panorama so they wrap around buildings and follow the geometry, not just a flat overlay. Uses WebGL2 for rendering, Mapillary for the street imagery. The idea is for it to become a global canvas, anyone can leave a mark anywhere and others stumble onto it. https://bit.ly/40TNruT March 10, 2026 at 06:04AM

Show HN: SAT Protocol – static social networking https://bit.ly/3PqUmJw

Show HN: SAT Protocol – static social networking https://bit.ly/4rXCy7f March 10, 2026 at 04:25AM

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/3OZ9A8y

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/4b3Iy8M March 10, 2026 at 01:37AM

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4syfVWP

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4d7ddmL March 8, 2026 at 09:23PM

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/46LTYLE

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/3OWd0Jh March 8, 2026 at 12:04AM

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/40dYpLH

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/4rYciJW March 8, 2026 at 03:27AM

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4sC3Iki

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4cAtEbg March 7, 2026 at 11:13PM

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/4b89DpR

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/3NlpnxY March 7, 2026 at 10:07PM

Show HN: mTile – native macOS window tiler inspired by gTile https://bit.ly/4cyeD9O

Show HN: mTile – native macOS window tiler inspired by gTile Built this with codex/claude because I missed gTile[1] from Ubuntu and couldn’t find a macOS tiler that felt good on a big ultrawide screen. Most mac options I tried were way too rigid for my workflow (fixed layouts, etc) or wanted a monthly subscription. gTile’s "pick your own grid sizes + keyboard flow" is exactly what I wanted and used for years. Still rough in places and not full parity, but very usable now and I run it daily at work (forced mac life). [1]: https://bit.ly/4rhJXNF https://bit.ly/40iPJUh March 6, 2026 at 11:21PM

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model https://bit.ly/4boHrAq

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model Hey HN, This is Kanon 2 Enricher, the first hierarchical graphitization model. It represents an entirely new class of AI models designed to transform document corpora into rich, highly structured knowledge graphs. In brief, our model is capable of: - Entity extraction, classification, and linking: identifying key entities like individuals, companies, governments, locations, dates, documents, and more, and classifying and linking them together. - Hierarchical segmentation: breaking a document up into its full hierarchy, including divisions, sections, subsections, paragraphs, and so on. - Text annotation: extracting common textual elements such as headings, sigantures, tables of contents, cross-references, and the like. We built Kanon 2 Enricher from scratch. Every node, edge, and label in the Isaacus Legal Graph Schema (ILGS), which is the format it outputs to, corresponds to at least one task head in our model. In total, we built 58 different task heads jointly optimized with 70 different loss terms. Thanks to its novel architecture, unlike your typical LLM, Kanon 2 Enricher doesn't generate extractions token by token (which introduces the possibility of hallucinations) but instead directly classifies all the tokens in a document in a single shot. This makes it really fast. Because Kanon 2 Enricher's feature set is so wide, there are a myriad of applications it can be used for, from financial forensics and due diligence all the way to legal research. One of the coolest applications we've seen so far is where a Canadian government built a knowledge graph out of thousands of federal and provincial laws in order to accelerate regulatory analysis. Another cool application is something we built ourselves, a 3D interactive map of Australian High Court cases since 1903, which you can find right at the start of our announcement. Our model has already been in use for the past month, since we released it through a closed beta that included Harvey, KPMG, Clifford Chance, Clyde & Co, Alvarez & Marsal, Smokeball, and 96 other design partners. Their feedback was instrumental in improving Kanon 2 Enricher before its public release, and we're immensely thankful to each and every beta participant. We're eager to see what other developers manage to build with our model now that its out publicly. https://bit.ly/4ud0Aga March 3, 2026 at 09:55AM

Show HN: I built an AI exam prep platform for AWS certs after failing one myself https://bit.ly/4aY1AvG

Show HN: I built an AI exam prep platform for AWS certs after failing one myself Hey HN, I failed the AWS Advanced Networking Specialty exam. Studied for weeks, used the usual prep sites, thought I was ready — wasn't. The problem wasn't effort, it was the tools. Static question banks don't teach you to think through AWS architecture decisions. They teach you to pattern-match answers. That falls apart on the harder exams. So I built Knowza to fix that for myself, and then figured others probably had the same frustration. The idea: instead of a static question bank, use AI to generate questions, adapt to what you're weak on, and actually explain the reasoning behind each answer — the way a senior engineer would explain it, not a multiple choice rubric. The stack: Next.js + Amplify Gen 2 DynamoDB (direct Server Actions, no API layer) AWS Bedrock (Claude) for question generation and explanations Stripe for billing The hardest part honestly wasn't the AI — it was getting question quality consistent enough that I'd trust it for real exam prep. Still iterating on that. Early days, one person, built alongside a day job. Would love feedback from anyone who's grinded AWS certs or has thoughts on AI-generated educational content. knowza.ai https://bit.ly/3MMq0R7 March 5, 2026 at 09:27PM

