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Show HN: What Is Hacker News Working On?

I tagged all comments from "What Are You Working On?" (like <a href="https://news.ycombinator.com/item?id=45561428">https://news.ycombinator.com/item?id=45561428</a>) posts and built a simple SvelteKit website, hope it's helpful to find people with similar projects. I'm also thinking of adding some analysis of project types over time to see changes in tech

Show HN: See chords as flags – Visual harmony of top composers on musescore

I designed a relative piano-roll-based music notation. I used 12 colored arranged in a specific way to make visible the main effects and oppositions of Western tonal harmony. The tonic is always white, so a manual annotation/interpretation is required for each MIDI file.<p>All chords are flags of three to four colors. Minor mode is darker, major mode is lighter. Colors are arranged in thirds.<p>I sorted the pieces from simple complex harmony. I also wrote a bit of text to explain what you may see. There's also a corpus of structures: hyperlinks of tags that allow you to find similar patterns throughout my corpus of 3000+ popular pieces.<p>My method makes chord progressions memorizable and instantly visible in the scores. No preparation of Roman numeral analysis / chord symbols analysis is required. After a bit of training the chords will stare right in your eyes.<p>It's not synesthesia, it's a missing script for tonal music which makes harmonically identical things look the same (or similar).<p>I've also recorded lectures on my method in Russian (<a href="https://www.youtube.com/playlist?list=PLzQrZe3EemP5pVPYMwBJGtiejiN3qtCce" rel="nofollow">https://www.youtube.com/playlist?list=PLzQrZe3EemP5pVPYMwBJG...</a>). I'm sorry I haven't yet found time to re-record in English.<p>I've also sketched a friendlier intro: <a href="https://vpavlenko.github.io/d/" rel="nofollow">https://vpavlenko.github.io/d/</a><p>Sorry, but this thing won't make any sense if you're color-blind.<p>It's open-source: <a href="https://github.com/vpavlenko/rawl" rel="nofollow">https://github.com/vpavlenko/rawl</a><p>Earlier context: <a href="https://news.ycombinator.com/item?id=39165596">https://news.ycombinator.com/item?id=39165596</a><p>(Back then colors were less logical, and there was no corpus of 3000+ piece annotated yet)

Show HN: qqqa – A fast, stateless LLM-powered assistant for your shell

I built qqqa as an open-source project, because I was tired of bouncing between shell, ChatGPT / the browser for rather simple commands. It comes with two binaries: qq and qa.<p>qq means "quick question" - it is read-only, perfect for the commands I always forget.<p>qa means "quick agent" - it is qq's sibling that can run things, but only after showing its plan and getting an approval by the user.<p>It is built entirely around the Unix philosophy of focused tools, stateless by default - pretty much the opposite of what most coding agent are focusing on.<p>Personally I've had the best experience using Groq + gpt-oss-20b, as it feels almost instant (up to 1k tokens/s according to Groq) - but any OpenAI-compatible API will do.<p>Curious if the HN crowd finds it useful - and of course, AMA.

Show HN: I scraped 3B Goodreads reviews to train a better recommendation model

Hi everyone,<p>For the past couple months I've been working on a website with two main features:<p>- <a href="https://book.sv" rel="nofollow">https://book.sv</a> - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews<p>- <a href="https://book.sv/intersect" rel="nofollow">https://book.sv/intersect</a> - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: <a href="https://book.sv/remove-my-data" rel="nofollow">https://book.sv/remove-my-data</a>)<p>Technical info available here: <a href="https://book.sv/how-it-works" rel="nofollow">https://book.sv/how-it-works</a><p>Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.<p>Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.

Show HN: I built a local-first daily planner for iOS

Show HN: A CSS-Only Terrain Generator

Show HN: Why write code if the LLM can just do the thing? (web app experiment)

I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI consistency between requests. The capability exists; performance is the problem. When inference gets 10x faster, maybe the question shifts from "how do we generate better code?" to "why generate code at all?"

Show HN: Why write code if the LLM can just do the thing? (web app experiment)

I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI consistency between requests. The capability exists; performance is the problem. When inference gets 10x faster, maybe the question shifts from "how do we generate better code?" to "why generate code at all?"

Show HN: Strange Attractors

I went down the rabbit hole on a side project and ended up building this: Strange Attractors(<a href="https://blog.shashanktomar.com/posts/strange-attractors" rel="nofollow">https://blog.shashanktomar.com/posts/strange-attractors</a>). It’s built with three.js.<p>Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun.<p>My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to 3D, not sure if it’s mathematically correct, but it’s the coolest by far. I have left all the params configurable, so give it a try. I called it Simone (Maybe).<p>If you like math-art experiments, check it out. Would love feedback, especially from folks who know more about the math side.

