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Show HN: Training a model to identify AI web content from structure alone

Hey HN! We’re Vincent and Jochen from Sitefire (<a href="https://sitefire.ai">https://sitefire.ai</a>). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM).<p>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.<p>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.<p>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?<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>We published the study with all the figures on arXiv: <a href="https://arxiv.org/abs/2609.15369" rel="nofollow">https://arxiv.org/abs/2609.15369</a>. The code is on GitHub: <a href="https://github.com/pulse-energy-eu/slopshape" rel="nofollow">https://github.com/pulse-energy-eu/slopshape</a><p>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): <a href="https://sitefire.ai/slop-checker">https://sitefire.ai/slop-checker</a>.<p>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: <a href="https://sitefire.ai/spot-the-slop">https://sitefire.ai/spot-the-slop</a>.

Show HN: JevBench, a reproducible benchmark for typed decision models

Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects <i>really</i> perform in comparison.<p>Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.<p>JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.<p>A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.<p>Leaderboard right now:<p><pre><code> #1 - Jev 74.4 #2 - SemIf 73.1 #3 - djev 73.0 #4 - Winnow-12B Q8 71.2 #5 reflex 4B 70.3. </code></pre> MIT harness, public items, frozen artifacts, scoring code and public per-task outcomes:<p><a href="https://github.com/fstandhartinger/jevbench" rel="nofollow">https://github.com/fstandhartinger/jevbench</a><p>Two no-signup demos:<p><a href="https://who-is-right.app.mintapis.com" rel="nofollow">https://who-is-right.app.mintapis.com</a><p><a href="https://is-it-ai-slop.app.mintapis.com" rel="nofollow">https://is-it-ai-slop.app.mintapis.com</a><p>Limitations: English-only; latency from one German server; local/demo latency gets a disclosed ×2 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.<p>Wdyt?

Show HN: Drop – A rootless Linux sandbox with gVisor support

I created Drop because I always felt uneasy installing and running third-party programs using my main user account. A single compromised dependency means a full compromise of the system. What is even worse, because I ship software from my computer, a single compromised dependency can lead to compromise of all the users of my software.<p>Containers and VMs are one solution, but for local work, they are often detrimental to productivity. It takes effort to configure a machine with all the tools and configs needed for productive work, but a container or a VM will be stripped of all these tools. This is great for production deployments, where the aim is a reproducible system with minimal dependencies, but can get in the way of productive local work.<p>Drop is language independent, but the workflow is inspired by Python's virtualenv. With virtualenv the environment isolation is only a convention that relies on installed dependencies being good citizens. With Drop the isolation is enforced.<p>Each Drop environment gets its own writable and easily disposable home dir, with only selected config files and dirs from the original home mounted, mostly read-only.<p>Drop uses Linux namespaces for isolation (user, mount, network, PID, IPC, cgroup), doesn't require root and, as an option, uses gVisor user-space kernel, which adds protection against exploiting host kernel vulnerabilities to escape the sandbox.<p>I don't want to make the introductory post too lengthy, but I'm here to answer any questions and give any additional technical details.<p>Note: This is my 3rd submission of the project, the first two did not draw attention. Since then I have added support for gVisor and created a project website to better explain the concept and organize documentation.

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:<p>- what growers were paid at shipping point<p>- what buyers paid at the big city wholesale markets<p>- what prices supermarkets advertised in their weekly ads<p>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.<p>I think this should be public data that is easy to access and understand, so I built an open-source tracker for it.<p>Github: <a href="https://github.com/kadoa-org/food-price-monitor" rel="nofollow">https://github.com/kadoa-org/food-price-monitor</a>