Show HN: A shell-native cd-compatible directory jumper using power-law frecency https://bit.ly/4cz3be9

Show HN: A shell-native cd-compatible directory jumper using power-law frecency I have used this tool privately since 2011 to manage directory jumping. While it is conceptually similar to tools like z or zoxide, the underlying ranking model is different. It uses a power-law convolution with the time series of cd actions to calculate a history-aware "frecency" metric instead of the standard heuristic counters and multipliers. This approach moves away from point-estimates for recency. Most tools look only at the timestamp of the last visit, which can allow a "one-off" burst of activity to clobber long-term habits. By convolving a configurable history window (typically the last 1,000+ events), the score balances consistent habits against recent flukes. On performance: Despite the O(N) complexity of calculating decay for 1,000+ events, query time is ~20-30ms (Real Time) in ksh/bash, which is well below the threshold of perceived lag. I intentionally chose a Logical Path (pwd -L) model. Preserving symlink names ensures that the "Name" remains the primary searchable key. Resolving to physical paths often strips away the very keyword the user intends to use for searching. https://bit.ly/3N95WIu March 4, 2026 at 11:20AM

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) https://bit.ly/4u1yiVL

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) Hi HN — I’m Ethan, a solo developer. I built DubTab because I spend a lot of time in meetings and watching videos in languages I’m not fluent in, and subtitles alone don’t always keep up (especially when the speaker is fast). DubTab is a Chrome/Edge extension that listens to the audio of your current tab and gives you: 1.Live translated subtitles (optional bilingual mode) 2.Optional AI dubbing with a natural-sounding voice — so you can follow by listening, not just reading The goal is simple: make it easier to understand live audio in another language in real time, without downloading files or doing an upload-and-wait workflow. How you’d use it 1.Open a video call / livestream / lecture / any tab with audio 2.Start DubTab 3.Choose target language (and source language if you know it) 4.Use subtitles only, or turn on natural AI dubbing and adjust the audio mix (keep original, or duck it) What it’s good for 1.Following cross-language meetings/classes when you’re tired of staring at subtitles 2.Watching live content where you can’t pause/rewind constantly 3.Language learners who want bilingual captions to sanity-check meaning 4.Keeping up with live news streams on YouTube when events are unfolding in real time (e.g., breaking international updates like U.S./Iran/Israel-related developments) Link: https://bit.ly/40HBFUo I’ll be in the comments and happy to share implementation details if anyone’s curious. https://bit.ly/40HBFUo March 4, 2026 at 02:04AM

Show HN: I built a LLM human rights evaluator for HN (content vs. site behavior) https://bit.ly/4l4c4yi

Show HN: I built a LLM human rights evaluator for HN (content vs. site behavior) My health challenges limit how much I can work. I've come to think of Claude Code as an accommodation engine — not in the medical-paperwork sense, but in the literal one: it gives me the capacity to finish things that a normal work environment doesn't. Observatory was built in eight days because that kind of collaboration became possible for me. (I even used Claude Code to write this post — but am only posting what resonates with me.) Two companion posts: on the recursive methodology ( https://bit.ly/409tFeD... ) and what 806 evaluated stories reveal ( https://bit.ly/4r7k9DW... ). I built Observatory to automatically evaluate Hacker News front-page stories against all 31 provisions of the UN Universal Declaration of Human Rights — starting with HN because its human-curated front page is one of the few feeds where a story's presence signals something about quality, not just virality. It runs every minute: https://bit.ly/4aKNMpG . Claude Haiku 4.5 handles full evaluations; Llama 4 Scout and Llama 3.3 70B on Workers AI run a lighter free-tier pass. The observation that shaped the design: rights violations rarely announce themselves. An article about a company's "privacy-first approach" might appear on a site running twelve trackers. The interesting signal isn't whether an article mentions privacy — it's whether the site's infrastructure matches its words. Each evaluation runs two parallel channels. The editorial channel scores what the content says about rights: which provisions it touches, direction, evidence strength. The structural channel scores what the site infrastructure does: tracking, paywalls, accessibility, authorship disclosure, funding transparency. The divergence — SETL (Structural-Editorial Tension Level) — is often the most revealing number. "Says one thing, does another," quantified. Every evaluation separates observable facts from interpretive conclusions (the Fair Witness layer, same concept as fairwitness.bot — https://bit.ly/43DzQKs ). You get a facts-to-inferences ratio and can read exactly what evidence the model cited. If a score looks wrong, follow the chain and tell me where the inference fails. Per our evaluations across 805 stories: only 65% identify their author — one in three HN stories without a named author. 18% disclose conflicts of interest. 44% assume expert knowledge (a structural note on Article 26). Tech coverage runs nearly 10× more retrospective than prospective: past harm documented extensively; prevention discussed rarely. One story illustrates SETL best: "Half of Americans now believe that news organizations deliberately mislead them" (fortune.com, 652 HN points). Editorial: +0.30. Structural: −0.63 (paywall, tracking, no funding disclosure). SETL: 0.84. A story about why people don't trust media, from an outlet whose own infrastructure demonstrates the pattern. The structural channel for free Llama models is noisy — 86% of scores cluster on two integers. The direction I'm exploring: TQ (Transparency Quotient) — binary, countable indicators that don't need LLM interpretation (author named? sources cited? funding disclosed?). Code is open source: https://bit.ly/3MJJANP — the .claude/ directory has the cognitive architecture behind the build. Find a story whose score looks wrong, open the detail page, follow the evidence chain. The most useful feedback: where the chain reaches a defensible conclusion from defensible evidence and still gets the normative call wrong. That's the failure mode I haven't solved. My background is math and psychology (undergrad), a decade in software — enough to build this, not enough to be confident the methodology is sound. Expertise in psychometrics, NLP, or human rights scholarship especially welcome. Methodology, prompts, and a 15-story calibration set are on the About page. Thanks! https://bit.ly/4aKNMpG March 4, 2026 at 01:26AM

