The best Hacker News stories from Show from the past week

Latest posts:

Show HN: Han – A Korean programming language written in Rust

A few weeks ago I saw a post about someone converting an entire C++ codebase to Rust using AI in under two weeks.<p>That inspired me — if AI can rewrite a whole language stack that fast, I wanted to try building a programming language from scratch with AI assistance.<p>I've also been noticing growing global interest in Korean language and culture, and I wondered: what would a programming language look like if every keyword was in Hangul (the Korean writing system)?<p>Han is the result. It's a statically-typed language written in Rust with a full compiler pipeline (lexer → parser → AST → interpreter + LLVM IR codegen).<p>It supports arrays, structs with impl blocks, closures, pattern matching, try/catch, file I/O, module imports, a REPL, and a basic LSP server.<p>This is a side project, not a "you should use this instead of Python" pitch. Feedback on language design, compiler architecture, or the Korean keyword choices is very welcome.<p><a href="https://github.com/xodn348/han" rel="nofollow">https://github.com/xodn348/han</a>

Show HN: Channel Surfer – Watch YouTube like it’s cable TV

I know, it's a very first-world problem. But in my house, we have a hard time deciding what to watch. Too many options!<p>So I made this to recreate Cable TV for YouTube. I made it so it runs in the browser. Quickly import your subscriptions in the browser via a bookmarklet. No accounts, no sign-ins. Just quickly import your data locally.

Show HN: Axe – A 12MB binary that replaces your AI framework

I built Axe because I got tired of every AI tool trying to be a chatbot.<p>Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too.<p>Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI.<p>What Axe is:<p>- 12MB binary, two dependencies. no framework, no Python, no Docker (unless you want it)<p>- Stdin piping, something like `git diff | axe run reviewer` just works<p>- Sub-agent delegation. Where agents call other agents via tool use, depth-limited<p>- Persistent memory. If you want, agents can remember across runs without you managing state<p>- MCP support. Axe can connect any MCP server to your agents<p>- Built-in tools. Such as web_search and url_fetch out of the box<p>- Multi-provider. Bring what you love to use.. Anthropic, OpenAI, Ollama, or anything in models.dev format<p>- Path-sandboxed file ops. Keeps agents locked to a working directory<p>Written in Go. No daemon, no GUI.<p>What would you automate first?

Show HN: s@: decentralized social networking over static sites

Show HN: I built a tool that watches webpages and exposes changes as RSS

I built Site Spy after missing a visa appointment slot because a government page changed and I didn’t notice for two weeks.<p>It watches webpages for changes and shows the result like a diff. The part I think HN might find interesting is that it can monitor a specific element on a page, not just the whole page, and it can expose changes as RSS feeds.<p>So instead of tracking an entire noisy page, you can watch just a price, a stock status, a headline, or a specific content block. When it changes, you can inspect the diff, browse the snapshot history, or follow the updates in an RSS reader.<p>It’s a Chrome/Firefox extension plus a web dashboard.<p>Main features:<p>- Element picker for tracking a specific part of a page<p>- Diff view plus full snapshot timeline<p>- RSS feeds per watch, per tag, or across all watches<p>- MCP server for Claude, Cursor, and other AI agents<p>- Browser push, Email, and Telegram notifications<p>Chrome: <a href="https://chromewebstore.google.com/detail/site-spy/jeapcpanagdgipcfnncmogeojgfofige" rel="nofollow">https://chromewebstore.google.com/detail/site-spy/jeapcpanag...</a><p>Firefox: <a href="https://addons.mozilla.org/en-GB/firefox/addon/site-spy/" rel="nofollow">https://addons.mozilla.org/en-GB/firefox/addon/site-spy/</a><p>Docs: <a href="https://docs.sitespy.app" rel="nofollow">https://docs.sitespy.app</a><p>I’d especially love feedback on two things:<p>- Is RSS actually a useful interface for this, or do most people just want direct alerts?<p>- Does element-level tracking feel meaningfully better than full-page monitoring?