Show HN: Strange Attractors

I went down the rabbit hole on a side project and ended up building this: Strange Attractors(<a href="https://blog.shashanktomar.com/posts/strange-attractors" rel="nofollow">https://blog.shashanktomar.com/posts/strange-attractors</a>). It’s built with three.js.<p>Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun.<p>My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to 3D, not sure if it’s mathematically correct, but it’s the coolest by far. I have left all the params configurable, so give it a try. I called it Simone (Maybe).<p>If you like math-art experiments, check it out. Would love feedback, especially from folks who know more about the math side.

Show HN: Strange Attractors

I went down the rabbit hole on a side project and ended up building this: Strange Attractors(<a href="https://blog.shashanktomar.com/posts/strange-attractors" rel="nofollow">https://blog.shashanktomar.com/posts/strange-attractors</a>). It’s built with three.js.<p>Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun.<p>My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to 3D, not sure if it’s mathematically correct, but it’s the coolest by far. I have left all the params configurable, so give it a try. I called it Simone (Maybe).<p>If you like math-art experiments, check it out. Would love feedback, especially from folks who know more about the math side.

Show HN: Pipelex – Declarative language for repeatable AI workflows

We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them.<p>Why this instead of yet another workflow builder?<p>- Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open standard under MIT: language spec, runtime, API server, editor extensions, MCP server, n8n node. - Composable: pipes can call other pipes, created by you or shared in the community.<p>Why a domain-specific language?<p>- We need context, meaning and nuances preserved in a structured syntax that both humans and LLMs can understand - We need determinism, control, and reproducibility that pure prompts can't deliver - Bonus: editors, diffs, semantic coloring, easy sharing, search & replace, version control, linters…<p>How we got there:<p>Initially, we just wanted to solve every use-case with LLMs but kept rebuilding the same agentic patterns across different projects. So we challenged ourselves to keep the code generic and separate from use-case specifics, which meant modeling workflows from the relevant knowledge and know-how.<p>Unlike existing code/no-code frameworks for AI workflows, our abstraction layer doesn't wrap APIs, it transcribes business logic into a structured, unambiguous script executable by software and AI. Hence the "declarative" aspect: the script says what should be done, not how to do it. It's like a Dockerfile or SQL for AI workflows.<p>Additionally, we wanted the language to be LLM-friendly. Classic programming languages hide logic and context in variable names, functions, and comments: all invisible to the interpreter. In Pipelex, these elements are explicitly stated in natural language, giving AI full visibility: it's all logic and context, with minimal syntax.<p>Then, we didn't want to write Pipelex scripts ourselves so we dogfooded: we built a Pipelex workflow that writes Pipelex workflows. It's in the MCP and CLI: "pipelex build pipe '…'" runs a multi-step, structured generation flow that produces a validated workflow ready to execute with "pipelex run". Then you can iterate on it yourself or with any coding agent.<p>What’s included: Python library, FastAPI and Docker, MCP server, n8n node, VS Code extension.<p>What we’d like from you<p>1. Build a workflow: did the language work for you or against you? 2. Agent/MCP workflows and n8n node usability. 3. Suggest new kinds of pipes and other AI models we could integrate 4. Looking for OSS contributors to the core library but also to share pipes with the community<p>Known limitations<p>- Connectors: Pipelex doesn’t integrate with “your apps”, we focus on the cognitive steps, and you can integrate through code/API or using MCP or n8n - Visualization: we need to generate flow-charts - The pipe builder is still buggy - Run it yourself: we don’t yet provide a hosted Pipelex API, it’s in the works - Cost-tracking: we only track LLM costs, not image generation or OCR costs yet - Caching and reasoning options: not supported yet<p>Links<p>- GitHub: <a href="https://github.com/Pipelex/pipelex" rel="nofollow">https://github.com/Pipelex/pipelex</a> - Cookbook: <a href="https://github.com/Pipelex/pipelex-cookbook" rel="nofollow">https://github.com/Pipelex/pipelex-cookbook</a> - Starter: <a href="https://github.com/Pipelex/pipelex-starter" rel="nofollow">https://github.com/Pipelex/pipelex-starter</a> - VS Code extension: <a href="https://github.com/Pipelex/vscode-pipelex" rel="nofollow">https://github.com/Pipelex/vscode-pipelex</a> - Docs: [<a href="https://docs.pipelex.com" rel="nofollow">https://docs.pipelex.com</a>](<a href="https://docs.pipelex.com/" rel="nofollow">https://docs.pipelex.com/</a>) - Demo video (2 min): <a href="https://youtu.be/dBigQa8M8pQ" rel="nofollow">https://youtu.be/dBigQa8M8pQ</a> - Discord for support and sharing: <a href="https://go.pipelex.com/discord" rel="nofollow">https://go.pipelex.com/discord</a><p>Thanks for reading. If you try Pipelex, tell us exactly where it hurts, that’s the most valuable feedback we can get.

