Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/dn4ZpIf

Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/oj0rFug September 23, 2026 at 10:05PM

Show HN: AgentRun: DSL to turn agents into Workflows https://ift.tt/dluk9wC

Show HN: AgentRun: DSL to turn agents into Workflows Hi HN, I just open sourced the DSL that our harness in grep.ai uses to turn repeatable parts of agent work into workflows. You can combine tool calls, code, Jev-powered system one decisions for things like routing and screening evidence, and agents when a step needs more investigation. Our harness uses the traces and retro notes agents leave behind when doing a job to figure out which parts can become a workflow. The idea is to make the work easier to understand and avoid paying for a full agent loop where one isn’t needed. For example, a research workflow can split a question into subquestions, send agents to research them in parallel, use Jev to screen the evidence, and have another agent write the report. You can inspect the steps, evaluate the evidence screening separately, or change one agent without rebuilding everything. The DSL and examples are in our GitHub. There’s a scripted demo you can run without API keys: https://ift.tt/vIlDE6e You can also use it as a Pi extension to build, inspect, and run workflows: https://ift.tt/nyx1j4z I would love to hear if this is useful to others. More background on how AgentRun works in this video: https://www.youtube.com/watch?v=vOVhtGjtwpg . Or read about our use cases in this article: https://ift.tt/x6rDuyi . https://ift.tt/vIlDE6e September 23, 2026 at 11:42PM

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

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/GvfW30U https://ift.tt/683qu2b September 23, 2026 at 11:15PM

Show HN: Conway's Game of Life in boot sector https://ift.tt/p6G45Ss

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/HtByb4w September 21, 2026 at 10:28PM

Show HN: Training a model to identify AI web content from structure alone https://ift.tt/Ekli5PQ

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/yJgHscu . The code is on GitHub: https://ift.tt/bm9TsGH 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/nhmTuGE . 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/FXULSak . https://ift.tt/yJgHscu September 22, 2026 at 05:00PM

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

Show HN: FreeCoffee – Self-hostable donation tool with crypto support https://ift.tt/ywiVJ8X September 22, 2026 at 11:01PM

Show HN: Blackgit – use Git to only download those cared files https://ift.tt/IlsEQJA

Show HN: Blackgit – use Git to only download those cared files Git is supposed to be bad at handling mono repos especially where there are many binary files like game projects repo, and also there are no permisson settings, but are important in game development. BlackGit's cli client lets you download only cared files through sparse-checkout(initially not even the default root files) with a mini agent to use natural languages to manipulate the repo, and blackgit's server serves a file level control layer as a proxy to upstreams(github/gitlab) https://ift.tt/zu8MI1O September 22, 2026 at 12:13AM