The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Reflota – Open-source data recovery for NTFS/FAT disks with phased copy
Show HN: I built a Rick and Morty explainer video generator
> paste any site or article or a script
> we turn it into a Rick and Morty sketch with dialogues, scenes, action and Rick and Morty of course
> click generate to turn the script into a storyboard you can review
> click generate to get a final video (this part is paid as it uses AI video under the hood)
Show HN: RealtimeVAD – trading false positives for 110ms earlier end-of-speech
Hey guys. I recently watched the latency reports for my local voice assistant, which show a waveform plus silero probability for voice activity. I noticed silero lags visibly on the speech off signal.<p>There had to be a method to get this signal closer to what I felt is optimal. My idea was to use a 10 ms energy detector with a threshold that adapts to environment or room noise. Which worked amazingly well. It created more false positives but in my speculative generation pipeline that would not matter. If it's wrong and the user continues speaking I can abort the pipeline fast.<p>I benchmarked my solution over ~3200 turns from my assistant logs and it came out ~110 ms faster initially. Still 100 ms now after some a fix which was needed for quiet speech.<p>I made it public if you want to have a look. If you measure it on your data I really appreciate any feedback. There's a benchmark script in the repo.<p>Thx for reading, have a good day folks.
Show HN: Apron Chat Protocol
Hi HN friends - I've been obsessed with chat protocols for ages - one previous project is ssh-chat (<a href="https://ssh.chat/" rel="nofollow">https://ssh.chat/</a>)<p>I come bringing a new kind of chat protocol that I've been working on since I spent quite a lot of time exploring the 14 other competing standards we've come up with in the past few decades.<p>The protocol details are here: <a href="https://apron.chat" rel="nofollow">https://apron.chat</a><p>I'm hoping that Apron will better reflect our needs beyond 2026, which I expect will move more and more toward semi-private chats rather than global social networks.<p>Come say hi on the live public demo: <a href="https://web.apron.chat" rel="nofollow">https://web.apron.chat</a><p>Some fun facts:<p>- The demo server runs in a Cloudflare Durable Worker. It's quite efficient, since it's hibernated except for when a message comes in. Easy to self-host this in the free tier.<p>- Despite the JSON protocol being intentionally unabbreviated ("message" instead of "msg", etc), a typical Apron session still uses almost half as much bandwidth and messages compared to an equivalent Matrix or XMTP session. This is largely because Apron minimizes the number of roundtrips and state required for common chat scenarios. Also it compresses very nicely!<p>- The official reference client is a PWA (Apron Web), we also have a TUI, macOS, and an iOS implementation by third party Apron-enjoyers. There's two reference server implementations (Cloudflare worker in TS and a Go implementation), plus a bunch of minimal implementations.<p>- My favourite fun capability is embedding streaming messages, great for LLM outputs. This can be done just by piping to a curl command, no additional dependencies. There's also a mode for streaming a terminal (by piping something like `script` from util-linux which handles row/col/baud flushing, no special dependencies either). See section 4.8 for details: <a href="https://apron.chat/protocol#48-embeds-and-avatars" rel="nofollow">https://apron.chat/protocol#48-embeds-and-avatars</a>
Show HN: Kapso – Iterative learning from enterprise data
Hey HN! We're Alireza and Majid building Leeroo (<a href="https://leeroo.com/">https://leeroo.com/</a>). Previously we’ve built AI systems for products with millions of users, including at LinkedIn and Meta. We’re now focusing on building AI systems for enterprise data. Today, we're open-sourcing Kapso (<a href="https://github.com/Leeroo-AI/kapso" rel="nofollow">https://github.com/Leeroo-AI/kapso</a>), an engine that runs experiments, deploys the best solution, and learns from every run.<p>The nature of data work is iterative: investigate, form hypotheses, experiment, and improve based on what you learn. This bottom-up process is the only way to deliver meaningful outcomes quickly in large, messy enterprise data environments. Consultants gathering context in meetings or teams defining a business ontology upfront to fit into the data has no chance of execution, in quarters and sometimes years. An AI-native system should automate this process, compounding learning to each new investigation and implementation.