Show HN: I made a heatmap diff viewer for code reviews

0github.com is a pull request viewer that color-codes every diff line/token by how much human attention it probably needs. Unlike PR-review bots, we try to flag not just by "is it a bug?" but by "is it worth a second look?" (examples: hard-coded secret, weird crypto mode, gnarly logic, ugly code).<p>To try it, replace github.com with 0github.com in any pull-request URL. Under the hood, we split the PR into individual files, and for each file, we ask an LLM to annotate each line with a data structure that we parse into a colored heatmap.<p>Examples:<p><a href="https://0github.com/manaflow-ai/cmux/pull/666" rel="nofollow">https://0github.com/manaflow-ai/cmux/pull/666</a><p><a href="https://0github.com/stack-auth/stack-auth/pull/988" rel="nofollow">https://0github.com/stack-auth/stack-auth/pull/988</a><p><a href="https://0github.com/tinygrad/tinygrad/pull/12995" rel="nofollow">https://0github.com/tinygrad/tinygrad/pull/12995</a><p><a href="https://0github.com/simonw/datasette/pull/2548" rel="nofollow">https://0github.com/simonw/datasette/pull/2548</a><p>Notice how all the example links have a 0 prepended before github.com. This navigates you to our custom diff viewer where we handle the same URL path parameters as github.com. Darker yellows indicate that an area might require more investigation. Hover on the highlights to see the LLM's explanation. There's also a slider on the top left to adjust the "should review" threshold.<p>Repo (MIT license): <a href="https://github.com/manaflow-ai/cmux" rel="nofollow">https://github.com/manaflow-ai/cmux</a>

Show HN: I made a heatmap diff viewer for code reviews

0github.com is a pull request viewer that color-codes every diff line/token by how much human attention it probably needs. Unlike PR-review bots, we try to flag not just by "is it a bug?" but by "is it worth a second look?" (examples: hard-coded secret, weird crypto mode, gnarly logic, ugly code).<p>To try it, replace github.com with 0github.com in any pull-request URL. Under the hood, we split the PR into individual files, and for each file, we ask an LLM to annotate each line with a data structure that we parse into a colored heatmap.<p>Examples:<p><a href="https://0github.com/manaflow-ai/cmux/pull/666" rel="nofollow">https://0github.com/manaflow-ai/cmux/pull/666</a><p><a href="https://0github.com/stack-auth/stack-auth/pull/988" rel="nofollow">https://0github.com/stack-auth/stack-auth/pull/988</a><p><a href="https://0github.com/tinygrad/tinygrad/pull/12995" rel="nofollow">https://0github.com/tinygrad/tinygrad/pull/12995</a><p><a href="https://0github.com/simonw/datasette/pull/2548" rel="nofollow">https://0github.com/simonw/datasette/pull/2548</a><p>Notice how all the example links have a 0 prepended before github.com. This navigates you to our custom diff viewer where we handle the same URL path parameters as github.com. Darker yellows indicate that an area might require more investigation. Hover on the highlights to see the LLM's explanation. There's also a slider on the top left to adjust the "should review" threshold.<p>Repo (MIT license): <a href="https://github.com/manaflow-ai/cmux" rel="nofollow">https://github.com/manaflow-ai/cmux</a>

Show HN: In a single HTML file, an app to encourage my children to invest

Show HN: In a single HTML file, an app to encourage my children to invest

Show HN: In a single HTML file, an app to encourage my children to invest

Show HN: Learn German with Games

I just started learning German, and it has been a frustrating experience, to say the least. There are so many seemingly arbitrary rules that make pattern recognition very difficult. Therefore, I have been looking for ways to make memorization a bit easier and fun. So, I came up with a bunch of games to make learning German a bit more engaging. Hope you find it useful as well!

Show HN: Learn German with Games

I just started learning German, and it has been a frustrating experience, to say the least. There are so many seemingly arbitrary rules that make pattern recognition very difficult. Therefore, I have been looking for ways to make memorization a bit easier and fun. So, I came up with a bunch of games to make learning German a bit more engaging. Hope you find it useful as well!

Show HN: Bash Screensavers

A github project to collect a bunch of bash-based screensavers/visualizations.