Show HN: Pure Particles, an open source art particle toy built with Three.js https://bit.ly/4ipdiTZ

Show HN: Pure Particles, an open source art particle toy built with Three.js Hi HN, I’m Romain, a designer from France. I’m trying to build a tool to make art with code. This is the first piece. Drop any image and the particles take its colours. You can record a clip of the result. Built with three.js. Free and open source Feedback welcome. https://bit.ly/4ikivwd October 29, 2026 at 07:57AM

Show HN: A Cursor-like workspace for your LaTeX papers https://bit.ly/4idb2jZ

Show HN: A Cursor-like workspace for your LaTeX papers https://bit.ly/4yT2SSX October 29, 2026 at 06:39AM

Show HN: Fabivo – Furniture design from a photo to a numbered cut list https://bit.ly/4sOSzxT

Show HN: Fabivo – Furniture design from a photo to a numbered cut list Hello all, Ive been building Fabivo, a free furniture planner that runs in the browser. You sketch it simply by drawing it, (by far my favorite feature of this app), clicking for boards or just selecting some starting designs. But what i think separates fabivo from others its the image to model ai assistant, which took me a looong time to develop and get some quirks fixed, thanks god latest models are focusing on 3d, thats been helping too! The photo part is an image to JSON CAD harness: the model returns a parametric furniture model in mm that the app can build and edit. That harness is also what i benchmark, and honestly its the reason the app exists. I wanted to see if a photo could turn into a plan i could take to the saw. Also finetuned some image models and found interesting stuff, that was a first. My intention was to basically reuse the drawing feature to convert a 2d line sketch from image models like gpt image / nano banana to my parametric model, which "worked", and was actually thinking of using that for prod, but then cheaper/better models at 3d launched, but anyways, i learned a lot! Im getting better performance from my harness than from plain 3js, tho this is anecdotally and havent actually benchmarked it... Happy to answer anything, especially about the harness or the cut order. edit: Adding the numbers, missed them. 17 photos of real furniture, three models, same harness, medium reasoning, same tools. Scored on how close the returned structure and boards are to the gold model, so its my own set. (tho i have a hard time making a good scoring metric... just usually go by look!) Model (medium reasoning) Avg. time / photo Avg. input tokens / photo Avg. output tokens / photo Avg. tool calls / photo GPT-6.1 Terra 9.5 s 30,409 632 1.06 Claude Sonnet 5.5 13.6 s 50,167 1,592 1.18 Claude Haiku 5.5 34.9 s 66,786 6,658 2.35 Terra was both cheaper per photo and the most consistent to my likings. Sonnet was close on quality, Haiku needed way more tool calls to get there. https://bit.ly/4jRTTO1 October 29, 2026 at 06:21AM

Show HN: A Lego-like composable Framework for RSI https://bit.ly/4srM7wi

Show HN: A Lego-like composable Framework for RSI https://bit.ly/4BquXDD October 28, 2026 at 08:25AM

Show HN: Cirkit – Annotate, Trace, and Ask AI About Schematic PDFs https://bit.ly/3UMhqpg

Show HN: Cirkit – Annotate, Trace, and Ask AI About Schematic PDFs Documentation tool for EEs to document their design better https://bit.ly/3Wvg1nt October 28, 2026 at 04:09AM

Show HN: Aqueous Archive Format https://bit.ly/4BzRBJO

Show HN: Aqueous Archive Format https://bit.ly/4z9cM3v October 28, 2026 at 12:31AM

Show HN: Software Hate Index (real-time ranking of SaaS complaints) https://bit.ly/3WOmmKQ

Show HN: Software Hate Index (real-time ranking of SaaS complaints) https://bit.ly/4yQa4z3 October 27, 2026 at 09:46AM

Show HN: ESP-Watchtower – $3 ESP8266 Home Network Sentinel and Canary Honeypot https://bit.ly/47Nd0Bt

Show HN: ESP-Watchtower – $3 ESP8266 Home Network Sentinel and Canary Honeypot https://bit.ly/4i6S5zl October 27, 2026 at 03:15AM

Show HN: Shittyscreenshares.com https://bit.ly/4sqlzLO

Show HN: Shittyscreenshares.com https://bit.ly/4yKC2Mz October 27, 2026 at 01:02AM

Show HN: ADRs for AI – Deliberation records for human–AI work, versioned in Git https://bit.ly/4BJa8DB

Show HN: ADRs for AI – Deliberation records for human–AI work, versioned in Git Why is this worth your time? Essentially, this solves two problems of AI-assisted programming: (a) the first problem is how to do oversight and what do oversight of and this is solved through the ORRCF (for Options, Recommendation, Rationale, Confidence, Falsifier) question format (in which models are supposed to surface all their doubts with fully fleshed out recommendations, with falsifier, to be approved or overridden) (b) the second problem is how to scaffold sustainable AI-written code; the ADRs (Architectural Deliberation Records, after Nygard's decision records) written by the models, summarize all the thought process involved in any design decision and provides the blueprint for the code. In tandem, the records provide both the reason why certain non-obvious design choices were made, from a constant, to a library choice, to an ordering in an operation; and also under what conditions different decisions would be made, for instance, in a different platform, or under different latency requirements, or for a different audience, and so on. This is a framework I've been working for over a year, that is the starting point of all my projects. It involves a lot of moving parts and my challenge is figuring out onboarding. I'm hoping the plugin, ADRs4AI/plugins, just created and now also submitted to Anthropic's marketplace, will make it easier. I am also in the process of recording introductory videos. I would like feedback on the onboarding, on what you make of the currently online materials, and how I could make any of this clearer. Thanks! https://bit.ly/3W76mDv October 25, 2026 at 01:43AM