Show HN: Remotely use my guitar tuner

Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon

Hi HN, we're Sanchit and Shubham (YC W26). We built a fast inference engine for Apple Silicon. LLMs, speech-to-text, text-to-speech – MetalRT beats llama.cpp, Apple's MLX, Ollama, and sherpa-onnx on every modality we tested. Custom Metal shaders, no framework overhead.<p>Also, we've open-sourced RCLI, the fastest end-to-end voice AI pipeline on Apple Silicon. Mic to spoken response, entirely on-device. No cloud, no API keys.<p>To get started:<p><pre><code> brew tap RunanywhereAI/rcli https://github.com/RunanywhereAI/RCLI.git brew install rcli rcli setup # downloads ~1 GB of models rcli # interactive mode with push-to-talk </code></pre> Or:<p><pre><code> curl -fsSL https://raw.githubusercontent.com/RunanywhereAI/RCLI/main/install.sh | bash </code></pre> The numbers (M4 Max, 64 GB, reproducible via `rcli bench`):<p>LLM decode – 1.67x faster than llama.cpp, 1.19x faster than Apple MLX (same model files): - Qwen3-0.6B: 658 tok/s (vs mlx-lm 552, llama.cpp 295) - Qwen3-4B: 186 tok/s (vs mlx-lm 170, llama.cpp 87) - LFM2.5-1.2B: 570 tok/s (vs mlx-lm 509, llama.cpp 372) - Time-to-first-token: 6.6 ms<p>STT – 70 seconds of audio transcribed in 101 ms. That's 714x real-time. 4.6x faster than mlx-whisper.<p>TTS – 178 ms synthesis. 2.8x faster than mlx-audio and sherpa-onnx.<p>We built this because demoing on-device AI is easy but shipping it is brutal. Voice is the hardest test: you're chaining STT, LLM, and TTS sequentially, and if any stage is slow, the user feels it. Most teams fall back to cloud APIs not because local models are bad, but because local inference infrastructure is.<p>The thing that's hard to solve is latency compounding. In a voice pipeline, you're stacking three models in sequence. If each adds 200ms, you're at 600ms before the user hears a word, and that feels broken. You can't optimize one stage and call it done. Every stage needs to be fast, on one device, with no network round-trip to hide behind.<p>We went straight to Metal. Custom GPU compute shaders, all memory pre-allocated at init (zero allocations during inference), and one unified engine for all three modalities instead of stitching separate runtimes together.<p>MetalRT is the first engine to handle all three modalities natively on Apple Silicon. Full methodology:<p>LLM benchmarks: <a href="https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-engine-apple-silicon">https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...</a><p>Speech benchmarks: <a href="https://www.runanywhere.ai/blog/metalrt-speech-fastest-stt-tts-apple-silicon">https://www.runanywhere.ai/blog/metalrt-speech-fastest-stt-t...</a><p>How: Most inference engines add layers between you and the GPU: graph schedulers, runtime dispatchers, memory managers. MetalRT skips all of it. Custom Metal compute shaders for quantized matmul, attention, and activation - compiled ahead of time, dispatched directly.<p>Voice Pipeline optimizations details: <a href="https://www.runanywhere.ai/blog/fastvoice-on-device-voice-ai-pipeline-apple-silicon">https://www.runanywhere.ai/blog/fastvoice-on-device-voice-ai...</a> RAG optimizations: <a href="https://www.runanywhere.ai/blog/fastvoice-rag-on-device-retrieval-augmented-voice-ai">https://www.runanywhere.ai/blog/fastvoice-rag-on-device-retr...</a><p>RCLI is the open-source voice pipeline (MIT) built on MetalRT: three concurrent threads with lock-free ring buffers, double-buffered TTS, 38 macOS actions by voice, local RAG (~4 ms over 5K+ chunks), 20 hot-swappable models, and a full-screen TUI with per-op latency readouts. Falls back to llama.cpp when MetalRT isn't installed.<p>Source: <a href="https://github.com/RunanywhereAI/RCLI" rel="nofollow">https://github.com/RunanywhereAI/RCLI</a> (MIT)<p>Demo: <a href="https://www.youtube.com/watch?v=eTYwkgNoaKg" rel="nofollow">https://www.youtube.com/watch?v=eTYwkgNoaKg</a><p>What would you build if on-device AI were genuinely as fast as cloud?

Show HN: How I topped the HuggingFace open LLM leaderboard on two gaming GPUs

I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants.<p>The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole.<p>The whole thing was developed on 2x RTX 4090s in my basement. I'm now running current models (GLM-4.7, Qwen3.5, MiniMax M2.5) on a dual GH200 rig (see my other post). Code and new models coming soon.<p>Happy to answer questions.