Show HN: jevals – replacing LLM judges with typed Jev decisions

Show HN: jevals – replacing LLM judges with typed Jev decisions

Show HN: Foremerge – Catch intent conflicts between parallel coding agents

At, GPTree, we run several coding agents across our team on one repo using parallel worktrees. Apart from wasted time reviewing and fixing conflicts at PR time, the failures that hurt the most are when multiple plans or tickets cause architecture changes that cannot both be true. Ex. one agent replaces a class while another one is in the process of extending it. Git only notices if the resulting patches happen to touch the same lines and the review only catches it if they are familiar with both tickets.<p>Foremerge is a local "git like" coordination layer that sits above git (ie. does not interact with or change the way git and worktrees function), Before editing each agent publishes an intent and the scopes it will change, with the operation it plans to complete on each one.<p><pre><code> foremerge intent publish --agent "$A" \ --summary "Replace PaymentService with StripePaymentService" \ --scope symbol:PaymentService=replace foremerge intent publish --agent "$B" \ --summary "Add PayPal support to PaymentService" \ --scope symbol:PaymentService=extend </code></pre> The publish by the 2nd agent returns a HIGH destructive_vs_additive finding before writing any code. Agents keep their own worktrees and the shared state is one SQLite file in gits common direectory. No hooks, no merge drivers, nothing rewrites your history.<p>It ships as one Rust binary with a CLI and MCP server with 18 tools and `foremerge setup all` wires it into Claude Code, Codex and Cursor. Because the protocol has nothing provider specific, a Claude agent and a Codex agent coordinate through the same store. Before any work is accepted, Foremerge runs a named check that you configured against the exact git state of the change. An agent that says tests pass is recorded but it dosnt satisfy the acceptance gate without running the check itself.<p>Detection is deterministic, no judge model reading your code. HIGH conflicts are only asserted for declared operations, ie. matches inferred from prose cap out below high. Claims are advisory leases, not locks so two agents can still hold the same scope without deadlock. The open source version is single matching and so not a distributed consensus.<p>We have tested this up to 98 parallel agents all working on the same repo with zero conflicts (was supposed to be 100 but 2 agents failed to run due to resource limitations)<p>I replayed 76 intents on my own agents from a build last week in the order they happened. The sample had exactly 1 conflict (which was flagged) and the review found a blind spot where one agent claimed scope by class name and the other claimed it by an internal method. We are working on fixing that for the next release.<p>Setup is a 30s install by pasting the quickstart instructions from the readme.md into your agent or manually: `curl -fsSL <a href="https://foremerge.com/install.sh" rel="nofollow">https://foremerge.com/install.sh</a> | sh` or `cargo install --locked foremerge`, then `foremerge init && foremerge setup all` in a repo. Apache-2.0.<p>The feedback I want most is which conflicts between your agents plans would you actually want flagged and which would you tollerate as noise?<p>Repo here: <a href="https://github.com/naw103/foremerge" rel="nofollow">https://github.com/naw103/foremerge</a> Website: <a href="https://foremerge.com" rel="nofollow">https://foremerge.com</a><p>More information on the problems this solves: <a href="https://foremerge.com/blog/" rel="nofollow">https://foremerge.com/blog/</a>

Show HN: Foremerge – Catch intent conflicts between parallel coding agents

At, GPTree, we run several coding agents across our team on one repo using parallel worktrees. Apart from wasted time reviewing and fixing conflicts at PR time, the failures that hurt the most are when multiple plans or tickets cause architecture changes that cannot both be true. Ex. one agent replaces a class while another one is in the process of extending it. Git only notices if the resulting patches happen to touch the same lines and the review only catches it if they are familiar with both tickets.<p>Foremerge is a local "git like" coordination layer that sits above git (ie. does not interact with or change the way git and worktrees function), Before editing each agent publishes an intent and the scopes it will change, with the operation it plans to complete on each one.<p><pre><code> foremerge intent publish --agent "$A" \ --summary "Replace PaymentService with StripePaymentService" \ --scope symbol:PaymentService=replace foremerge intent publish --agent "$B" \ --summary "Add PayPal support to PaymentService" \ --scope symbol:PaymentService=extend </code></pre> The publish by the 2nd agent returns a HIGH destructive_vs_additive finding before writing any code. Agents keep their own worktrees and the shared state is one SQLite file in gits common direectory. No hooks, no merge drivers, nothing rewrites your history.<p>It ships as one Rust binary with a CLI and MCP server with 18 tools and `foremerge setup all` wires it into Claude Code, Codex and Cursor. Because the protocol has nothing provider specific, a Claude agent and a Codex agent coordinate through the same store. Before any work is accepted, Foremerge runs a named check that you configured against the exact git state of the change. An agent that says tests pass is recorded but it dosnt satisfy the acceptance gate without running the check itself.<p>Detection is deterministic, no judge model reading your code. HIGH conflicts are only asserted for declared operations, ie. matches inferred from prose cap out below high. Claims are advisory leases, not locks so two agents can still hold the same scope without deadlock. The open source version is single matching and so not a distributed consensus.<p>We have tested this up to 98 parallel agents all working on the same repo with zero conflicts (was supposed to be 100 but 2 agents failed to run due to resource limitations)<p>I replayed 76 intents on my own agents from a build last week in the order they happened. The sample had exactly 1 conflict (which was flagged) and the review found a blind spot where one agent claimed scope by class name and the other claimed it by an internal method. We are working on fixing that for the next release.<p>Setup is a 30s install by pasting the quickstart instructions from the readme.md into your agent or manually: `curl -fsSL <a href="https://foremerge.com/install.sh" rel="nofollow">https://foremerge.com/install.sh</a> | sh` or `cargo install --locked foremerge`, then `foremerge init && foremerge setup all` in a repo. Apache-2.0.<p>The feedback I want most is which conflicts between your agents plans would you actually want flagged and which would you tollerate as noise?<p>Repo here: <a href="https://github.com/naw103/foremerge" rel="nofollow">https://github.com/naw103/foremerge</a> Website: <a href="https://foremerge.com" rel="nofollow">https://foremerge.com</a><p>More information on the problems this solves: <a href="https://foremerge.com/blog/" rel="nofollow">https://foremerge.com/blog/</a>

