Show HN: Gat – Version large files with Git, without an LFS server https://ift.tt/Nl4r1Fo

Show HN: Gat – Version large files with Git, without an LFS server Hi HN, I built Gat because I wanted one thing from data versioning: check out an old Git commit and recover the datasets, model weights, or assets that belong to it. It's basically a lock file for large files. I’d appreciate any feedback! https://ift.tt/5jrxfsv September 27, 2026 at 10:50PM

Show HN: Lofi Cities – Pixel-art city nights with browser-generated lofi https://ift.tt/H2f9OYg

Show HN: Lofi Cities – Pixel-art city nights with browser-generated lofi https://loficities.com/ September 28, 2026 at 12:14AM

Show HN: Sixb, a TypeScript ontology framework for modeling your domain https://ift.tt/qacpVek

Show HN: Sixb, a TypeScript ontology framework for modeling your domain https://docs.sixb.ai September 26, 2026 at 11:45PM

Show HN: Blender Copilot https://ift.tt/gkuJrh4

Show HN: Blender Copilot Deepseek generated Blender harness in 7USD. I wanted to remake my 6DoF spaceship flying prototype. What I did not want to do was model the spaceship. Manually. In Blender. So instead of spending a month learning to model, I built a Copilot inside Blender in a day. Which is the same thing, except I still cannot model. A chat panel in the 3D viewport sidebar. I type a sentence. The model writes Python, and it runs against my actual scene, the file open on screen, not a copy that some other process has to keep in sync. Then I sat down and had a conversation with my own addon. Five sentences: 1. Make a scifi looking spaceship. It should look scifi-y 2. Add RCS thrusters to get 6DoF flight 3. Make the RCS thrusters match the scifi look. And also position them properly. 4. Give the entire Spaceship a nice flying animation. Make it smooth. Roll and sway. 5. Add exhaust flames to main thrusters as well as RCS thrusters. animate the RCS thrusters to fire in sync with the animated motion. they should fire the correct ones so that expected motion should happen. 15 minutes. 52 tool calls. A ship with swept wings, a tail fin and a glass canopy. Four sets of steering thrusters, 22 exhaust flames. A full flight animation. The bit I did not expect. It checked every one of its own thrusters to make sure nothing was in the way. Found the rear ones were firing straight into the side of the ship. So it rebuilt them, angled them outward, moved them to the wingtips, and checked them all again. Nobody asked it to do that. It decided exhaust should not be firing into the ship it just built. Total bill for building the tool: $7.18. 3,833 requests, 613M tokens, 97% served from cache. One spaceship: about a seventh of a cent. Now the uncomfortable part. I didn't model a single vertex. I didn't read Blender's addon dev docs. I didn't learn its Python API, or how to draw a panel in it. Everything the tool needed to know, it asked the running Blender instead. I didn't learn how to build an agent harness either. I built one anyway. I didn't become a Blender artist. I became the person who gave hands to a model. Recently I read someone who described his new job: every spec, ticket and fix written by an AI, nobody reading any of it, twelve-hour days spent pressing enter. No sense of victory. I know exactly what he means. I just did the same. I am not proud of the prototype. Gamedevs were known to spend time making game engines before they started making the games. Its different now. No joy. https://ift.tt/DEWGMSX September 26, 2026 at 11:36PM

Show HN: Jev predicting your life choices https://ift.tt/Udbz0v3

Show HN: Jev predicting your life choices Built a simple quiz to check whether AI (Jev) can simply learn and predict life choices. Experiment) https://quiz.seek.ws/ September 26, 2026 at 09:54PM

Show HN: Jev Plays Pokémon Red https://ift.tt/dObMNQl

Show HN: Jev Plays Pokémon Red Hey HN! Wanted to share a fun project I've been hacking on. Given Jev can make decisions really fast (but not fast enough to play Doom yet sadly), I wanted to try and push it to play a more complex game than Tetris. So I went with Pokémon. I've spent endless hours playing this game as a child so building this was a ton of fun. I open sourced everything in case you want to hack on it yourself here: https://ift.tt/ehKVJ9Y The game is being streamed live including the tokens and cost - hopefully we get all the badges and don't get stuck in a cave :) https://jev-pokemon.vercel.app/ September 25, 2026 at 07:58PM