Show HN: DenchClaw – Local CRM on Top of OpenClaw

Hi everyone, I am Kumar, co-founder of Dench (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>). We were part of YC S24, an agentic workflow company that previously worked with sales floors automating niche enterprise tasks such as outbound calling, legal intake, etc.<p>Building consumer / power-user software always gave me more joy than FDEing into an enterprise. It did not give me joy to manually add AI tools to a cloud harness for every small new thing, at least not as much as completely local software that is open source and has all the powers of OpenClaw (I can now talk to my CRM on Telegram!).<p>A week ago, we launched Ironclaw, an Open Source OpenClaw CRM Framework (<a href="https://x.com/garrytan/status/2023518514120937672?s=20" rel="nofollow">https://x.com/garrytan/status/2023518514120937672?s=20</a>) but people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>).<p>OpenClaw today feels like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode was the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt.<p>That is how we think about DenchClaw. We are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world.<p>Demo: <a href="https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43" rel="nofollow">https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43</a><p><pre><code> npx denchclaw </code></pre> I use DenchClaw daily for almost everything I do. It also works as a coding agent like Cursor - DenchClaw built DenchClaw. I am addicted now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter.<p>On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill.<p>The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. Thank you DuckDB team!<p>It creates a new OpenClaw profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA.<p>Think of it as Cursor for your Mac (also works on Linux and Windows) which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do linkedin outreach using MY browser.<p>DenchClaw finds your Chrome Profile and copies it fully into its own, so you won’t have to log in into all your websites again. DenchClaw sees what you see, does what you do. It’s an everything app, that sits locally on your mac.<p>Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use.<p>We would love you all to break it, stress test its CRM capabilities, how it streams subagents for lead enrichment, hook it into your Apollo, Gmail, Notion and everything there is. Looking forward to comments/feedback!

Show HN: µJS, a 5KB alternative to Htmx and Turbo with zero dependencies

I built µJS because I wanted AJAX navigation without the verbosity of HTMX or the overhead of Turbo.<p>It intercepts links and form submissions, fetches pages via AJAX, and swaps fragments of the DOM. Single <script> tag, one call to `mu.init()`. No build step, no dependencies.<p>Key features: patch mode (update multiple fragments in one request), SSE support, DOM morphing via idiomorph, View Transitions, prefetch on hover, polling, and full HTTP verb support on any element.<p>At ~5KB gzipped, it's smaller than HTMX (16KB) and Turbo (25KB), and works with any backend: PHP, Python, Go, Ruby, whatever.<p>Playground: <a href="https://mujs.org/playground" rel="nofollow">https://mujs.org/playground</a><p>Comparison with HTMX and Turbo: <a href="https://mujs.org/comparison" rel="nofollow">https://mujs.org/comparison</a><p>About the project creation, why and when: <a href="https://mujs.org/about" rel="nofollow">https://mujs.org/about</a><p>GitHub: <a href="https://github.com/Digicreon/muJS" rel="nofollow">https://github.com/Digicreon/muJS</a><p>Happy to discuss the project.

Show HN: I built a real-time OSINT dashboard pulling 15 live global feeds

Sup HN,<p>So I got tired of bouncing between Flightradar, MarineTraffic, and Twitter every time something kicked off globally, so I wrote a dashboard to aggregate it all locally. It’s called Shadowbroker.<p>I’ll admit I leaned way too hard into the "movie hacker" aesthetic for the UI, but the actual pipeline underneath is real. It pulls commercial/military ADS-B, the AIS WebSocket stream (about 25,000+ ships), N2YO satellite telemetry, and GDELT conflict data into a single MapLibre instance.<p>Getting this to run without melting my browser was the hardest part. I'm running this on a laptop with an i5 and an RTX 3050, and initially, dumping 30k+ moving GeoJSON features onto the map just crashed everything. I ended up having to write pretty aggressive viewport culling, debounce the state updates, and compress the FastAPI payloads by like 90% just to make it usable.<p>My favorite part is the signal layer—it actually calculates live GPS jamming zones by aggregating the real-time navigation degradation (NAC-P) of commercial flights overhead.<p>It’s Next.js and Python. I threw a quick-start script in the releases if you just want to spin it up, but the repo is open if you want to dig into the backend.<p>Let me know if my MapLibre implementation is terrible, I'm always looking for ways to optimize the rendering.