Show HN: Lossless-memory – a personal AI memory that never summarizes

Show HN: Lossless-memory – a personal AI memory that never summarizes

Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM

Sorry for the pretentious name, I know, I know.. It just contains all the pieces I would like to see a AGI model to have, and I can't stand the temptation. Before throwing rocks at me, please take a glance at the Readme, and I hope it will cover your mood a little bit.<p>So, first of all it does work and you can see the sample from the whole training run here: <a href="https://raw.githubusercontent.com/volotat/mini-AGI/refs/heads/main/runs/samples.txt" rel="nofollow">https://raw.githubusercontent.com/volotat/mini-AGI/refs/head...</a><p>Here is the scaling law graph I have so far, and it looks very promising: <a href="https://github.com/volotat/mini-AGI/blob/main/assets/scaling.png" rel="nofollow">https://github.com/volotat/mini-AGI/blob/main/assets/scaling...</a><p>The model was built under my deep dissatisfaction so we cannot really train even moderately big models (1B+ scale) on the consumer's hardware. We can inference and fine-tune them for sure, but I would like to have full control over what the model sees over the training run, so it is fully aligned with my interests, not some corporations.<p>I was thinking about for some time and come up with two interesting ideas I thought worth pursuing: MoE with a lot of experts that gets added and pruned from the model while it trains, where only a small subset of of experts are actually in use at any particular moment + batch 1 training on the single continuous stream of data.<p>First allows us to be bounded only by the disk space in terms of number of parameters and load and unload experts only when they are needed. The second (if figured out and it turns out to be doable) allows us to get aways with small VRAM capacity because we do not need to store big randomized batches and their respective gradients.<p>I started brainstorming with Claude and after some time we found an approach that seems to be promising, and low and behold, a few weeks pass and you can see the results yourself.<p>Obviously, I did use AI in the process of making this project and I am pretty sure it would be completely impossible for me to do something like this without it, so I hope it is more than justified.<p>The model is still running over the first of 7.8B characters corpus I selected for training, so the weights are not out yet, and it's about a couple weeks of waiting until they are cooked at the current reading speed. And yeah, the model just read continuous interleaved passages from the dataset, each by 32K characters long each as a single stream. Just as you or I would do.<p>The set up seems to be really simple so you can git clone the project, run it and observe everything for yourself.<p>Thanks for your attention.

Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM

Sorry for the pretentious name, I know, I know.. It just contains all the pieces I would like to see a AGI model to have, and I can't stand the temptation. Before throwing rocks at me, please take a glance at the Readme, and I hope it will cover your mood a little bit.<p>So, first of all it does work and you can see the sample from the whole training run here: <a href="https://raw.githubusercontent.com/volotat/mini-AGI/refs/heads/main/runs/samples.txt" rel="nofollow">https://raw.githubusercontent.com/volotat/mini-AGI/refs/head...</a><p>Here is the scaling law graph I have so far, and it looks very promising: <a href="https://github.com/volotat/mini-AGI/blob/main/assets/scaling.png" rel="nofollow">https://github.com/volotat/mini-AGI/blob/main/assets/scaling...</a><p>The model was built under my deep dissatisfaction so we cannot really train even moderately big models (1B+ scale) on the consumer's hardware. We can inference and fine-tune them for sure, but I would like to have full control over what the model sees over the training run, so it is fully aligned with my interests, not some corporations.<p>I was thinking about for some time and come up with two interesting ideas I thought worth pursuing: MoE with a lot of experts that gets added and pruned from the model while it trains, where only a small subset of of experts are actually in use at any particular moment + batch 1 training on the single continuous stream of data.<p>First allows us to be bounded only by the disk space in terms of number of parameters and load and unload experts only when they are needed. The second (if figured out and it turns out to be doable) allows us to get aways with small VRAM capacity because we do not need to store big randomized batches and their respective gradients.<p>I started brainstorming with Claude and after some time we found an approach that seems to be promising, and low and behold, a few weeks pass and you can see the results yourself.<p>Obviously, I did use AI in the process of making this project and I am pretty sure it would be completely impossible for me to do something like this without it, so I hope it is more than justified.<p>The model is still running over the first of 7.8B characters corpus I selected for training, so the weights are not out yet, and it's about a couple weeks of waiting until they are cooked at the current reading speed. And yeah, the model just read continuous interleaved passages from the dataset, each by 32K characters long each as a single stream. Just as you or I would do.<p>The set up seems to be really simple so you can git clone the project, run it and observe everything for yourself.<p>Thanks for your attention.

