The best Hacker News stories from Show from the past day
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Show HN: Wyrm – Solve algebra by touch, built on an open-source soundness engine
There is a mobile game called DragonBox. It sort of tricks you into learning algebra by starting with very abstract manipulations of a puzzle that must follow rules... gradually the game teaches you more and more rules and also strips out the more abstract elements until on the last levels you are finally solving real equations. I loved it, it taught my kids algebra.... and it was just fun.<p>Over the years I often thought that there should be a calculator for Algebra that works this way... something where you can drag terms around and cancel & distribute with gestures, but most importantly enter your own problems. It should also do more kinds of problems than DragonBox allowed. So I finally decided to build it.<p><a href="https://dicroce.github.io/wyrm/home.html" rel="nofollow">https://dicroce.github.io/wyrm/home.html</a><p>Here's a video showing it: <a href="https://www.youtube.com/watch?v=_STbS4zvIlU" rel="nofollow">https://www.youtube.com/watch?v=_STbS4zvIlU</a>. If you'd rather just play with it: there's a limited in-browser demo (real engine, a few example equations, no download) on the landing page — <a href="https://dicroce.github.io/wyrm/home.html" rel="nofollow">https://dicroce.github.io/wyrm/home.html</a>.<p>The app can be found on iOS (<a href="https://apps.apple.com/us/app/wyrm-math/id6782342042">https://apps.apple.com/us/app/wyrm-math/id6782342042</a>) and as of this week on Google Play (<a href="https://play.google.com/store/apps/details?id=com.dicroce.wyrm">https://play.google.com/store/apps/details?id=com.dicroce.wy...</a>).<p>I also decided to open source the underlying math engine so others could build on it: <a href="https://github.com/dicroce/wyrm_math" rel="nofollow">https://github.com/dicroce/wyrm_math</a>. My goal for the engine btw is to build it all the way up to Calculus.<p>Monetization is deliberately boring: the engine is free (MIT), and the polished gesture app is $4.99 once. No subscriptions, ads, accounts, or analytics.<p>I'd love feedback on the engine design — especially from anyone who's worked on CAS or proof-assistant-adjacent problems. And if you played DragonBox as a kid and wished it went further: this is for you!
Show HN: Runloom – Go-style coroutines for Python free-threaded
Show HN: Frugon – Find which LLM calls a cheaper model could handle (local, MIT)
I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week.<p>I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks.<p>I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model.<p>When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The general savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie.<p>It's deliberately small, because I tend to over complicate/think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation.<p>Nothing is hosted. capture is an optional local proxy on your own machine, and there's no endpoint in the path of your data. You can confirm this by checking the source.<p>I included a demo so you can check out the output. It has a synthetic 56k call log (a month's worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving.<p>Try it:<p><pre><code> uvx frugon analyze --demo
</code></pre>
or<p><pre><code> uv tool install frugon
</code></pre>
Then point it at your own logs.<p>All feedback is welcome, especially any on the routing/quality logic, or anything else, good or bad.
Show HN: Reviving my 2001 college band with AI
25 years ago, I joined a band called Fading Maize at Ripon College in Wisconsin. We did what we could with what we had. We recorded 3 albums over the next 3 years and played at as many bars and coffee shops as we could. We built a website with Microsoft Frontpage. Then we went our separate ways, got married, had kids, focused on other things.<p>Earlier this year I had the idea to approach the lead singer who wrote all of the lyrics and melodies to the stuff we played back then, and wanted to "reimagine" everything in 2026 using AI. That's the project I want to share here!<p>The site has a before/after player where you can flip between the original dorm-room recording and the 2026 version mid-song without losing your place, so you can hear exactly what changed. The original 2001 website is preserved and browsable at <a href="https://www.fadingmaize.com/2001" rel="nofollow">https://www.fadingmaize.com/2001</a>, rough edges intact.<p>Working on this, the thing that sparked in my own mind is that it was an experiment in a certain way to use AI. The songs, lyrics, and arrangements are the original human work (in this case from 2001-2003). We wrote the lyrics, we created the melodies, we played the parts, it just didn't sound as good as we heard it in our own heads.<p>The stuff AI creates is awesome, but it means less if it's just the AI cranking everything out from the ground up. In our case, the AI was only there to help us get the results we originally wanted back in 2001 when we were cooking ramen in our dorm rooms and couldn't afford anything fancy<p>Being fully transparent about our use of AI, sticking tightly to our original lyrics and melodies, but making full use of AI to give us the studio, session players, and production budget we never had seemed like the right balance of concerns.<p>I'm super proud of how it turned out and the transparency we've used along the way. Happy to discuss the audio pipeline, the site (Next.js), or what it's
like to A/B your 20-year-old self!<p>p.s. Oh and check this out! I remember this day. Our site was getting absolutely hammered! <a href="https://www.youtube.com/watch?v=KPJWlnN9tSE&t=43" rel="nofollow">https://www.youtube.com/watch?v=KPJWlnN9tSE&t=43</a>
Show HN: Reverse-engineering web apps into agent tools
Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes.<p>The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort.<p>Check out these short demos below that show the agent in software you're probably familiar with:<p>- Jira: <a href="https://demo.frigade.com/hn?skill=jira">https://demo.frigade.com/hn?skill=jira</a><p>- Spotify: <a href="https://demo.frigade.com/hn?skill=spotify">https://demo.frigade.com/hn?skill=spotify</a><p>- Hacker News (lol): <a href="https://demo.frigade.com/hn?skill=hackernews">https://demo.frigade.com/hn?skill=hackernews</a><p>- Full Demo: <a href="https://demo.frigade.com/hn?skill=full-demo">https://demo.frigade.com/hn?skill=full-demo</a><p>As you can see in the examples, you can do way more (and faster) than what you normally would be able to via point and click. And we never even touched the source code of these products!<p>Why do this?<p>In an ideal world, every application has an MCP server or an easily-digestible API available for AI agents to feed from. In practice, we found that even very modern software tends to have a spider web of confusing APIs and services that AI agents simply cannot use out of the box. Security also becomes a huge issue as applications have different (often homebrewed) standards for how endpoints are secured (JWTs/cookies/mix of both). Finally, having an actual browser agent go in and use the application on behalf of the user (i.e. computer-use), is simply too brittle, slow, and burns a lot of tokens.<p>We took our existing browser agent that’s already trained to use and learn authenticated applications, and added an extra step that automatically turns the app’s authenticated APIs into "recipes". A recipe is a mix of the following:<p>- API endpoint + method<p>- Authentication method (and how to retrieve refresh auth tokens/cookies)<p>- Response schema<p>- Input schema (for POST/PUT)<p>- Human readable description of what the tool does<p>Putting it all together, these become reusable tools for LLMs, all without writing or maintaining any code. Even if the APIs change our agent figures this out and replaces the recipe for the tool with the updated version.<p>Adding tools to an AI agent becomes super simple this way:<p>- Our agent trains on the app and builds the recipes<p>- The app owner enables discovered tools from our dashboard<p>- The agent can now take actions on the user’s behalf directly inside the application. For instance, saying something like "invite my teammate to my workspace" would securely call the existing API endpoint for inviting users without proxying or relaying through a third party.<p>Of course, there's a ton of edge cases you run into when you try to do this - every application is intrinsically different despite how many "standards" exist. Fun fact: graphql was by far the worst API to work with in standardizing the recipes.<p>Looking forward to your feedback/comments!
Show HN: LastShelf – an emergency map of your family's documents bills& contacts
After my father was diagnosed with Stage 3 kidney cancer, my family was thrown into a tailspin. Getting second opinions, planning surgery, ensuring insurance coverage, coping with the fear. It was a lot to process.<p>In the middle of dealing with all the medical logistics, I realized none of our family could answer if he:
- Had a medical directive?
- How to trigger his life insurance policy?
- Where is his will and who is the executor?
- What bank accounts and credit cards existed?
- What bills are not on auto-pay?
- When these bills due and how are they paid?<p>That wasn’t solved by password managers or budgeting apps. So I built it.<p>LastShelf: automatically discovers, documents and distributes a map of critical life documents, expenses & contacts in the event of an emergency. Register here: <a href="https://www.lastshelf.ai/" rel="nofollow">https://www.lastshelf.ai/</a><p>If you’ve lived through a similar crisis, I really want to hear what would have made the process easier.<p>Anyone who shares their feedback with me will get the first year free. Send a note to support [at] lastshelf.ai
Show HN: I mapped 8.5M research papers into an interactive atlas
When I read papers, I have to jump between multiple tabs to find the dataset, code, videos, peer reviews, and so on. I tried to fix this with this project.<p>It started as a project just for papers on arXiv, but after its initial success on Twitter (got like 1.9k views: the most I have gotten for a post), I have now expanded it to include other openly available papers from PubMed Central, bioRxiv, medRxiv, and eLife. These papers have been linked with their genes, proteins, diseases, drugs, clinical trials, 3D protein structures, code, and cited and similar papers.<p>This project now has four parts:<p>First, a map. I embedded nearly 8.5M papers (with SPECTER2), ran UMAP for 2D representation, and rendered them as a scatterplot. The dots can be clicked to see brief information about the papers, like an LLM TLDR, key findings, peer reviews, linked entities, and more. The clusters are also labeled, though you might have to zoom in.<p>Second, I built a detailed paper page for each paper. They give you the paper's full text, images, videos, peer reviews (from OpenReview), GitHub links, Hugging Face dataset/model links, clinical trials, genes, diseases, 3D protein structures, cited papers, and similar papers. You can also copy the whole page, including the full paper text and image URLs, as markdown for your LLM.<p>Third, I have released an extension so you can read all this information in your sidebar by clicking "open in Tomesphere" that shows up in arXiv, PMC, bioRxiv, Google Scholar, or medRxiv. I have tried to provide as much information as possible in the extension, though for things like viewing all the images or a 3D protein structure, you might still have to go to the paper page using the link provided in the extension.<p>Fourth, all this data is available for your LLM via MCP. The MCP does have a 50-query free limit (this jumps 10x with signup).<p>Note: this project is still in beta, so papers might have some mismatched information. I am rolling out feedback forms soon to improve the data quality. Thank you so much for taking the time to read this.
