The best Hacker News stories from Show from the past day
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Show HN: My self-written hobby OS is finally running on my vintage IBM ThinkPad
Finally got my hobby OS up and running on real hardware. I love the old IBM thinkpads, so thought it was the perfect machine to get it working on. Been working on it for quite some time now, but this has been a big milestone!
Show HN: Zev – Remember (or discover) terminal commands
Show HN: Faasta – A self-hosted Serverless platform for WASM-wasi-HTTP in Rust
I've just released an early version off my project I've been working on for a few months now and would love some feedback.<p><a href="https://github.com/fourlexboehm/faasta">https://github.com/fourlexboehm/faasta</a><p>I was surprised there isn't yet an open/source and standards compliant way to host wasi-http functions in a way that takes advantages of WASM, a multi tenanted application.<p>If you're not familiar with wasi, Compared to something like AWS Lambda, this approach is much more efficient as a single process can serve 1000s of function invocations concurrently and asynchronously, instead requiring an entire VM.<p>This is still early days for the project, but feel free to download the cli utility with cargo install cargo-faasta.<p>Feel free to test deploying functions on my hosted instance at <a href="https://website.faasta.xyz" rel="nofollow">https://website.faasta.xyz</a>.<p>The service is free to use and currently supports deployments via GitHub OAuth, with a limit of 10 functions per GitHub account.
Show HN: Faasta – A self-hosted Serverless platform for WASM-wasi-HTTP in Rust
I've just released an early version off my project I've been working on for a few months now and would love some feedback.<p><a href="https://github.com/fourlexboehm/faasta">https://github.com/fourlexboehm/faasta</a><p>I was surprised there isn't yet an open/source and standards compliant way to host wasi-http functions in a way that takes advantages of WASM, a multi tenanted application.<p>If you're not familiar with wasi, Compared to something like AWS Lambda, this approach is much more efficient as a single process can serve 1000s of function invocations concurrently and asynchronously, instead requiring an entire VM.<p>This is still early days for the project, but feel free to download the cli utility with cargo install cargo-faasta.<p>Feel free to test deploying functions on my hosted instance at <a href="https://website.faasta.xyz" rel="nofollow">https://website.faasta.xyz</a>.<p>The service is free to use and currently supports deployments via GitHub OAuth, with a limit of 10 functions per GitHub account.
Show HN: BugStalker - a modern Rust debugger
Show HN: BugStalker - a modern Rust debugger
Show HN: Formalizing Principia Mathematica using Lean
This project aims to formalize the first volume of Prof. Bertrand Russell’s Principia Mathematica using the Lean theorem prover. Throughout the formalization, I tried to rigorously follow Prof. Russell’s proof, with no or little added statements from my side, which were only necessary for the formalization but not the logical argument. Should you notice any inaccuracy (even if it does not necessarily falsify the proof), please let me know as I would like to proceed with the same spirit of rigour. Before starting this project, I had already found Prof. Elkind’s formalization of the Principia using Rocq (formerly Coq), which is much mature work than this one. However, I still thought it would be fun to do it using Lean4.<p><a href="https://ndrwnaguib.com/principia/" rel="nofollow">https://ndrwnaguib.com/principia/</a><p><a href="https://github.com/ndrwnaguib/principia">https://github.com/ndrwnaguib/principia</a>
Show HN: Formalizing Principia Mathematica using Lean
This project aims to formalize the first volume of Prof. Bertrand Russell’s Principia Mathematica using the Lean theorem prover. Throughout the formalization, I tried to rigorously follow Prof. Russell’s proof, with no or little added statements from my side, which were only necessary for the formalization but not the logical argument. Should you notice any inaccuracy (even if it does not necessarily falsify the proof), please let me know as I would like to proceed with the same spirit of rigour. Before starting this project, I had already found Prof. Elkind’s formalization of the Principia using Rocq (formerly Coq), which is much mature work than this one. However, I still thought it would be fun to do it using Lean4.<p><a href="https://ndrwnaguib.com/principia/" rel="nofollow">https://ndrwnaguib.com/principia/</a><p><a href="https://github.com/ndrwnaguib/principia">https://github.com/ndrwnaguib/principia</a>