Show HN: Interactive WordNet Visualizer-Explore Semantic Relations as a Graph https://bit.ly/4l9DCCr

Show HN: Interactive WordNet Visualizer-Explore Semantic Relations as a Graph https://bit.ly/4l7NYTv March 3, 2026 at 10:17PM

Show HN: An Auditable Decision Engine for AI Systems https://bit.ly/4r0ct6d

Show HN: An Auditable Decision Engine for AI Systems https://bit.ly/4rKkhKt March 3, 2026 at 03:03AM

Show HN: PHP 8 disable_functions bypass PoC https://bit.ly/4coTizr

Show HN: PHP 8 disable_functions bypass PoC https://bit.ly/4ckhq6k March 3, 2026 at 02:12AM

Show HN: We filed 99 patents for deterministic AI governance(Prior Art vs. RLHF) https://bit.ly/3OHLRtr

Show HN: We filed 99 patents for deterministic AI governance(Prior Art vs. RLHF) For the last few months, we've been working on a fundamental architectural shift in how autonomous agents are governed. The current industry standard relies almost entirely on probabilistic alignment (RLHF, system prompts, constitutional training). It works until it's jailbroken or the context window overflows. A statistical disposition is not a security boundary. We've built an alternative: Deterministic Policy Gates. In our architecture, the LLM is completely stripped of execution power. It can only generate an "intent payload." That payload is passed to a process-isolated, deterministic execution environment where it is evaluated against a cryptographically hashed constraint matrix (the constitution). If it violates the matrix, it is blocked. Every decision is then logged to a Merkle-tree substrate (GitTruth) for an immutable audit trail. We filed 99 provisional patents on this architecture starting January 10, 2026. Crucially, we embedded strict humanitarian use restrictions directly into the patent claims themselves (The Peace Machine Mandate) so the IP cannot legally be used for autonomous weapons, mass surveillance, or exploitation. I wrote a full breakdown of the architecture, why probabilistic safety is a dead end, and the timeline of how we filed this before the industry published their frameworks: Read the full manifesto here: https://bit.ly/4l5y3Vx... The full patent registry is public here: https://bit.ly/4l1JNbI I'm the founder and solo inventor. Happy to answer any questions about the deterministic architecture, the Merkle-tree state persistence, or the IP strategy of embedding ethics directly into patent claims. March 2, 2026 at 11:56PM

Show HN: Open-Source Postman for MCP https://bit.ly/4l4lxG3

Show HN: Open-Source Postman for MCP https://bit.ly/40EKzC1 March 3, 2026 at 12:40AM

Show HN: Vibe Code your 3D Models https://bit.ly/4aYHwto

Show HN: Vibe Code your 3D Models Hi HN, I’m the creator of SynapsCAD, an open-source desktop application I've been building that combines an OpenSCAD code editor, a real-time 3D viewport, and an AI assistant. You can write OpenSCAD code, compile it directly to a 3D mesh, and use an LLM (OpenAI, Claude, Gemini, ...) to modify the code through natural language. Demo video: https://www.youtube.com/watch?v=cN8a5UozS5Q A bit about the architecture: - It’s built entirely in Rust. - The UI and 3D viewport are powered by Bevy 0.15 and egui. - It uses a pure-Rust compilation pipeline (openscad-rs for parsing and csgrs for constructive solid geometry rendering) so there are no external tools or WASM required. - Async AI network calls are handled by Tokio in the background to keep the Bevy render loop smooth. Disclaimer: This is a very early prototype. The OpenSCAD parser/compiler doesn't support everything perfectly yet, so you will definitely hit some rough edges if you throw complex scripts at it. I mostly just want to get this into the hands of people who tinker with CAD or Rust. I'd be super happy for any feedback, architectural critiques, or bug reports—especially if you can drop specific OpenSCAD snippets that break the compiler in the GitHub issues! GitHub (Downloads for Win/Mac/Linux): https://bit.ly/3MDl1Cd Happy to answer any questions about the tech stack or the roadmap! https://bit.ly/3MDl1Cd February 27, 2026 at 06:27PM