Show HN: I built a real-time OSINT dashboard pulling 15 live global feeds

Sup HN,<p>So I got tired of bouncing between Flightradar, MarineTraffic, and Twitter every time something kicked off globally, so I wrote a dashboard to aggregate it all locally. It’s called Shadowbroker.<p>I’ll admit I leaned way too hard into the "movie hacker" aesthetic for the UI, but the actual pipeline underneath is real. It pulls commercial/military ADS-B, the AIS WebSocket stream (about 25,000+ ships), N2YO satellite telemetry, and GDELT conflict data into a single MapLibre instance.<p>Getting this to run without melting my browser was the hardest part. I'm running this on a laptop with an i5 and an RTX 3050, and initially, dumping 30k+ moving GeoJSON features onto the map just crashed everything. I ended up having to write pretty aggressive viewport culling, debounce the state updates, and compress the FastAPI payloads by like 90% just to make it usable.<p>My favorite part is the signal layer—it actually calculates live GPS jamming zones by aggregating the real-time navigation degradation (NAC-P) of commercial flights overhead.<p>It’s Next.js and Python. I threw a quick-start script in the releases if you just want to spin it up, but the repo is open if you want to dig into the backend.<p>Let me know if my MapLibre implementation is terrible, I'm always looking for ways to optimize the rendering.

Show HN: Swarm – Program a colony of 200 ants using a custom assembly language

We built an ant colony simulation as an internal hiring challenge at Moment and decided to open it up publicly.<p>You write a program in a custom assembly-like (we call it ant-ssembly) instruction set that controls 200 ants. Each ant can sense nearby cells (food, pheromones, home, other ants) but has no global view. The only coordination mechanism is pheromone trails, which ants can emit and sense them, but that's it. Your program runs identically on every ant.<p>The goal is to collect the highest percentage of food across a set of maps. Different map layouts (clustered food, scattered, obstacles) reward very different strategies. The leaderboard is live.<p>Grand prize is a trip to Maui for two paid for by Moment. Challenge closes March 12.<p>Curious what strategies people discover. We've seen some surprisingly clever emergent behavior internally.

Show HN: Moongate – Ultima Online server emulator in .NET 10 with Lua scripting

I've been building a modern Ultima Online server emulator from scratch. It's not feature-complete (no combat, no skills yet), but the foundation is solid and I wanted to share it early.<p>What it does today: - Full packet layer for the classic UO client (login, movement, items, mobiles) - Lua scripting for item behaviors (double-click a potion, open a door — all defined in Lua, no C# recompile) - Spatial world partitioned into sectors with delta sync (only sends packets for new sectors when crossing boundaries) - Snapshot-based persistence with MessagePack - Source generators for automatic DI wiring, packet handler registration, and Lua module exposure - NativeAOT support — the server compiles to a single native binary - Embedded HTTP admin API + React management UI - Auto-generated doors from map statics (same algorithm as ModernUO/RunUO)<p>Tech stack: .NET 10, NativeAOT, NLua, MessagePack, DryIoc, Kestrel<p>What's missing: Combat, skills, weather integration, NPC AI. This is still early — the focus so far has been on getting the architecture right so adding those systems doesn't require rewiring everything.<p>Why not just use ModernUO/RunUO? Those are mature and battle-tested. I started this because I wanted to rethink the architecture from scratch: strict network/domain separation, event-driven game loop, no inheritance-heavy item hierarchies, and Lua for rapid iteration on game logic without recompiling.<p>GitHub: <a href="https://github.com/moongate-community/moongatev2" rel="nofollow">https://github.com/moongate-community/moongatev2</a>

Show HN: Poppy – A simple app to stay intentional with relationships

I built Poppy as a side project to help people keep in touch more intentionally. Would love feedback on onboarding, reminders, and overall UX. Happy to answer questions.

