The best Hacker News stories from Show from the past day
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Show HN: Treepeat – Code similarity detection using Tree-sitter
treepeat is a tool that finds similarities in your codebase.<p>Find duplicate code blocks meaningful to the language (classes/functions), not just lines.<p>Find near-duplicates: ignore whitespace, strings, high level AST nodes such as function and names.<p>Find structurally similar code: anonymize identifiers, constants, etc.<p>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.<p>Languages supported: astro, bash, css, go, html, javascript, lua, markdown (plus codeblocks), python, sql, typescript, java, kotlin, rust, yaml
Show HN: An open-source manufacturing ERP/MES/QMS
Show HN: How long do I need to work at my salary before I can coast, or retire?
I made a financial planning tool in a couple hours that can model various stages of life and potentially guide job searching and work timelines.
Show HN: Koi.rest – watch some fish and regain your balance
Hi there. My name is Paul. I’m a developer, an explorer and I’ve got ADHD. As of the beginning of August, I’m also unemployed.<p>This last part, along with everything else this past year, has caused me a great deal of stress.<p>While our balcony zen garden project is yet to be completed, I had an idea to create a virtual one that everyone can use.<p>It's an idea that, unfortunately, I’ve been postponing for a while now, mostly because I have no fucking idea how to do it as I don’t know JavaScript, and I don't have the capacity to learn it right now.<p>So I let perfect be the enemy of good and, well... just kept the idea to myself.<p>Then I said "fuck it" and used AI to make the thing I really wanted to make.<p>I realized I didn't want "perfect". I wanted "good enough".<p>I tweaked, added, removed, drew, researched, questioned, tested... I just wasn't the one coding it.<p>So now, instead of occupying my brain, it now lives on the internet for others to enjoy.<p>Yes, there’s something noble about making something entirely on your own, but what good is an idea that just sits in my head?<p>So here I am. I made the thing. The weird, little, quiet koi pond.<p>The silly project of passion. The little corner of the internet to let strangers watch fish quietly, together.<p>I hope this pond helps you as much as it helped me.
Show HN: Whiteboard (YC W26) – An open-source IDE for thoughtful software design
Hello! We’re Sid, Alex, Ketan, and Milan. We’re building Whiteboard (<a href="https://whiteboard.dev.fast/">https://whiteboard.dev.fast/</a>), an open-source desktop app where humans and agents can architect software together in a common workspace. Here’s our repo: <a href="https://github.com/devdotfast/whiteboard" rel="nofollow">https://github.com/devdotfast/whiteboard</a>.<p>We were missing the feeling of a “whiteboard session” with another dev where you leave with a deep understanding of a system, so we built this app for ourselves. Whiteboard plugs into the tools you already use - e.g. Claude Code, Codex, etc. – and gives your agent an SDK to draw on an in-app canvas to describe its work. We began with an MVP based on HTML artifacts and started rethinking the app as we ran into limitations:<p>1. Built on top of CodeOSS: We found that in pure HTML tools it was hard to connect a spec or diagram to code. In Whiteboard, when you click on visualizations like a sequence diagram, an entity relationship diagram, or a quote from the agent’s trace, you can jump to the underlying code directly. When navigating code, you get keybindings and LSP support from VSCode out of the box. We’ve found this is especially valuable because tradeoffs are often only discovered after a first pass at implementation (re: slop)<p>2. Semantic diff viewer: we wrote a semantic, AST-aware diff viewer in Rust so you can only view the code changes which are relevant to you [1]. We’ve set up some sane defaults: large added functions are summarized as pseudocode, and things like unit tests and large documentation changes are collapsed / hidden. This is all customizable with a WASM-based plugin system.<p>3. Decision Log: We found it difficult to reason about what set of decisions our agents made autonomously. So we built tools for agents to query and link their own traces to the Whiteboard, so you can understand how the requirements that you set were implemented, and understand what decisions your agent made autonomously.<p>Here’s a quick demo video explaining more: <a href="https://www.youtube.com/watch?v=ChPn3ftULWE" rel="nofollow">https://www.youtube.com/watch?v=ChPn3ftULWE</a><p>Folks at companies like Salesforce and Modal are using Whiteboard today as a review tool for architecture or spec-level changes – really any change where they want to be involved:<p>1. Reviewing your own coding agent’s work: because Whiteboard makes it easier to review large amounts of code, folks will typically have their AI agents create a prototype and a corresponding Whiteboard session so they can iterate on the design.<p>2. Reviewing other people’s changes: We’ve found that Whiteboard is particularly helpful when composed with tools like Greptile. For example, you can run an automated code reviewer on small changes and escalate to a Whiteboard session for the changes that require human judgement.