Show HN: Formalizing Principia Mathematica using Lean
This project aims to formalize the first volume of Prof. Bertrand Russell’s Principia Mathematica using the Lean theorem prover. Throughout the formalization, I tried to rigorously follow Prof. Russell’s proof, with no or little added statements from my side, which were only necessary for the formalization but not the logical argument. Should you notice any inaccuracy (even if it does not necessarily falsify the proof), please let me know as I would like to proceed with the same spirit of rigour. Before starting this project, I had already found Prof. Elkind’s formalization of the Principia using Rocq (formerly Coq), which is much mature work than this one. However, I still thought it would be fun to do it using Lean4.<p><a href="https://ndrwnaguib.com/principia/" rel="nofollow">https://ndrwnaguib.com/principia/</a><p><a href="https://github.com/ndrwnaguib/principia">https://github.com/ndrwnaguib/principia</a>
Show HN: Magnitude – open-source, AI-native test framework for web apps
Hey HN, Anders and Tom here - we’ve been building an end-to-end testing framework powered by visual LLM agents to replace traditional web testing.<p>We know there's a lot of noise about different browser agents. If you've tried any of them, you know they're slow, expensive, and inconsistent. That's why we built an agent specifically for running test cases and optimized it just for that:<p>- Pure vision instead of error prone "set-of-marks" system (the colorful boxes you see in browser-use for example)<p>- Use tiny VLM (Moondream) instead of OpenAI/Anthropic computer use for dramatically faster and cheaper execution<p>- Use two agents: one for planning and adapting test cases and one for executing them quickly and consistently.<p>The idea is the planner builds up a general plan which the executor runs. We can save this plan and re-run it with only the executor for quick, cheap, and consistent runs. When something goes wrong, it can kick back out to the planner agent and re-adjust the test.<p>It’s completely open source. Would love to have more people try it out and tell us how we can make it great.<p>Repo: <a href="https://github.com/magnitudedev/magnitude">https://github.com/magnitudedev/magnitude</a>
Show HN: Magnitude – open-source, AI-native test framework for web apps
Hey HN, Anders and Tom here - we’ve been building an end-to-end testing framework powered by visual LLM agents to replace traditional web testing.<p>We know there's a lot of noise about different browser agents. If you've tried any of them, you know they're slow, expensive, and inconsistent. That's why we built an agent specifically for running test cases and optimized it just for that:<p>- Pure vision instead of error prone "set-of-marks" system (the colorful boxes you see in browser-use for example)<p>- Use tiny VLM (Moondream) instead of OpenAI/Anthropic computer use for dramatically faster and cheaper execution<p>- Use two agents: one for planning and adapting test cases and one for executing them quickly and consistently.<p>The idea is the planner builds up a general plan which the executor runs. We can save this plan and re-run it with only the executor for quick, cheap, and consistent runs. When something goes wrong, it can kick back out to the planner agent and re-adjust the test.<p>It’s completely open source. Would love to have more people try it out and tell us how we can make it great.<p>Repo: <a href="https://github.com/magnitudedev/magnitude">https://github.com/magnitudedev/magnitude</a>
Show HN: I used OpenAI's new image API for a personalized coloring book service
I've had an idea for a long time to generate a cute coloring book based on family photos, send it to a printing service, and then deliver it to people.<p>Last month, when OpenAI's Sora was released for public use I (foolishly) thought I'd manually drag-and-drop each order’s photos into Sora's UI and copy the resulting images back into my system. This took way too much time (about an hour for each of the few books I made and tested with family and friends). It clearly wasn't possible to release this version because I’d be losing a huge amount of time on every order. So instead, I decided I'd finish off the project as best I could, put it "on ice," and wait for the API release.<p>The API is now released (quicker than I thought it'd be, too!) and I integrated it last night. I'd love your feedback on any and all aspects.<p>The market is mostly family-based, but from my testing of the physical book I've found that both adults and kids enjoy coloring them in (it's surprisingly cathartic and creative). If you would like to order one you can get 10% off by tapping the total price line item five times.