<p>For instance, one of our enterprise projects was moving a company off its old data estate onto a lakehouse with more than 30 source-system families, thousands of tables and transformations, and hundreds of BI reports. No individual or AI agent knew how that data estate was connected and operated. The only way to learn it was to investigate the company's repos, dbt models, documentation, and data itself and propose hypotheses about the estate, run experiments, and learn from them, until we understand how each system behaves and under which conditions.<p>The iterative work goes beyond understanding how data and systems fit together, and the business operations. Building useful software is iterative too. Whether you’re optimizing a warehouse, building a lakehouse pipeline or predicting churn, you need to run experiments and learn what works best in that environment. As you deliver more projects, that know-how compounds, similar in principle to RL. RAGing documentation has little value until it has been tried in a specific environment and the consequences are learned. As with human engineering work, experimentation and learning should happen in a sandbox.<p>We built Kapso for this kind of work, you give it an objective and success criteria, and it works towards an optimum solution that can be deployed on your stack.<p>You can view a video walkthrough here: <a href="https://www.youtube.com/watch?v=W4FHlza1fRs" rel="nofollow">https://www.youtube.com/watch?v=W4FHlza1fRs</a><p>Here’s how it works…<p>Kapso has a bank of lessons that are induced or deduced from the past experiments, and each carries a credence score, conditions on where it applies, and the evidence behind it. For each new experiment, an ideation module proposes ideas considering the earlier lessons, re-ranks them by expected return against the budget each would use, and sends the top-ranked ones to the implementor. Because the bank keeps learning from the past experiments in that environment, the proposed ideas are better suited to it than generic hypotheses from an LLM or RAG. Also, results of each experiment are verified by a verifier module that no experiment can access and cheat. As we’ve found when building such systems, sometimes the success criteria and evaluation tests need to be evolved. The experimentation modules can propose updates to the metrics and criteria for the verifier to approve or reject.<p>After each run, learn() updates the bank of lessons’ scores and boundaries using the evidence from that run. Changes to the bank can also be A/B tested by running otherwise identical builds with the previous and updated banks. The bank is built on git, so you can share your experience with others, list the banks available to you, and update them (`kapso experience share/list/update` in CLI). The system is model-agnostic and coding-agent-agnostic.<p>kapso.deploy(solution, target_stack) follows the same workflow, runs a shorter experimentation loop to adapt the best solution to the target stack (e.g. Docker, Modal, BentoCloud, LangGraph). Every domain has a harness to start from, you can write your own domain harness with /kapso:harness in the plugin or through CLI (kapso harness new <name>), and compound over that by running experimentations.<p>We ran it on some public benchmarks and live competitions, with the following results…<p>- Predictive modelling on relational databases: first on RelBench, Kumo AI (now Nvidia) and Stanford's benchmark, ahead of the best tabular foundation model (<a href="https://star-project.stanford.edu/relbench/leaderboard/" rel="nofollow">https://star-project.stanford.edu/relbench/leaderboard/</a>). This shows that keeping the learning in the harness-level can beat foundational table models. Updating a bank of lessons costs far less than retraining a tabular foundation model.<p>- ML Engineering: First on OpenAI's MLE-bench at submission, still among the top open-source systems (<a href="https://github.com/openai/mle-bench/pull/107" rel="nofollow">https://github.com/openai/mle-bench/pull/107</a>).<p>- AI competitions: ahead of the best agent the ALE-Bench authors at Sakana AI reported, and running autonomously at the live International Olympiad in AI 2026, above all 471 human contestants and in the top three AI systems (<a href="https://ioai-official.org/ai-models-track/ioai²-2026-report/" rel="nofollow">https://ioai-official.org/ai-models-track/ioai²-2026-re...</a>).<p>We have a public bank for ML and Data at Leeroopedia (<a href="https://leeroopedia.com/" rel="nofollow">https://leeroopedia.com/</a>): around 100k pages extracted by experimenting on papers and repos, about 6M requests a month, about 28% from AI agents.<p>If you are at a growing company with a complex data estate across multiple systems and functions, and your data and AI team is stretched, we would love to help!<p><a href="https://github.com/Leeroo-AI/kapso" rel="nofollow">https://github.com/Leeroo-AI/kapso</a>
Show HN: Build a PC in 3D in the Browser
I've been building this over the past few years. I left my job working on Google Earth to make a 3D PC builder. We have quite a few users so it's able to fund our team but I realized I never shared on HN!