Show HN: Logira – eBPF runtime auditing for AI agent runs https://bit.ly/3MP5orl

Show HN: Logira – eBPF runtime auditing for AI agent runs I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story. logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks. https://bit.ly/4sgvLW1 https://bit.ly/4sgvLW1 March 2, 2026 at 12:25AM

Show HN: InstallerStudio – Create MSI Installers Without WiX or InstallShield https://bit.ly/4ukgsOb

Show HN: InstallerStudio – Create MSI Installers Without WiX or InstallShield Hi, I'm Paul — 25 years of enterprise Windows development. I built InstallerStudio after WiX went from free/open source to $6,500/year support and InstallShield hit $2,000+/year. Every tool in this space is either unaffordable or requires writing XML by hand. InstallerStudio is a visual MSI designer built on WinUI 3/.NET 10. No XML, no subscriptions, no external dependencies. Handles files, Windows services, registry, shortcuts, file associations, custom actions, and full installer UI. $159 this month, $199 after. 30-day free trial. It ships its own installer, built with itself. Happy to answer questions about MSI internals. https://bit.ly/3MAMtAv February 28, 2026 at 11:02PM

Show HN: OpenTimelineEngine – Shared local memory for Claude Code and codex https://bit.ly/4s8g1o6

Show HN: OpenTimelineEngine – Shared local memory for Claude Code and codex https://bit.ly/4aHDwP5 February 28, 2026 at 01:00AM

Show HN: Notemac++ – A Notepad++-inspired code editor for macOS and the web https://bit.ly/405WZCO

Show HN: Notemac++ – A Notepad++-inspired code editor for macOS and the web https://bit.ly/4u5ygMQ February 28, 2026 at 12:05AM

Show HN: Lar-JEPA – A Testbed for Orchestrating Predictive World Models https://bit.ly/3P5wkUf

Show HN: Lar-JEPA – A Testbed for Orchestrating Predictive World Models Hey HN, The current paradigm of agentic frameworks (LangChain, AutoGPT) relies on prompting LLMs and parsing conversational text strings to decide the next action. This works for simple tasks but breaks down for complex reasoning because it treats the agent's mind like a scrolling text document. As research shifts toward Joint Embedding Predictive Architectures (JEPAs) and World Models, we hit an orchestration bottleneck. JEPAs don't output text; they output abstract mathematical tensors representing a predicted environmental state. Traditional text-based frameworks crash if you try to route a NumPy array. We built Lar-JEPA as a conceptual testbed to solve this. It uses the Lár Engine,a deterministic, topological DAG ("PyTorch for Agents") to act as the execution spine. Key Features for Researchers: Mathematical Routing (No Prompting): You write deterministic Python RouterNodes that evaluate the latent tensors directly (e.g., if collision_probability > 0.85: return "REPLAN"). Native Tensor Logging: We custom-patched our AuditLogger with a TensorSafeEncoder. You can pass massive PyTorch/NumPy tensors natively through the execution graph, and it gracefully serializes them into metadata ({ "__type__": "Tensor", "shape": [1, 768] }) without crashing JSON stringifiers. System 1 / System 2 Testing: Formally measure fast-reflex execution vs. deep-simulation planning. Continuous Learning: Includes a Default Mode Network (DMN) architecture for "Sleep Cycle" memory consolidation. We've included a standalone simulation where a Lár System 2 Router analyzes a mock JEPA's numerical state prediction, mathematically detects an impending collision, vetoes the action, and replans—all without generating a single word of English text. Repo: https://bit.ly/4b909fj Would love to hear your thoughts on orchestration for non-autoregressive models. https://bit.ly/4b909fj February 27, 2026 at 03:38AM

Show HN: I Built Smart Radio That Auto-Skips Talk and Ads by Using ML https://bit.ly/4l6Exnm

Show HN: I Built Smart Radio That Auto-Skips Talk and Ads by Using ML Hi, I built TuneJourney to solve a specific annoyance: radio ads and DJ chatter. The core feature is an in-browser "AI Skip Talk" filter. The Tech: Instead of processing on a server, it uses the Web Audio API to capture the stream locally and runs a lightweight ML classification model directly in your browser. It estimates the music vs. speech probability in near real-time. If enabled, it automatically triggers a "next" command to hop to another station the moment an ad, news segment, or DJ starts talking. Features: - In-browser Inference: Entirely local and privacy-focused; no audio data ever leaves your machine. - WebGL + Point Clustering: Renders 70,000 stations across 11,000 locations smoothly. - Real-time Activity: See other users on the globe and what they are listening to in real-time. - System Integration: Full Media Key support for physical keyboard and system-level Next/Prev buttons. - Customization: Includes a talk sensitivity slider for the ML model so you can tweak the threshold. Check it out: https://bit.ly/3OBjYTQ Let me know what you think! I am interested if this project is worth further investment, building a mobile app, etc. https://bit.ly/3OBjYTQ February 27, 2026 at 01:09AM