Show HN: I used OpenAI's new image API for a personalized coloring book service
I've had an idea for a long time to generate a cute coloring book based on family photos, send it to a printing service, and then deliver it to people.<p>Last month, when OpenAI's Sora was released for public use I (foolishly) thought I'd manually drag-and-drop each order’s photos into Sora's UI and copy the resulting images back into my system. This took way too much time (about an hour for each of the few books I made and tested with family and friends). It clearly wasn't possible to release this version because I’d be losing a huge amount of time on every order. So instead, I decided I'd finish off the project as best I could, put it "on ice," and wait for the API release.<p>The API is now released (quicker than I thought it'd be, too!) and I integrated it last night. I'd love your feedback on any and all aspects.<p>The market is mostly family-based, but from my testing of the physical book I've found that both adults and kids enjoy coloring them in (it's surprisingly cathartic and creative). If you would like to order one you can get 10% off by tapping the total price line item five times.
Show HN: I used OpenAI's new image API for a personalized coloring book service
I've had an idea for a long time to generate a cute coloring book based on family photos, send it to a printing service, and then deliver it to people.<p>Last month, when OpenAI's Sora was released for public use I (foolishly) thought I'd manually drag-and-drop each order’s photos into Sora's UI and copy the resulting images back into my system. This took way too much time (about an hour for each of the few books I made and tested with family and friends). It clearly wasn't possible to release this version because I’d be losing a huge amount of time on every order. So instead, I decided I'd finish off the project as best I could, put it "on ice," and wait for the API release.<p>The API is now released (quicker than I thought it'd be, too!) and I integrated it last night. I'd love your feedback on any and all aspects.<p>The market is mostly family-based, but from my testing of the physical book I've found that both adults and kids enjoy coloring them in (it's surprisingly cathartic and creative). If you would like to order one you can get 10% off by tapping the total price line item five times.
Show HN: Infat – Declaritive application assocation manager for macOS
Bello! Made this to help navigate the tumultuous process of navigating to a new machine on Mac when you have a number of custom utilities setup for editing particular files. This is designed to make that as easy as possible, and add some magic on top of that, like setting mailto handlers or anything else of that breed. Use XDG_CONFIG_HOME to keep it organized.<p>Credit to <a href="https://github.com/moretension/duti">https://github.com/moretension/duti</a> for the original inspiration for the project.<p>Happy to answer and help with whatever.
Show HN: Colanode, open-source and local-first Slack and Notion alternative
Hey HN,<p>I'm Hakan, the founder of Colanode (<a href="https://github.com/colanode/colanode">https://github.com/colanode/colanode</a>), an open-source, local-first collaboration app combining the best of Slack-style chats and Notion-style note-taking, fully self-hostable for complete data control. Here's a quick demo: <a href="https://www.youtube.com/watch?v=wp1hoSCEArg" rel="nofollow">https://www.youtube.com/watch?v=wp1hoSCEArg</a><p>As a heavy Notion user, I often found it tough to get my teams fully onboard since people naturally gravitate toward chat for quick interactions. Maintaining context between chat apps like Slack and documentation apps like Notion became increasingly frustrating. Switching contexts, losing track of information, and managing data across multiple tools created unnecessary friction.<p>This frustration led me to build Colanode, a single platform integrating structured notes and knowledge management with real-time chat. After building the first version, early feedback highlighted a critical issue: teams/organizations want full control over sensitive data, especially conversations. That's why I decided to open-source Colanode under an Apache 2.0 license, making it fully self-hostable so you can retain complete ownership and privacy over your data.<p>Colanode is built with simplicity and extensibility in mind, using only open-source tools and avoiding any vendor or cloud lock-in. It features a local-first architecture offering complete offline support. From a technical perspective, Colanode consists of a Node.js server API and an Electron desktop client, with mobile apps coming soon. Everything in Colanode is represented as a node (e.g., message, file, folder, chat, channel, database, record), each with specific attributes and permissions. All reads and writes performed by the desktop client happen locally within a SQLite database, and changes sync seamlessly via a synchronization engine built on top of SQLite, Postgres, and Yjs—a CRDT library for conflict resolution. The server then propagates these changes to other collaborators. You can self-host the server in any environment using Docker, Postgres, Redis, and any S3-compatible storage, and connect using the official desktop client, which supports simultaneous connections to multiple servers and accounts. This local-first approach also prepares us for future integrations with fully local LLMs, further enhancing privacy and performance.<p>I'd love your feedback and suggestions on Colanode. What features would you like to see? What would you change?<p>Thanks, looking forward to your thoughts!