Show HN: Isometric SF – A pixel-art map of the SF Bay Area
Technical Write-up: <a href="https://www.justinchavez.com/blog/isometric-sf-how/" rel="nofollow">https://www.justinchavez.com/blog/isometric-sf-how/</a><p>Hi HN! Showcasing this fun project. I took 1300 square miles of the SF Bay Area from Google Photorealistic 3D Tiles and transformed them into a cohesive pixel map. It's really fun to explore and stumble upon various famous landmarks like Apple Park, Palace of Fine Arts, or UC Berkeley.<p>The technicals involve using Nano Banana 2 to train a Qwen-Image-Edit 2511 LoRA which is used to prototype the style and generation methods. For final map I used Qwen to train a Klein 4B LoRA which reproduced the style at 1/4 of the cost. More details in the blog post.<p>Let me know what you think! I really enjoyed this process of fine-tuning smaller open source models.<p>If I did the map with Nano Banana 2 it would have cost around 7k USD!<p>Credit for the idea goes to Isometric NYC.
Show HN: AgentRun – open-source, Self-improving Managed Agents
We (GREP.AI, YC F26) are open-sourcing AgentRun and turning it into a full managed agents platform. Read more about it in this post: <a href="https://agentrun.ai/blog/introducing-agentrun-run-smarter-agents-use-fewer-tokens" rel="nofollow">https://agentrun.ai/blog/introducing-agentrun-run-smarter-ag...</a>.<p>It includes what we originally built for our own agent harness:<p>- Self-improving agents
- Web-grounded workflows
- A range of sandboxes and computer environments
- Thousands of tools and integrations
- Detailed analytics on how your agents behave<p>You can bring your own agent. AgentRun works with Pi, Claude Agents, Codex, OpenCode, and any other harness that supports ACP.<p>You can self-host it (see 1-click install on Render) with an OpenRouter key, or try the hosted version at console.agentrun.ai.<p>We’d love feedback, especially on the architecture (see README), or anything we may be missing to make it useful to you.
Show HN: AgentRun – open-source, Self-improving Managed Agents
We (GREP.AI, YC F26) are open-sourcing AgentRun and turning it into a full managed agents platform. Read more about it in this post: <a href="https://agentrun.ai/blog/introducing-agentrun-run-smarter-agents-use-fewer-tokens" rel="nofollow">https://agentrun.ai/blog/introducing-agentrun-run-smarter-ag...</a>.<p>It includes what we originally built for our own agent harness:<p>- Self-improving agents
- Web-grounded workflows
- A range of sandboxes and computer environments
- Thousands of tools and integrations
- Detailed analytics on how your agents behave<p>You can bring your own agent. AgentRun works with Pi, Claude Agents, Codex, OpenCode, and any other harness that supports ACP.<p>You can self-host it (see 1-click install on Render) with an OpenRouter key, or try the hosted version at console.agentrun.ai.<p>We’d love feedback, especially on the architecture (see README), or anything we may be missing to make it useful to you.
Show HN: Klad, a paint app your coding agent can drive (source-available)
I've been developing applications for a long time and have turned to game development as well lately. One of the things that frustrated me the most was that using AI agents for game development is very annoying in the art department. Your only options right now are to either use an image genAI model and roll the dice every time you want to make an edit, or have your agent draw with something like vectors.<p>I was looking for an application that would allow me to ask an agent to make a first draft of whatever art I needed with actual brushes, and then be able to go in myself and edit every last stroke or layer that it created.<p>Klad is that application. Written in Rust, it can be fully controlled by an agent through the CLI, and every function is available in a nice UI as well: a brush engine, frame-by-frame animation (and bones/IK), vectors, text layers, colours, layer effects, infinite undo (even after re-opening the file!),…<p>Why I think it's useful for a wider audience: Adobe showed agents editing Photoshop files at MAX this week, with a private beta planned for early 2027.<p>Klad is built on the same core idea, with editable paint strokes as core architecture, and it works today.<p>Clone the repo to try it out: <a href="https://github.com/klad-app/klad" rel="nofollow">https://github.com/klad-app/klad</a><p>You can basically point Claude to the repo, ask it to build it, and pick up the drawing skills. Then ask it to make any drawing you want! (After that, you or the agent can change absolutely every brush stroke in the app again, which makes edits a breeze.)<p>The quality of the drawings really depends on the agent you use. I have tried it with Claude Fable 5.2 since it came out on Monday, and the drawings are already quite impressive. I'm confident that this will only improve.<p>I use it every day on Mac, so that definitely works well. It builds on Linux too (tablet pen pressure still needs some work there). Windows builds in CI, I haven't used it there myself yet but I have some reports that it works perfectly fine.<p>Coming up next is support for pixel art. I'm in the testing phase with this right now, and I expect it to go live in the repo sometime this weekend.<p>Klad is source-available rather than open source: free to build and use for individuals and small companies, and I plan to sell pre-built binaries later, the way Aseprite does. The file format, CLI reference and agent skill are MIT.<p>What I want to hear from people who try it: Can your agent work out the CLI without help, and where does it get confused? Can you go in and change one of its strokes easily?<p>The command reference and guides are available at <a href="https://klad.studio" rel="nofollow">https://klad.studio</a><p>I'll be here answering any questions about the technical side as well, as it's a pretty interesting challenge to build a performant engine with editable brush strokes that render as paint.