Show HN: I mapped 8.5M research papers into an interactive atlas
When I read papers, I have to jump between multiple tabs to find the dataset, code, videos, peer reviews, and so on. I tried to fix this with this project.<p>It started as a project just for papers on arXiv, but after its initial success on Twitter (got like 1.9k views: the most I have gotten for a post), I have now expanded it to include other openly available papers from PubMed Central, bioRxiv, medRxiv, and eLife. These papers have been linked with their genes, proteins, diseases, drugs, clinical trials, 3D protein structures, code, and cited and similar papers.<p>This project now has four parts:<p>First, a map. I embedded nearly 8.5M papers (with SPECTER2), ran UMAP for 2D representation, and rendered them as a scatterplot. The dots can be clicked to see brief information about the papers, like an LLM TLDR, key findings, peer reviews, linked entities, and more. The clusters are also labeled, though you might have to zoom in.<p>Second, I built a detailed paper page for each paper. They give you the paper's full text, images, videos, peer reviews (from OpenReview), GitHub links, Hugging Face dataset/model links, clinical trials, genes, diseases, 3D protein structures, cited papers, and similar papers. You can also copy the whole page, including the full paper text and image URLs, as markdown for your LLM.<p>Third, I have released an extension so you can read all this information in your sidebar by clicking "open in Tomesphere" that shows up in arXiv, PMC, bioRxiv, Google Scholar, or medRxiv. I have tried to provide as much information as possible in the extension, though for things like viewing all the images or a 3D protein structure, you might still have to go to the paper page using the link provided in the extension.<p>Fourth, all this data is available for your LLM via MCP. The MCP does have a 50-query free limit (this jumps 10x with signup).<p>Note: this project is still in beta, so papers might have some mismatched information. I am rolling out feedback forms soon to improve the data quality. Thank you so much for taking the time to read this.
Show HN: FableCut – A browser video editor AI agents can drive (zero deps)
Show HN: FableCut – A browser video editor AI agents can drive (zero deps)
Show HN: Analog Watch
Show HN: Analog Watch
Show HN: Yamanote.fun – A complete soundscape for Tokyo's Yamanote line
After visiting Japan for the first time a decade ago I became completely enamoured with Tokyo's Yamanote Line railway loop. Particularly the sonic experience of it. Like so many others I fell in love with the charming departure melodies and enjoyed discovering experiences like Yamanot.es (<a href="https://news.ycombinator.com/item?id=45045307">https://news.ycombinator.com/item?id=45045307</a>) here on Hacker News when I returned home.<p>But it wasn't until my second trip to Tokyo that I truly appreciated how much the door chimes, on-board announcements and train noise were contributing to the rich soundscape that I loved.<p>I returned home and found myself playing YouTube videos of Yamanote Line journeys as I worked. The combination of sonics, ambience and softly spoken Japanese was incredibly soothing to me.<p>But these recordings were often incomplete, poorly captured or out of date, and I wanted something far more comprehensive.<p>So I gathered up all of the constituent parts from Reddit threads, YouTube videos and Japanese fan sites, and set about recreating the experience of riding the Yamanote Line in Logic Pro X. Melody, door chimes and announcement, all stitched together under a bed of train noise and ambience.<p>I turned those soundscapes into an Alexa Skill (<a href="https://www.amazon.co.uk/Paul-Jackson-Yamanote-Line/dp/B07S18QRMV" rel="nofollow">https://www.amazon.co.uk/Paul-Jackson-Yamanote-Line/dp/B07S1...</a>) in 2019 and began to think about a companion website to share the soundscapes with a wider audience.<p>Seven years later and that website is Yamanote.fun: <a href="https://www.yamanote.fun/" rel="nofollow">https://www.yamanote.fun/</a>.<p>It's a small installable web app that plays the soundscapes like a playlist. All 30 stations and in both directions, since the inner and outer loops use different melodies. You can skip forward or back a station, and there's a scrub bar broken into melody / chime / ambience / announcement so you can jump straight to the bit you want. Each station has its own shareable link (yamanote.fun/jy13-ikebukuro-inner) that unfurls with the right station name and artwork when you share it.<p>It's a progressive web app too, so you can add it to your home screen and it behaves like a native app. There's an option to offline the audio too.<p>Under the hood it's relatively basic stuff: plain HTML, CSS & JS, audio served from Cloudflare R2 and the site hosted on Netlify. I was impressed to see how far I could get with the free tiers of these services. I designed the whole thing in Figma (I'm a Product Designer) and used Claude Code to architect and deliver the polished UI, PWA plumbing, offline caching and share-link infrastructure.<p>I would love feedback, particularly from anyone who's ridden the real thing.