Show HN: Colanode, open-source and local-first Slack and Notion alternative
Hey HN,<p>I'm Hakan, the founder of Colanode (<a href="https://github.com/colanode/colanode">https://github.com/colanode/colanode</a>), an open-source, local-first collaboration app combining the best of Slack-style chats and Notion-style note-taking, fully self-hostable for complete data control. Here's a quick demo: <a href="https://www.youtube.com/watch?v=wp1hoSCEArg" rel="nofollow">https://www.youtube.com/watch?v=wp1hoSCEArg</a><p>As a heavy Notion user, I often found it tough to get my teams fully onboard since people naturally gravitate toward chat for quick interactions. Maintaining context between chat apps like Slack and documentation apps like Notion became increasingly frustrating. Switching contexts, losing track of information, and managing data across multiple tools created unnecessary friction.<p>This frustration led me to build Colanode, a single platform integrating structured notes and knowledge management with real-time chat. After building the first version, early feedback highlighted a critical issue: teams/organizations want full control over sensitive data, especially conversations. That's why I decided to open-source Colanode under an Apache 2.0 license, making it fully self-hostable so you can retain complete ownership and privacy over your data.<p>Colanode is built with simplicity and extensibility in mind, using only open-source tools and avoiding any vendor or cloud lock-in. It features a local-first architecture offering complete offline support. From a technical perspective, Colanode consists of a Node.js server API and an Electron desktop client, with mobile apps coming soon. Everything in Colanode is represented as a node (e.g., message, file, folder, chat, channel, database, record), each with specific attributes and permissions. All reads and writes performed by the desktop client happen locally within a SQLite database, and changes sync seamlessly via a synchronization engine built on top of SQLite, Postgres, and Yjs—a CRDT library for conflict resolution. The server then propagates these changes to other collaborators. You can self-host the server in any environment using Docker, Postgres, Redis, and any S3-compatible storage, and connect using the official desktop client, which supports simultaneous connections to multiple servers and accounts. This local-first approach also prepares us for future integrations with fully local LLMs, further enhancing privacy and performance.<p>I'd love your feedback and suggestions on Colanode. What features would you like to see? What would you change?<p>Thanks, looking forward to your thoughts!
Show HN: Colanode, open-source and local-first Slack and Notion alternative
Hey HN,<p>I'm Hakan, the founder of Colanode (<a href="https://github.com/colanode/colanode">https://github.com/colanode/colanode</a>), an open-source, local-first collaboration app combining the best of Slack-style chats and Notion-style note-taking, fully self-hostable for complete data control. Here's a quick demo: <a href="https://www.youtube.com/watch?v=wp1hoSCEArg" rel="nofollow">https://www.youtube.com/watch?v=wp1hoSCEArg</a><p>As a heavy Notion user, I often found it tough to get my teams fully onboard since people naturally gravitate toward chat for quick interactions. Maintaining context between chat apps like Slack and documentation apps like Notion became increasingly frustrating. Switching contexts, losing track of information, and managing data across multiple tools created unnecessary friction.<p>This frustration led me to build Colanode, a single platform integrating structured notes and knowledge management with real-time chat. After building the first version, early feedback highlighted a critical issue: teams/organizations want full control over sensitive data, especially conversations. That's why I decided to open-source Colanode under an Apache 2.0 license, making it fully self-hostable so you can retain complete ownership and privacy over your data.<p>Colanode is built with simplicity and extensibility in mind, using only open-source tools and avoiding any vendor or cloud lock-in. It features a local-first architecture offering complete offline support. From a technical perspective, Colanode consists of a Node.js server API and an Electron desktop client, with mobile apps coming soon. Everything in Colanode is represented as a node (e.g., message, file, folder, chat, channel, database, record), each with specific attributes and permissions. All reads and writes performed by the desktop client happen locally within a SQLite database, and changes sync seamlessly via a synchronization engine built on top of SQLite, Postgres, and Yjs—a CRDT library for conflict resolution. The server then propagates these changes to other collaborators. You can self-host the server in any environment using Docker, Postgres, Redis, and any S3-compatible storage, and connect using the official desktop client, which supports simultaneous connections to multiple servers and accounts. This local-first approach also prepares us for future integrations with fully local LLMs, further enhancing privacy and performance.<p>I'd love your feedback and suggestions on Colanode. What features would you like to see? What would you change?<p>Thanks, looking forward to your thoughts!