Show HN: Jido 2.0, Elixir Agent Framework

Hi HN!<p>I'm the author of an Elixir Agent Framework called Jido. We reached our 2.0 release this week, shipping a production-hardened framework to build, manage and run Agents on the BEAM.<p>Jido now supports a host of Agentic features, including:<p>- Tool Calling and Agent Skills - Comprehensive multi-agent support across distributed BEAM processes with Supervision - Multiple reasoning strategies including ReAct, Chain of Thought, Tree of Thought, and more - Advanced workflow capabilities - Durability through a robust Storage and Persistence layer - Agentic Memory - MCP and Sensors to interface with external services - Deep observability and debugging capabilities, including full stack OTel<p>I know Agent Frameworks can be considered a bit stale, but there hasn't been a major release of a framework on the BEAM. With a growing realization that the architecture of the BEAM is a good match for Agentic workloads, the time was right to make the announcement.<p>My background is enterprise engineering, distributed systems and Open Source. We've got a strong and growing community of builders committed to the Jido ecosystem. We're looking forward to what gets built on top of Jido!<p>Come build agents with us!

Show HN: Timber – Ollama for classical ML models, 336x faster than Python

Show HN: Govbase – Follow a bill from source text to news bias to social posts

Govbase tracks every bill, executive order, and federal regulation from official sources (Congress.gov, Federal Register, White House). An AI pipeline breaks each one down into plain-language summaries and shows who it impacts by demographic group.<p>It also ties each policy directly to bias-rated news coverage and politician social posts on X, Bluesky, and Truth Social. You can follow a single bill from the official text to how media frames it to what your representatives are saying about it.<p>Free on web, iOS, and Android.<p><a href="https://govbase.com" rel="nofollow">https://govbase.com</a><p>I'd love feedback from the community, especially on the data pipeline or what policy areas/features you feel are missing.

Show HN: I built a sub-500ms latency voice agent from scratch

I built a voice agent from scratch that averages ~400ms end-to-end latency (phone stop → first syllable). That’s with full STT → LLM → TTS in the loop, clean barge-ins, and no precomputed responses.<p>What moved the needle:<p>Voice is a turn-taking problem, not a transcription problem. VAD alone fails; you need semantic end-of-turn detection.<p>The system reduces to one loop: speaking vs listening. The two transitions - cancel instantly on barge-in, respond instantly on end-of-turn - define the experience.<p>STT → LLM → TTS must stream. Sequential pipelines are dead on arrival for natural conversation.<p>TTFT dominates everything. In voice, the first token is the critical path. Groq’s ~80ms TTFT was the single biggest win.<p>Geography matters more than prompts. Colocate everything or you lose before you start.<p>GitHub Repo: <a href="https://github.com/NickTikhonov/shuo" rel="nofollow">https://github.com/NickTikhonov/shuo</a><p>Follow whatever I next tinker with: <a href="https://x.com/nick_tikhonov" rel="nofollow">https://x.com/nick_tikhonov</a>

Show HN: Now I Get It – Translate scientific papers into interactive webpages

Understanding scientific articles can be tough, even in your own field. Trying to comprehend articles from others? Good luck.<p>Enter, Now I Get It!<p>I made this app for curious people. Simply upload an article and after a few minutes you'll have an interactive web page showcasing the highlights. Generated pages are stored in the cloud and can be viewed from a gallery.<p>Now I Get It! uses the best LLMs out there, which means the app will improve as AI improves.<p>Free for now - it's capped at 20 articles per day so I don't burn cash.<p>A few things I (and maybe you will) find interesting:<p>* This is a pure convenience app. I could just as well use a saved prompt in Claude, but sometimes it's nice to have a niche-focused app. It's just cognitively easier, IMO.<p>* The app was built for myself and colleagues in various scientific fields. It can take an hour or more to read a detailed paper so this is like an on-ramp.<p>* The app is a place for me to experiment with using LLMs to translate scientific articles into software. The space is pregnant with possibilities.<p>* Everything in the app is the result of agentic engineering, e.g. plans, specs, tasks, execution loops. I swear by Beads (<a href="https://github.com/steveyegge/beads" rel="nofollow">https://github.com/steveyegge/beads</a>) by Yegge and also make heavy use of Beads Viewer (<a href="https://news.ycombinator.com/item?id=46314423">https://news.ycombinator.com/item?id=46314423</a>) and Destructive Command Guard (<a href="https://news.ycombinator.com/item?id=46835674">https://news.ycombinator.com/item?id=46835674</a>) by Jeffrey Emanuel.<p>* I'm an AWS fan and have been impressed by Opus' ability to write good CFN. It still needs a bunch of guidance around distributed architecture but way better than last year.