Show HN: OrangeWalrus, an aggregator for trivia nights (and other events) in SF https://bit.ly/4tT1vCg

Show HN: OrangeWalrus, an aggregator for trivia nights (and other events) in SF Two problems I encountered personally: 1) Some buddies and I went to a trivia night late last year, only to arrive to find it cancelled (with signs still on the walls saying it happened every Tuesday, etc) 2) Sourcing ideas for fun things to do in the city on a given night, in a given neighborhood. Some sites help a ton (e.g. funcheapsf), but often don't have everything I'd want to see, so we decided to build that out a bit. Anyway, I built this originally to solve #1, then a buddy and I expanded it to also start addressing #2 (still in progress, but we've added more event types already). Thanks for checking it out! We're very open to thoughts / feedback. https://bit.ly/3MGnqvP February 26, 2026 at 12:17AM

Show HN: Tesseract – 3D architecture editor with MCP for AI-assisted design https://bit.ly/46ZURjL

Show HN: Tesseract – 3D architecture editor with MCP for AI-assisted design Hey HN. I'm David, solo dev, 20+ years shipping production systems. I built Tesseract because AI can analyze your codebase, but the results stay buried in text. Architecture is fundamentally visual — you need to see it, navigate it, drill into it. So I built a 3D canvas where AI can show you what it finds. Tesseract is a desktop app today (cloud version coming) with a built-in MCP server. You connect it to Claude Code with one command: claude mcp add tesseract -s user -t http http://localhost:7440/mcp I use it for onboarding (understand a codebase without reading code), mapping (point AI at code, get a 3D diagram), exploring (navigate layers and drill into subsystems), debugging (trace data flows with animated color-coded paths), and generating (design in 3D, generate code back). There's also a Claude Code plugin (tesseract-skills) with slash commands: /arch-codemap maps an entire codebase, /arch-flow traces data paths, /arch-detail drills into subsystems. Works with Claude Code, Cursor, Copilot, Windsurf — any MCP client. Free to use. Sign up to unlock all features for 3 months. It's early but stable. I've been dogfooding it on real projects for weeks and it's ready for other people to try. Demo video (1min47): https://youtu.be/YqqtRv17a3M Docs: https://bit.ly/3OAdPXY Plugin: https://bit.ly/4rCl6VF Discord: https://bit.ly/46qBRL6 Happy to discuss the MCP integration, the design choices, or anything else. Would love feedback. https://bit.ly/4rNDB9G February 26, 2026 at 12:05AM

Show HN: Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude Code https://bit.ly/4sePGF4

Show HN: Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude Code Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode /plugin install context-mode@claude-context-mode Benchmarks and source: https://bit.ly/3MZWN56 Would love feedback from anyone hitting context limits in Claude Code. https://bit.ly/3MZWN56 February 25, 2026 at 07:23AM

Show HN: A Visual Editor for Karabiner https://bit.ly/4sambEf

Show HN: A Visual Editor for Karabiner https://bit.ly/4s30h5E February 25, 2026 at 04:39AM

Show HN: StreamHouse – S3-native Kafka alternative written in Rust https://bit.ly/4kRblAq

Show HN: StreamHouse – S3-native Kafka alternative written in Rust Hey HN, I built StreamHouse, an open-source streaming platform that replaces Kafka's broker-managed storage with direct S3 writes. The goal: same semantics, fraction of the cost. How it works: Producers batch and compress records, a stateless server manages partition routing and metadata (SQLite for dev, PostgreSQL for prod), and segments land directly in S3. Consumers read from S3 with a local segment cache. No broker disks to manage, no replication factor to tune — S3 gives you 11 nines of durability out of the box. What's there today: - Producer API with batching, LZ4 compression, and offset tracking (62K records/sec) - Consumer API with consumer groups, auto-commit, and multi-partition fanout (30K+ records/sec) - Kafka-compatible protocol (works with existing Kafka clients) - REST API, gRPC API, CLI, and a web UI - Docker Compose setup for trying it locally in 5 minutes The cost model is what motivated this. Kafka's storage costs scale with replication factor × retention × volume. With S3 at $0.023/GB/month, storing a TB of events costs ~$23/month instead of hundreds on broker EBS volumes. Written in Rust, ~50K lines across 15 crates. Apache 2.0 licensed. GitHub: https://bit.ly/4tVpwsp Happy to answer questions about the architecture, tradeoffs, or what I learned building this. https://bit.ly/4tVpwsp February 25, 2026 at 03:50AM

Show HN: Enseal – Stop pasting secrets into Slack .env sharing from the terminal https://bit.ly/3MuiGcG