Show HN: Lemon Slice Live – Have a video call with a transformer model
Hey HN, this is Lina, Andrew, and Sidney from Lemon Slice. We’ve trained a custom diffusion transformer (DiT) model that achieves video streaming at 25fps and wrapped it into a demo that allows anyone to turn a photo into a real-time, talking avatar. Here’s an example conversation from co-founder Andrew: <a href="https://www.youtube.com/watch?v=CeYp5xQMFZY" rel="nofollow">https://www.youtube.com/watch?v=CeYp5xQMFZY</a>. Try it for yourself at: <a href="https://lemonslice.com/live">https://lemonslice.com/live</a>.<p>(Btw, we used to be called Infinity AI and did a Show HN under that name last year: <a href="https://news.ycombinator.com/item?id=41467704">https://news.ycombinator.com/item?id=41467704</a>.)<p>Unlike existing avatar video chat platforms like HeyGen, Tolan, or Apple Memoji filters, we do not require training custom models, rigging a character ahead of time, or having a human drive the avatar. Our tech allows users to create and immediately video-call a custom character by uploading a single image. The character image can be any style - from photorealistic to cartoons, paintings, and more.<p>To achieve this demo, we had to do the following (among other things! but these were the hardest):<p>1. Training a fast DiT model. To make our video generation fast, we had to both design a model that made the right trade-offs between speed and quality, and use standard distillation approaches. We first trained a custom video diffusion transformer (DiT) from scratch that achieves excellent lip and facial expression sync to audio. To further optimize the model for speed, we applied teacher-student distillation. The distilled model achieves 25fps video generation at 256-px resolution. Purpose-built transformer ASICs will eventually allow us to stream our video model at 4k resolution.<p>2. Solving the infinite video problem. Most video DiT models (Sora, Runway, Kling) generate 5-second chunks. They can iteratively extend it by another 5sec by feeding the end of the 1st chunk into the start of the 2nd in an autoregressive manner. Unfortunately the models experience quality degradation after multiple extensions due to accumulation of generation errors. We developed a temporal consistency preservation technique that maintains visual coherence across long sequences. Our technique significantly reduces artifact accumulation and allows us to generate indefinitely-long videos.<p>3. A complex streaming architecture with minimal latency. Enabling an end-to-end avatar zoom call requires several building blocks, including voice transcription, LLM inference, and text-to-speech generation in addition to video generation. We use Deepgram as our AI voice partner. Modal as the end-to-end compute platform. And Daily.co and Pipecat to help build a parallel processing pipeline that orchestrates everything via continuously streaming chunks. Our system achieves end-to-end latency of 3-6 seconds from user input to avatar response. Our target is <2 second latency.<p>More technical details here: <a href="https://lemonslice.com/live/technical-report">https://lemonslice.com/live/technical-report</a>.<p>Current limitations that we want to solve include: (1) enabling whole-body and background motions (we’re training a next-gen model for this), (2) reducing delays and improving resolution (purpose-built ASICs will help), (3) training a model on dyadic conversations so that avatars learn to listen naturally, and (4) allowing the character to “see you” and respond to what they see to create a more natural and engaging conversation.<p>We believe that generative video will usher in a new media type centered around interactivity: TV shows, movies, ads, and online courses will stop and talk to us. Our entertainment will be a mixture of passive and active experiences depending on what we’re in the mood for. Well, prediction is hard, especially about the future, but that’s how we see it anyway!<p>We’d love for you to try out the demo and let us know what you think! Post your characters and/or conversation recordings below.
