Show HN: Radix – Visual UI for agentic programming https://ift.tt/t2ePXHM

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: Treepeat – Code similarity detection using Tree-sitter https://ift.tt/oXDVcur

Show HN: Treepeat – Code similarity detection using Tree-sitter treepeat is a tool that finds similarities in your codebase. Find duplicate code blocks meaningful to the language (classes/functions), not just lines. Find near-duplicates: ignore whitespace, strings, high level AST nodes such as function and names. Find structurally similar code: anonymize identifiers, constants, etc. Pull requests welcome: This is very much an proof of concept - I'm happy with it, but I haven't supported very many languages at present. Languages supported: astro, bash, css, go, html, javascript, lua, markdown (plus codeblocks), python, sql, typescript, java, kotlin, rust, yaml https://ift.tt/mQ9ETWp September 22, 2026 at 10:23PM

Show HN: Conway's Game of Life in boot sector https://ift.tt/kDiXV9O

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/j6KgtPX September 21, 2026 at 11:58PM

Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes https://ift.tt/c7laxtk

Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes Hello HN! I've spent years debugging Windows crashes with tools that were either friendly but limited (e.g. Visual Studio) or powerful but archaic (e.g. WinDbg). I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg. I built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what matters. Everything is interlinked so you can quickly and intuitivly navigate through the process space. ForensicDbg comes with an MCP server which allows for agenic debugging. The work done to interpret and interlink your data also benefits AI tools. It removes the risk of hallucinations while building a stable foundation for them to work from without spending tokens. If you want to try it out you can sign up and get a free beta license here: https://ift.tt/N8gCnhd https://ift.tt/QY3mhbr September 24, 2026 at 12:45AM

Show HN: Crest – Answer Claude Code approvals from your MacBook's notch https://ift.tt/SuvdBaK

Show HN: Crest – Answer Claude Code approvals from your MacBook's notch https://ift.tt/plHN5iZ September 23, 2026 at 11:18PM

Show HN: Training a model to identify AI web content from structure alone https://ift.tt/ed9O3CJ

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/nMXJipP . The code is on GitHub: https://ift.tt/GJwrNd6 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/V2x5NF7 . 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/FZ2reBp . https://ift.tt/nMXJipP September 22, 2026 at 06:30PM

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

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