Show HN: Visualization of Top News Stories https://bit.ly/3UMLn8H

Show HN: Visualization of Top News Stories news stories has bigger headings based on updates/votes. you can also add/remove the sources and save a unique link https://bit.ly/4yKXlh8 October 25, 2026 at 11:10PM

Show HN: AI Heat Map Sourced from over 140 News Feeds https://bit.ly/4i2R5MB

Show HN: AI Heat Map Sourced from over 140 News Feeds https://bit.ly/4z1zcDQ October 25, 2026 at 04:25AM

Show HN: Redmi phone can do things that $16K GPU is needed for on Ethereum https://bit.ly/4jKgV9v

Show HN: Redmi phone can do things that $16K GPU is needed for on Ethereum Recursive State proof verification from genesis: 10.78 seconds Peak process memory: 192.2 MiB Subsequent block downloads and verification: 11.94–13.20 seconds https://bit.ly/4spM5VU October 25, 2026 at 12:32AM

Show HN: Call4me – a ChatGPT plugin that lets it make phone calls on your behalf https://bit.ly/4jF1EGU

Show HN: Call4me – a ChatGPT plugin that lets it make phone calls on your behalf https://bit.ly/47DC2mC October 24, 2026 at 11:00PM

Show HN: How religions evolved? Charts, Maps, Citations https://bit.ly/4zhtldJ

Show HN: How religions evolved? Charts, Maps, Citations https://bit.ly/4z9scF6 October 24, 2026 at 08:37AM

Show HN: Encarta 95 Web https://bit.ly/4dQNcIc

Show HN: Encarta 95 Web Encarta is super nostalgic, and I was surprised it wasn't available to use online. So I embarked on the difficult task of making it so. The way this works is the most convoluted, nonsense and brittle system ever... but it works as a statically hosted gh-pages site! Please note: All the material on this site is freely available through the internet archive (see github readme) Github repo -> https://bit.ly/4zSjDyp https://bit.ly/4BqtOfa October 24, 2026 at 08:18AM

Show HN: Inference Cooperative, user-governed AI https://bit.ly/4yDCZpM

Show HN: Inference Cooperative, user-governed AI https://bit.ly/4yIF8k1 October 24, 2026 at 04:32AM

Show HN: libfpv - Modding FPV Drone into Any Game https://bit.ly/4zInT3s

Show HN: libfpv - Modding FPV Drone into Any Game https://bit.ly/4i2gqGv October 23, 2026 at 10:26PM

Show HN: Impossible Machines: declaratively program the physical world https://bit.ly/4yW6iEx

Show HN: Impossible Machines: declaratively program the physical world Hey all — Sharing a thought experiment I put together. The premise: what might declarative programming feel like for the physical world? We’re already familiar with this paradigm in software land (eg SQL etc where we declare what we want and the execution engine figures out how). I didn’t have access to an actuator so I mocked up the concept in software. The way it works is you demonstrate the effect and the system figures out how to achieve it. So say you want to ring a bell and you have a crank. The system will find a way to connect the two. If you add obstacles it figures out how to work around them (runs a constraint solver runs behind the scenes). I think what’s interesting about this is being able to think about the problem at a higher level of abstraction, with the system figuring out a way to achieve the intended effect. Anyways, curious to read reactions, feedback, and interesting ideas on how to extend this! https://bit.ly/489UfIx October 23, 2026 at 12:54AM

Show HN: CATT 0.2.1: Test agents/services against a Muse/Instinct-alike, in CI https://bit.ly/4sgHi8Y

Show HN: CATT 0.2.1: Test agents/services against a Muse/Instinct-alike, in CI Hey HN, we recently open sourced our Consumer Agent Testing Tool to help teams iterate against proprietary assistants like Muse and Instinct. We built this to reduce our own frustrations with building an MCP/API for these agents to consume. The problem: testing against these tools is hard! Agent memory + account statefulness make reproducibility, in the ways required for repeatable/reliable outcomes, between agents and the APIs/MCPs they interact with very challenging. The solution: CATT lets devs test their services against a surrogate Muse or Instinct agent (not affiliated with either of these companies). Testers define starting conditions, approvals, and success conditions for their service's state. CATT then records the full interaction (incl. tool calls, responses) so it can diff what happened from what the agent reports. Runs can be tested in local builds or via CI, with or without LLM backing. CATT is designed to emulate the important parts of personal assistant interactions. Its got: - Tool calls and command-line tool access - Simulated approvals for service actions - Outcomes tracking and comparison - Optional MCP profile based on an unofficial Muse template There are limitations to how much we know about different assistant specific behaviors so the tool labels its assumptions as `documented`, `unofficial`, `assumed` or `unknown` for developer review. We'll do our best to keep the tool updated as new insights about the internal workings of these assistants come to light. It is an area of active research and interest, a good example is this recent report on how Instinct uses Exa for web search [0]. Are you building a connector or MCP? If yes, we'd like to hear from you! Have you had assistants behave incoherently, use stale information, or procalim success without evidence? What else should we add tests for? The best way to get started is...to point your coding assistant at the repo and ask it to integrate the tool with your CI or local testing pipeline! Hope it's helpful! [0]: https://bit.ly/4ic9xB1... https://bit.ly/4i9kSSt October 22, 2026 at 11:18PM