Show HN: I couldn't deal with another Claude Code tab https://ift.tt/OqUrXzu

Show HN: I couldn't deal with another Claude Code tab https://aidash.dev/ September 25, 2026 at 11:52PM

Show HN: Offline recorder and transcriber on your MacBook's notch https://ift.tt/pEy2DGP

Show HN: Offline recorder and transcriber on your MacBook's notch Was dogfooding a version of this until a friend saw this and said I should probably productize this since it was more accurate than other commercial transcribers like Whisper. https://ift.tt/a5HgWr3 September 25, 2026 at 01:31AM

Show HN: Radix – Visual UI for agentic programming https://ift.tt/4t2uDW6

Show HN: Radix – Visual UI for agentic programming Hey HN, I'm Jordan from Radix. Radix is a UI tool for programming agents. You prompt your agent to generate a workspace for a task you're working on and get an interactive widget that persists locally on disk. I built Radix because I always needed tools while I was writing code: tools to test little experiments, to play around with UI, to visualise results etc. Sometimes these would end up as python tools that read and plotted data, or separate React apps spun up just to test a single feature. Lately obviously I've been using Claude artifacts but these are quite limited. I wanted a system where it was easy to leave comments or adapt an artifact without having to give vague descriptions back to the agent, and where ideally I could actually shift away from a chat window as the main interface. I've got a pro version of Radix which I will launch soon. This current version is entirely free and is definitely a beta -- there will be rough edges! Note: There's no telemetry or data captured. Your messages run entirely through your own agent. I require a key but that's just to get an idea of how many people are using it. Everything is stored locally (the workspaces are actually just React apps which you can edit "artisanally" if you want). If you have any questions please reply here or email me hello@radix-os.com Thank you! https://radix-os.com September 25, 2026 at 12:05AM

Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/syUpPkw

Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/jI961VN September 23, 2026 at 11:35PM

Show HN: Conway's Game of Life in boot sector https://ift.tt/Hl0Ffjq

Show HN: Conway's Game of Life in boot sector Hello HN! On these weekends, I was sitting and thinking about where our industry is going, and how fun it was earlier, when we wrote more code than chat messages. The thoughts quickly turned into a wish to build something like we did earlier. In my case, it ended up as something much "earlier" than I had in mind initially... So, Conway's Game of Life running from a 512-byte x86 boot sector. Maybe you will find it interesting. I tried to comment the code as much as possible, which might be especially useful if you are learning assembly. Have fun! https://ift.tt/TOJ7I4N September 21, 2026 at 11:58PM

Show HN: Training a model to identify AI web content from structure alone https://ift.tt/6cbiEue