Show HN: Lemon Slice Live – Have a video call with a transformer model
Hey HN, this is Lina, Andrew, and Sidney from Lemon Slice. We’ve trained a custom diffusion transformer (DiT) model that achieves video streaming at 25fps and wrapped it into a demo that allows anyone to turn a photo into a real-time, talking avatar. Here’s an example conversation from co-founder Andrew: <a href="https://www.youtube.com/watch?v=CeYp5xQMFZY" rel="nofollow">https://www.youtube.com/watch?v=CeYp5xQMFZY</a>. Try it for yourself at: <a href="https://lemonslice.com/live">https://lemonslice.com/live</a>.<p>(Btw, we used to be called Infinity AI and did a Show HN under that name last year: <a href="https://news.ycombinator.com/item?id=41467704">https://news.ycombinator.com/item?id=41467704</a>.)<p>Unlike existing avatar video chat platforms like HeyGen, Tolan, or Apple Memoji filters, we do not require training custom models, rigging a character ahead of time, or having a human drive the avatar. Our tech allows users to create and immediately video-call a custom character by uploading a single image. The character image can be any style - from photorealistic to cartoons, paintings, and more.<p>To achieve this demo, we had to do the following (among other things! but these were the hardest):<p>1. Training a fast DiT model. To make our video generation fast, we had to both design a model that made the right trade-offs between speed and quality, and use standard distillation approaches. We first trained a custom video diffusion transformer (DiT) from scratch that achieves excellent lip and facial expression sync to audio. To further optimize the model for speed, we applied teacher-student distillation. The distilled model achieves 25fps video generation at 256-px resolution. Purpose-built transformer ASICs will eventually allow us to stream our video model at 4k resolution.<p>2. Solving the infinite video problem. Most video DiT models (Sora, Runway, Kling) generate 5-second chunks. They can iteratively extend it by another 5sec by feeding the end of the 1st chunk into the start of the 2nd in an autoregressive manner. Unfortunately the models experience quality degradation after multiple extensions due to accumulation of generation errors. We developed a temporal consistency preservation technique that maintains visual coherence across long sequences. Our technique significantly reduces artifact accumulation and allows us to generate indefinitely-long videos.<p>3. A complex streaming architecture with minimal latency. Enabling an end-to-end avatar zoom call requires several building blocks, including voice transcription, LLM inference, and text-to-speech generation in addition to video generation. We use Deepgram as our AI voice partner. Modal as the end-to-end compute platform. And Daily.co and Pipecat to help build a parallel processing pipeline that orchestrates everything via continuously streaming chunks. Our system achieves end-to-end latency of 3-6 seconds from user input to avatar response. Our target is <2 second latency.<p>More technical details here: <a href="https://lemonslice.com/live/technical-report">https://lemonslice.com/live/technical-report</a>.<p>Current limitations that we want to solve include: (1) enabling whole-body and background motions (we’re training a next-gen model for this), (2) reducing delays and improving resolution (purpose-built ASICs will help), (3) training a model on dyadic conversations so that avatars learn to listen naturally, and (4) allowing the character to “see you” and respond to what they see to create a more natural and engaging conversation.<p>We believe that generative video will usher in a new media type centered around interactivity: TV shows, movies, ads, and online courses will stop and talk to us. Our entertainment will be a mixture of passive and active experiences depending on what we’re in the mood for. Well, prediction is hard, especially about the future, but that’s how we see it anyway!<p>We’d love for you to try out the demo and let us know what you think! Post your characters and/or conversation recordings below.