<p>Why we built this: we’re four buddies from college who quit our jobs as tech leads right before agentic coding became industry standard. As we iterated towards an MVP for a previous idea, we struggled to maintain a comprehensible codebase while reaping all the velocity benefits of agentic coding. As more PRs were merged without our understanding, we felt a ‘cognitive debt’ begin to seep in, until it became difficult for us to even contribute to the system [2].<p>We’re releasing our desktop app under an MIT license. Please poke through and feel free to contribute! Eventually we’ll charge companies for a hosted web version that manages whiteboard session creation alongside features like trajectory storage and multiplayer reviews. Everything will always remain self-hostable.<p>Thanks for reading, and we hope you try it out! We would love to hear any feedback and to learn from your expertise.<p>Here’s are the project links again: <a href="https://github.com/devdotfast/whiteboard" rel="nofollow">https://github.com/devdotfast/whiteboard</a>, and you can install (for MacOS + Linux) at <a href="https://install.dev.fast">https://install.dev.fast</a><p>[1] diffs library: <a href="https://github.com/devdotfast/diffr" rel="nofollow">https://github.com/devdotfast/diffr</a>
[2] Credit for the term ‘cognitive debt’ goes to <a href="https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck" rel="nofollow">https://www.geoffreylitt.com/2026/07/02/understanding-is-the...</a>
Show HN: Make cursed fonts like Times New Bastard
A joke tool that abuses OpenType's ligature feature to mix fonts. It works pretty fast on client-side by loading Python in WASM.
Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes
Hello HN!<p>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.<p>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.<p>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.<p>If you want to try it out you can sign up and get a free beta license here: <a href="https://www.forensicdbg.com/beta" rel="nofollow">https://www.forensicdbg.com/beta</a>
Show HN: RxFilm Studio–Create and edit your product videos with AI agent
Hi HN,<p>I built RxFilm Studio, a video workspace that lets you create and revise videos with AI agent.<p>The reason I started this project is simple: I’ve noticed that many independent developers and small teams want to create marketing videos for their product, but producing these videos requires a lot of time and effort — from editing and visual effects to voiceovers and subtitles.<p>So I built Rxfilm Studio to simplify the entire video creation process. Start with an idea, a product website, images, or existing footage, and then generate a complete video with:
- Story and structure
- Editing and timeline
- Visual and sound effects
- Voiceovers, background music
- Subtitles and translation<p>Github: <a href="https://github.com/rxtech-lab/film-workflow" rel="nofollow">https://github.com/rxtech-lab/film-workflow</a><p>Here's a demo marketing video created using RxFilm Studio:
<a href="https://youtu.be/9_M15fRdZQE" rel="nofollow">https://youtu.be/9_M15fRdZQE</a><p>Welcome to try it out and I would love to hear your feedback!
Show HN: Ive Sent It – online courier for files, with signed proof of delivery
Show HN: An atlas of system designs with interactive architecture diagrams
Show HN: AI·rete·RAG – a Rete rule engine decides, RAG explains why
Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact.<p>It runs the two in series instead:<p>1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes only from here. Same facts, same verdict, every time, with salience-based conflict resolution.
2. RAG retrieves passages from your own policy documents, and an LLM writes a plain-English explanation of the decision that was already made, citing those passages. It can't change the verdict.<p>A few things that went further than I expected:
- Rules are a graph, not flat lists: nested all/any/not, and rules can assert facts that other rules consume (forward chaining). The decision trace shows the causal chain.
- Audit mode records every rule evaluated, including the ones that didn't fire, condition by condition, with a snapshot of the rule set for replay.
- Rules can steer retrieval (a fired rule narrows which documents get searched), and retrieved text can be turned into facts for the engine.
- Non-technical authors can build rules in a visual editor, or paste a policy document and get LLM-drafted rules with citations. Drafts are never saved without review. YAML is still there for engineers.<p>The landing page has a live demo with no signup (8 demo domains: loan, fraud, clinical, insurance, legal, ops, e-commerce, blockchain). There's also an MCP server, so Claude and other agents can call /decide as a tool: `uvx ai-rete-rag-mcp`.<p>To be upfront: it's a hosted product with a free tier. The MCP client is open source (MIT, github.com/zaharajabeen13-create/ai-rete-rag-mcp); the engine and platform are not open source right now.<p>I'd especially like to hear from anyone who has had to explain an automated decision to a regulator or an auditor: what did they actually ask for?
Show HN: Npunlock – Run custom C kernels for Intel NPUs
Show HN: Npunlock – Run custom C kernels for Intel NPUs
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: 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: 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: 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: 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.