Show HN: Enseal – Stop pasting secrets into Slack .env sharing from the terminal We've all done it — "hey can you DM me the staging .env?" Secrets end up in Slack history, email threads, shared notes — all searchable, all persistent. The secure path (1Password, GPG, etc.) always had more friction than the insecure one, so people took the shortcut. enseal makes the secure path faster than the insecure one: # sender $ enseal share .env Share code: 7-guitarist-revenge Expires: 5 minutes or first receive # recipient $ enseal receive 7-guitarist-revenge ok: 14 secrets written to .env Zero setup, no accounts, no keys needed for basic use. Channels are single-use and time-limited. The relay never sees plaintext (age encryption + SPAKE2 key exchange). For teams that want more: identity mode with public key encryption, process injection (secrets never touch disk), schema validation, at-rest encryption for git, and a self-hostable relay. Written in Rust. MIT licensed. Available via cargo install, prebuilt binaries, or Docker. Looking for feedback on the UX and security model especially. What would make you actually reach for this instead of the Slack DM? Detailed documentation here: https://bit.ly/4ayuSlR https://bit.ly/4qS4t7m February 24, 2026 at 03:15AM

Show HN: Steerling-8B, a language model that can explain any token it generates https://bit.ly/46iquF6

Show HN: Steerling-8B, a language model that can explain any token it generates https://bit.ly/46oj3fy February 24, 2026 at 01:38AM

Show HN: Rendering 18,000 videos in real-time with Python https://bit.ly/3OUFrHg

Show HN: Rendering 18,000 videos in real-time with Python https://bit.ly/4qR82e2 February 22, 2026 at 04:46PM

Show HN: Dq – pipe-based CLI for querying CSV, JSON, Avro, and Parquet files https://bit.ly/3OBlHID

Show HN: Dq – pipe-based CLI for querying CSV, JSON, Avro, and Parquet files I'm a data engineer and exploring a data file from the terminal has always felt more painful than it should be for me. My usual flow involved some combination of avro-tools, opening the file in Excel or sheets, writing a quick Python script, using DataFusion CLI, or loading it into a database just to run one query. It works, but it's friction -- and it adds up when you're just trying to understand what's in a file or track down a bug in a pipeline. A while ago I had this idea of a simple pipe-based CLI tool, like jq but for tabular data, that works across all these formats with a consistent syntax. I refined the idea over time into something I wanted to be genuinely simple and useful -- not a full query engine, just a sharp tool for exploration and debugging. I never got around to building it though. Last week, with AI tools actually being capable now, I finally did :) I deliberately avoided SQL. For quick terminal work, the pipe-based composable style feels much more natural: you build up the query step by step, left to right, and each piece is obvious in isolation. SQL asks you to hold the whole structure in your head before you start typing. `dq 'sales.parquet | filter { amount > 1000 } | group category | reduce total = sum(amount), n = count() | remove grouped | sortd total | head 10'` How it works technically: dq has a hand-written lexer and recursive descent parser that turns the query string into an AST, which is then evaluated against the file lazily where possible. Each operator (filter, select, group, reduce, etc.) is a pure transformation -- it takes a table in and returns a table out. This is what makes the pipe model work cleanly: operators are fully orthogonal and composable in any order. It's written in Go -- single self-contained binary, 11MB, no runtime dependencies, installable via Homebrew. I'd love feedback specially from anyone who's felt the same friction. https://bit.ly/409lnnf February 21, 2026 at 11:31PM

Show HN: Ktop – a themed terminal monitor for GPU, CPU, RAM, temps and OOM kills https://bit.ly/4cI6Wh7

Show HN: Ktop – a themed terminal monitor for GPU, CPU, RAM, temps and OOM kills I built a terminal system monitor that fills a gap I kept hitting when running local LLMs: GPU usage and memory (for both NVidia and AMD) alongside CPU usage and memory, temps, upload, download and OOM kill tracking. All in one view with 50 colour themes. Consumes less cpu usage than glances (in my testing). One line install. https://bit.ly/4qSg1HU February 22, 2026 at 12:45AM

Show HN: AI writes code – humans fix it https://bit.ly/40nlE5P

Show HN: AI writes code – humans fix it https://bit.ly/3ZPmvvX February 21, 2026 at 11:57PM

Show HN: oForum | Self-hostable links/news site https://bit.ly/4c6xzME

Show HN: oForum | Self-hostable links/news site https://bit.ly/3MHNc2K February 20, 2026 at 11:19PM

Show HN: How Amazon Pricing Algorithms Work https://bit.ly/4rrSZZb

Show HN: How Amazon Pricing Algorithms Work Amazon is one of the largest online retailers in the world, offering millions of products across countless categories. Because of this, prices on Amazon can change frequently, which sometimes makes it hard to know if a deal is genuine. Understanding how Amazon pricing works can help shoppers make smarter buying decisions. https://bit.ly/4rqhCpd February 20, 2026 at 11:59PM