Show HN: Training a model to identify AI web content from structure alone Hey HN! We’re Vincent and Jochen from Sitefire ( https://sitefire.ai ). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM). With Sitefire (YC W26), we help marketing teams get recommended by AI Search (ChatGPT, Google AI Overviews, AI Mode, Claude, etc.). Our software monitors prompts, sees which web pages get cited, and uses these insights to help marketing teams take action, e.g. create YouTube videos or write the right blog posts. This means we have a commercial stake in AI-generated web content. And for now, high-information, AI-generated content works great to get cited and recommended in AI Search. But after talking to hundreds of marketing teams, it became clear that everyone despises AI-generated content (“AI slop”). And yet, everyone still wants to leverage AI to create content. So we asked ourselves: what characterizes AI slop? Can we train a model to identify it from human-generated web pages? Researchers from the University of Maryland and Google DeepMind already asked this question for fiction. Their paper StoryScope (Russell et al., 2026) showed that you can tell AI-written stories from human ones by their structure alone, without looking at the words. We ported their pipeline to commercial web pages. Using the Wayback Machine, we collected 2,250 blog posts from 268 B2B company websites that were written before ChatGPT existed. For each blog post, five AI models (GPT-5.4, Claude Sonnet 4.6, Gemini 3 Flash, DeepSeek V3.2, Kimi K2.5) wrote their own version. Instead of looking at the words, we looked at how each post is built. We had an AI model answer 214 questions about every post, e.g. how hard it pushes its own product, whether it backs up its claims with sources, or whether it quotes a named expert. Then we trained a classifier on these answers. On blog posts it had never seen before, our classifier told AI-generated and human posts apart with 98% accuracy, getting only 19 of 1,740 wrong. Why does it work so well? Because all five AI models write in a similar shape. Mapping every AI model’s values for these features, we see they cluster together, while the human values sit apart and spread out much more. Of the 1% most unique blog posts in our data set, 149 are human, only 4 are AI. So what characterizes AI slop? It tells you the same thing three times. The title already promises what you'll get ("How to Cut Onboarding Time in Half"), the intro lays out what's coming, and the ending says it all again. 77% of the AI posts end by repeating their main point, compared to only 12% of the human posts. We call it the tidy, self-announcing blog post. Still, each AI model has its own accent. We trained a second classifier to tell which of the five AI models wrote a post, or whether a human did. It picks the right author 79% of the time, where random guessing (1 in 6) would get 17%. Almost all of its mistakes are mix-ups between the AI models, not between human and AI. The cool thing about structural features is that you can't simply reword your way out of it. We had each AI model rewrite its own posts until, on average, 73% of their original 13-word sequences were gone, and the AI slop classifier still worked just as well. We're building this into Sitefire: our agents get a structural understanding of text, so the posts they write go deeper and vary the way human writing does. There's a lot we haven't tested yet, like the myriad of humanizer tools, human rewriting, restructuring a post, or prompting an AI model to explicitly avoid these habits. And our human posts are mostly from 2020 to 2022, while the AI posts were generated in August 2026. Structure can't really tell when a human post was written, but it's still not a same-year comparison. We published the study with all the figures on arXiv: https://ift.tt/sKhnQ8a . The code is on GitHub: https://ift.tt/WrjGEUR We're pretty sure your own blog isn't AI slop, is it? We built a checker that runs one of your posts through the ten features from the paper, so you can see for yourself (the full report asks for a work email): https://ift.tt/i6n9DTk . Think you can tell AI slop from human writing? We also made a little game to see if you can keep up with our model, which gets all five rounds right: https://ift.tt/tum9ESv . https://ift.tt/sKhnQ8a September 22, 2026 at 06:30PM

Show HN: FreeCoffee – Self-hostable donation tool with crypto support https://ift.tt/52pCGri

Show HN: FreeCoffee – Self-hostable donation tool with crypto support https://ift.tt/ph7ce5y September 23, 2026 at 12:31AM

Show HN: Notes on Agentic AI – A text-first guide for practicing engineers https://ift.tt/q9rP86K

Show HN: Notes on Agentic AI – A text-first guide for practicing engineers https://ift.tt/nkPBYwl September 22, 2026 at 11:49PM

Show HN: A website that tracks US food prices every day https://ift.tt/vxiKFdn

Show HN: A website that tracks US food prices every day The US Department of Agriculture (USDA) publishes daily food price data for about 290 commodities on: - what growers were paid at shipping point - what buyers paid at the big city wholesale markets - what prices supermarkets advertised in their weekly ads This data comes out as dozens of separate text reports and PDFs, one per market, with no history and no way to see how prices change over time. I think this should be public data that is easy to access and understand, so I built an open-source tracker for it. Github: https://ift.tt/lGM83xS https://ift.tt/bp2ieE5 September 22, 2026 at 12:54AM

Show HN: Two unnecessary playgrounds – C# and ZX Spectrum in the browser https://ift.tt/3pshJPj

Show HN: Two unnecessary playgrounds – C# and ZX Spectrum in the browser C# playground https://ift.tt/BzZGymn ZX-Spectrum playground https://ift.tt/LwT5omV September 22, 2026 at 12:52AM

Show HN: Viaduct – C4 models that coding agents can read and update https://ift.tt/GCXM4f3

Show HN: Viaduct – C4 models that coding agents can read and update https://ift.tt/TyLF5lY September 21, 2026 at 11:20PM