Show HN: Rubrol – Sub-10ms PDF engine using Typst instead of Headless Chrome

Show HN: Three genlocked RP2350B make a console – 3k sprite pixels per line)

Show HN: I created an open source locally usable full fledged AI platform

hi to all the readers this post is for my recent opensource project called ENZO<p>now answering what is enzo so enzo is an opensource platform where i clubbed all the free available api for anyone use under one hood with more than 2000 models available to use for chatting coding researching and much more now answering the most common question of why you should put your time looking the project so it has few distinct feature meaning<p>it has a dedicated agents tab where you can describe your need and create a special agent just for one specific task with master ability in that domain<p>second it has the ability to connect your gmail drive and calendar and then you can ask it to perform some specific tasks like reading you the most important mail of the day or finding recruiter mails and creating personalized reply based on your data which it stores locally on your device<p>third the coding mode offers a dedicated preview window where you can see your code running and have a look of it feels and edit it in realtime as well as all the modes are packed with dedicated skills which delivers promising results<p>fourth the ui features some additional things such as music tab where you can listen to any music want and it has a custom personalized feature which runs in background and an llm understands your taste and recommends similar kind of music you like<p>fifth the most important why your trust it with your api key then to explain i would say enzo a dedicated vault which manages all your api and to secure it the vault as aes 256 bit encryption which prevents any person or any middle man to look at your api key and since the whole program runs locally on your device you have complete freedom to oversee all the backend work happening and it also features password lock which if you enable saves a backup key and then locks your whole platform work behind a pass screen though it is not foolproof as any third party or malware containing extension can still fetch login tokens from your browser so its security also depends upon how you access it concluding all of it.<p>i urge to anyone who reads this to have a look at the platform even if you hate it just curse it in the comment its fine or if you would like to drop any feedback i would highly encourage that and since its my first work open source platform i know it has a lot of errors and bugs so i apologize upfront for it and if you consider my work worthy please drop a star on the repo that'll make my day

Show HN: A competition for small neural networks that play strategy games

Show HN: A competition for small neural networks that play strategy games

Show HN: A competition for small neural networks that play strategy games

Show HN: Sigabrt.dev – cronjob monitor with an SSH TUI

Hello HN. I built this mostly to monitor the things I host myself. I know it's nothing too exciting.<p>Anyway, the TL;DR is: Create an endpoint, and if your script/cronjob fails to regularly ping it, you get notified (by email or ntfy). E.g.:<p><pre><code> 0 * * * * ./script.sh && curl -fsS https://sigabrt.dev/pulse/<id>/beat </code></pre> It also has an SSH TUI which is currently experimental and read-only, mostly because I'm not sure whether it is actually useful or just a gimmick :):<p><pre><code> ssh sigabrt.dev </code></pre> To use it, simply add your SSH public key in your account settings.<p>Yes, there are services like this already, and this is minimalistic by comparison. Feedback is welcome.

Show HN: Sigabrt.dev – cronjob monitor with an SSH TUI

Hello HN. I built this mostly to monitor the things I host myself. I know it's nothing too exciting.<p>Anyway, the TL;DR is: Create an endpoint, and if your script/cronjob fails to regularly ping it, you get notified (by email or ntfy). E.g.:<p><pre><code> 0 * * * * ./script.sh && curl -fsS https://sigabrt.dev/pulse/<id>/beat </code></pre> It also has an SSH TUI which is currently experimental and read-only, mostly because I'm not sure whether it is actually useful or just a gimmick :):<p><pre><code> ssh sigabrt.dev </code></pre> To use it, simply add your SSH public key in your account settings.<p>Yes, there are services like this already, and this is minimalistic by comparison. Feedback is welcome.