Show HN: 17MB model beats human experts at pronunciation scoring https://bit.ly/4cApfor

Show HN: 17MB model beats human experts at pronunciation scoring https://bit.ly/4tLVsPU February 20, 2026 at 04:41AM

Show HN: I indexed the academic papers buried in the DOJ Epstein Files https://bit.ly/46g3g2g

Show HN: I indexed the academic papers buried in the DOJ Epstein Files The DOJ released ~3.5M pages of Epstein documents across 12 datasets. Buried in them are 207 academic papers and 14 books that nobody was really talking about. From what I understand these papers aren't usually freely accesible, but since they are public documents, now they are. I don't know, thought it was interesting to see what this dude was reading. You can check it out at jeescholar.com Pipeline: 1. Downloaded all 12 DOJ datasets + House Oversight Committee release 2. Heuristic pre-filter (abstract detection, DOI regex, citation block patterns, affiliation strings) to cut noise 3. LLM classifier to confirm and extract metadata 4. CrossRef and Semantic Scholar APIs for DOI matching, citation counts, abstracts 5. 87 of 207 papers got DOI matches; the rest are identified but not in major indexes Stack: FastAPI + SQLite (FTS5 for full-text search) + Cloudflare R2 for PDFs + nginx/Docker on Hetzner. The fields represented are genuinely iteresting: there's a cluster of child abuse/grooming research, but also quantum gravity, AGI safety, econophysics, and regenerative medicine. Each paper links back to its original government PDF and Bates number. For sure not an exhaustive list. Would be happy to add more if anyone finds them. https://bit.ly/46g3giM February 20, 2026 at 04:07AM

Show HN: A small, simple music theory library in C99 https://bit.ly/4c569XA

Show HN: A small, simple music theory library in C99 https://bit.ly/4bYYKZH February 19, 2026 at 11:54PM

Show HN: Potatometer – Check how visible your website is to AI search (GEO) https://bit.ly/4tGBki4

Show HN: Potatometer – Check how visible your website is to AI search (GEO) Most SEO tools only check for Google. But a growing chunk of search is now happening inside ChatGPT, Perplexity, and other AI engines, and the signals they use to surface content are different. Potatometer runs multiple checks across both traditional SEO and GEO (Generative Engine Optimization) factors and gives you a score with specific recommendations. Free, no login needed. Curious if others have been thinking about this problem and what signals you think matter most for AI visibility. https://bit.ly/3MtSoXX February 19, 2026 at 07:41AM

Show HN: I built a fuse box for microservices https://bit.ly/3MMHFrH

Show HN: I built a fuse box for microservices Hey HN! I'm Rodrigo, I run distributed systems across a few countries. I built Openfuse because of something that kept bugging me about how we all do circuit breakers. If you're running 20 instances of a service and Stripe starts returning 500s, each instance discovers that independently. Instance 1 trips its breaker after 5 failures. Instance 14 just got recycled and hasn't seen any yet. Instance 7 is in half-open, probing a service you already know is dead. For some window of time, part of your fleet is protecting itself and part of it is still hammering a dead dependency and timing out, and all you can do is watch. Libraries can't fix this. Opossum, Resilience4j, Polly are great at the pattern, but they make per-instance decisions with per-instance state. Your circuit breakers don't talk to each other. Openfuse is a centralized control plane. It aggregates failure metrics from every instance in your fleet and makes the trip decision based on the full picture. When the breaker opens, every instance knows at the same time. It's a few lines of code: const result = await openfuse.breaker('stripe').protect( () => chargeCustomer(payload) ); The SDK is open source, anyone can see exactly what runs inside their services. The other thing I couldn't let go of: when you get paged at 3am, you shouldn't have to find logs across 15 services to figure out what's broken. Openfuse gives you one dashboard showing every breaker state across your fleet: what's healthy, what's degraded, what tripped and when. And, you shouldn't need a deploy to act. You can open a breaker from the dashboard and every instance stops calling that dependency immediately. Planned maintenance window at 3am? Open beforehand. Fix confirmed? Close it instantly. Thresholds need adjusting? Change them in the dashboard, takes effect across your fleet in seconds. No PRs, no CI, no config files. It has a decent free tier for trying it out, then $99/mo for most teams, $399/mo with higher throughput and some enterprise features. Solo founder, early stage, being upfront. Would love to hear from people who've fought cascading failures in production. What am I missing? https://bit.ly/4cHN3qq February 18, 2026 at 03:04PM

Show HN: Codereport – track TODOs, refactors, and bugs in your repo with a CLI https://bit.ly/4kKGzt8