Show HN: Fable52.games – Play and inspect Fable 5.2 single-file HTML games
Show HN: Aircad – Professional drafting and 3D modeling in the browser
Aircad is a professional CAD system running in the browser with deep AI integration. It includes professional grade drafting and documentation tools, NURBS-based solid modeling, and a lot more. You can make professional quality design documents with it. It is all integrated behind a scripting API that can do anything you can do in the interface and also perform complex modeling operations efficiently. It reads and writes file formats like DXF, DWG, STEP, and 3DM.<p>I built Aircad because I was frustrated with what was out there. Current CAD tools are expensive and slow to improve. New features barely move the needle. With Aircad, you have fast, powerful geometry tools for just about anything, from conceptual models to your final design.<p>The newer generation of web-based CAD isn’t comprehensive enough to complete a project or is slower than existing desktop products because it sends performance-sensitive work to a server (OnShape), or isn’t designed to scale to real work in a collaborative setting (many "toy" CAD tools).<p>Current CAD collaboration workflows are fragile, costly, and complex. They’re file-based and separate the design artifacts (eg PDFs) from the source. Collaboration is often an expensive add-on that raises your subscription, is hard to learn, and feels unnatural. Aircad builds it in: version history, drawing comparison, forking, and realtime collaboration.<p>AI in most CAD tools is tacked on as a plug-in instead of being deeply integrated, and those plug-ins are often inefficient, hard to adopt, and costly. Aircad has a built-in AI agent and a comprehensive scripting API with a stage-and-commit workflow, so you know exactly what is happening during a complex change. You can use AI to write new scripts and commands that extend Aircad with repeatable, shareable workflows.
Show HN: My $300 embroidery machine needed a subscription, so I built my own app
Show HN: My $300 embroidery machine needed a subscription, so I built my own app
Show HN: Godbolt-in-a-floating-window for Neovim (C, C++, Rust)
No internet connection needed. Uses a locally-installed compiler for assembly generation.
Show HN: Godbolt-in-a-floating-window for Neovim (C, C++, Rust)
No internet connection needed. Uses a locally-installed compiler for assembly generation.
Show HN: Black Ops II statically recompiled to native ARM64 macOS
I grew up with Black Ops 2 and wanted to see if I could make it work on my 2020 MacBook Pro. So I used Claude Opus 5.5 to create a tool that converts your copy of the game (from Steam) to a native ARM64 Mac app, with almost 60fps in all areas. The whole game is statically recompilated ahead-of-time (AOT) and does not use any emulator whatsoever, be it Wine, Crossover or Rosetta. The best thing is the performance, although some heat issues are still to fix. You get to play the whole Campaign, local Multiplayer (with bots) and local Zombies (all DLCs maps; you need to have them in your Steam library obviously) at about 60fps. Split-screen was not in the PC version, but I've been working on it lately; it's coming soon! LAN mode is on the roadmap for later too :)
Show HN: Black Ops II statically recompiled to native ARM64 macOS
I grew up with Black Ops 2 and wanted to see if I could make it work on my 2020 MacBook Pro. So I used Claude Opus 5.5 to create a tool that converts your copy of the game (from Steam) to a native ARM64 Mac app, with almost 60fps in all areas. The whole game is statically recompilated ahead-of-time (AOT) and does not use any emulator whatsoever, be it Wine, Crossover or Rosetta. The best thing is the performance, although some heat issues are still to fix. You get to play the whole Campaign, local Multiplayer (with bots) and local Zombies (all DLCs maps; you need to have them in your Steam library obviously) at about 60fps. Split-screen was not in the PC version, but I've been working on it lately; it's coming soon! LAN mode is on the roadmap for later too :)
Show HN: I recreated Papers Please but you're deciding which YC startups to fund
Show HN: I recreated Papers Please but you're deciding which YC startups to fund