Show HN: Getting GLM 5.2 running on my slow computer
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me.<p>But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility.<p>I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context. How it responds in int4 and whether the quality is maintained or not. Until I got to the point, on my computer with 32GB of RAM, I was able to communicate with GLM 5.2 with times that, of course, aren't high in cold start, but even then, we're talking about 0.1 tok/s, but that wasn't important to me. The important thing was the journey to reach this goal. I just wanted it to work at all costs, even slowly.<p>So I created Colibrì, which was born from a very simple idea, to be honest, but tested in every way, where a 744B Mixture-of-Experts model activates only ~40B parameters per token—and only ~11 GB of those change from token to token (the routed experts). So:<p>The dense part (attention, shared experts, embeddings—~17B params) stays resident in RAM at int4 (~9.9 GB); The 21,504 routed experts (75 MoE layers × 256 experts + the MTP head, ~19 MB each at int4) live on disk (~370 GB) and are streamed on demand, with a per-layer LRU cache, an optional pinned hot-store, and the OS page cache as a free L2.<p>The engine is a single C file (c/glm.c, ~1,300 lines) plus small headers. No BLAS, no Python at runtime, no GPU.No GPU or serious hardware because I don't have that hardware so I can't test it on hardware that is more powerful than my computer.Colibrì is a one-person project, written and tested entirely on a 12-core laptop with 25 GB of RAM — the numbers above are the ceiling of what I can measure at home.<p>Any feedback is welcome! (and if anyone wanted to participate in the project I would be delighted)<p>Repo: <a href="https://github.com/JustVugg/colibri" rel="nofollow">https://github.com/JustVugg/colibri</a>
Show HN: Getting GLM 5.2 running on my slow computer
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me.<p>But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility.<p>I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context. How it responds in int4 and whether the quality is maintained or not. Until I got to the point, on my computer with 32GB of RAM, I was able to communicate with GLM 5.2 with times that, of course, aren't high in cold start, but even then, we're talking about 0.1 tok/s, but that wasn't important to me. The important thing was the journey to reach this goal. I just wanted it to work at all costs, even slowly.<p>So I created Colibrì, which was born from a very simple idea, to be honest, but tested in every way, where a 744B Mixture-of-Experts model activates only ~40B parameters per token—and only ~11 GB of those change from token to token (the routed experts). So:<p>The dense part (attention, shared experts, embeddings—~17B params) stays resident in RAM at int4 (~9.9 GB); The 21,504 routed experts (75 MoE layers × 256 experts + the MTP head, ~19 MB each at int4) live on disk (~370 GB) and are streamed on demand, with a per-layer LRU cache, an optional pinned hot-store, and the OS page cache as a free L2.<p>The engine is a single C file (c/glm.c, ~1,300 lines) plus small headers. No BLAS, no Python at runtime, no GPU.No GPU or serious hardware because I don't have that hardware so I can't test it on hardware that is more powerful than my computer.Colibrì is a one-person project, written and tested entirely on a 12-core laptop with 25 GB of RAM — the numbers above are the ceiling of what I can measure at home.<p>Any feedback is welcome! (and if anyone wanted to participate in the project I would be delighted)<p>Repo: <a href="https://github.com/JustVugg/colibri" rel="nofollow">https://github.com/JustVugg/colibri</a>
Show HN: 18 Words
Show HN: 18 Words
Show HN: Chiptune Radio
I built a chiptune song generator and its broadcasting algorithmically generated chiptune music.
Show HN: Fortress – a stealth Chromium so your agents stop getting blocked
Show HN: Neil the Seal Game
Neil the seal now has a game to destroy Battery Point.