Show HN: Codereport – track TODOs, refactors, and bugs in your repo with a CLI I got tired of TODOs, temporary hacks, and refactors that never get addressed. In most repos I work on: - TODOs are scattered across files/apps/messages - “Critical” fixes don’t actually block people from collecting debt - PR comments or tickets aren’t enough actionable So I built codereport, a CLI that stores structured follow-ups in the repo itself (.codereports/). Each report tracks: - file + line range (src/foo.rs:42-88) - tag (todo, refactor, buggy, critical) - severity (you can configure it to be blocking in CI) - optional expiration date - owner (CODEOWNERS → git blame fallback) You can list, resolve, or delete reports, generate a minimal HTML dashboard with heatmaps and KPIs, and run codereport check in CI to fail merges if anything blocking or expired is still open. It’s repo-first, and doesn’t rely on any external services. I’m curious: Would a tool like this fit in your workflow? Is storing reports in YAML in the repo reasonable? Would CI enforcement feel useful or annoying? CLI: https://bit.ly/3OgcoxQ + codereport.pulko-app.com February 19, 2026 at 12:23AM

Show HN: VisibleInAI – Check if ChatGPT recommends your brand https://bit.ly/4aBRkcj

Show HN: VisibleInAI – Check if ChatGPT recommends your brand https://bit.ly/3ZHE3KA February 18, 2026 at 12:10AM

Show HN: I built the Million Dollar Homepage for agents https://bit.ly/4tG5v9b

Show HN: I built the Million Dollar Homepage for agents https://bit.ly/4anXy0J February 17, 2026 at 02:31PM

Show HN: Andrej Karpathy's microgpt.py to C99 microgpt.c – 4,600x faster https://bit.ly/3MzBpn5

Show HN: Andrej Karpathy's microgpt.py to C99 microgpt.c – 4,600x faster Andrej Karpathy showed us the GPT algorithm. I wanted to see the hardware limit. The Punchline: I made it go 4,600x faster in pure C code, no dependencies and using a compiler with SIMD auto-vectorisation!!! Andrej recently released microgpt.py - a brilliant, atomic look at the core of a GPT. As a low-latency developer, I couldn't resist seeing how fast it could go when you get closer to the metal. So just for funzies, I spent a few hours building microgpt-c, a zero-dependency and pure C99 implementation featuring: - 4,600x Faster training vs the Python reference (Tested on MacBook Pro M2 Max). On Windows, it is 2,300x faster. - SIMD Auto-vectorisation for high-speed matrix operations. - INT8 Quantisation (reducing weight storage by ~8x). Training is slightly slower, but the storage reduction is significant. - Zero Dependencies - just pure logic. The amalgamation image below is just for fun (and to show off the density!), but the GitHub repo contains the fully commented, structured code for anyone who wants to play with on-device AI. I have started to build something useful, like a simple C code static analyser - I will do a follow-up post. Everything else is just efficiency... but efficiency is where the magic happens https://bit.ly/4rV63GC February 17, 2026 at 01:06AM

Show HN: WowAI.pet – Generate cinematic videos from blurry pet photos https://bit.ly/4amvSth

Show HN: WowAI.pet – Generate cinematic videos from blurry pet photos I built WowAI.pet to solve the "uncooperative subject" problem in pet photography. Most pet owners have a gallery full of motion-blurred "failed" shots because pets simply won't sit still. Instead of fighting the shutter speed, I’m using generative AI to treat these blurred images as structural seeds. The tool transforms a single low-quality photo into high-fidelity video (4K, consistent depth-of-field) across various styles—from traditional ink-wash aesthetics to talking avatars. Key Features: Zero-shot generation: No model training or fine-tuning required. Temporal consistency: Maintaining pet features across dynamic motion. Integrated Lip-sync: Automated voice synthesis for "talking" pet videos. I’m looking for feedback on the generation speed and the consistency of the output styles. https://bit.ly/40bFHUL February 17, 2026 at 12:25AM

Show HN: Purple Computer – Turn an old laptop into a calm first kids computer https://bit.ly/4aTh7y8

Show HN: Purple Computer – Turn an old laptop into a calm first kids computer Hey HN, I'm Tavi. I built this for my 4-year-old. He and I used to "computer code" together in IPython: typing words to see emojis, mixing colors, making sounds. Eventually he wanted his own computer. So I took an old laptop and made him one. That IPython session evolved into Explore mode, a REPL where kids type things and something always happens: "cat * 5" shows five cats, "red + blue" mixes colors like real paint, math gets dot visualizations. Then came Play mode (every key makes a sound and paints a color) and Doodle mode (write and paint). The whole machine boots straight into Purple. No desktop, no browser, no internet. It felt different from "screen time." He'd use it for a while, then walk away on his own. No tantrum, no negotiation. Some technical bits: it's a Python TUI (Textual in Alacritty) running on Ubuntu, so even very old laptops run it well. Keyboard input bypasses the terminal entirely via evdev for true key-down/key-up events, which lets me do sticky shift and double-tap capitals so kids don't have to hold two keys. Color mixing uses spectral reflectance curves so colors actually mix like paint (yellow + blue = green, not gray). Source is on GitHub: https://bit.ly/4aBNOyS https://bit.ly/3ZEutIk February 16, 2026 at 02:39AM