The best Hacker News stories from Show from the past day
Latest posts:
Show HN: How far do I have to go to run into 100k people?
Show HN: Scala Tutorials – interactive Scala 3 lessons in the browser
Show HN: tale.fyi, we deserve a home for fiction
for decades, i have been concerned that the internet was being built around non-fiction, so i built something to show how we could celebrate great fiction on the web.<p>i started with an amazing library from the public domain, and i also added tools to add your own stories<p>super interested to hear any feedback, and if you read anything good!
Show HN: Yap – OSS on-device voice dictation for macOS with no model to download
Hey HN! I wanted to share this OSS project I've been working on.<p>It's called Yap and its a small menu-bar app for macOS that does voice to text for any input. You'll set a hotkey, press it, talk, press it again, and the text gets pasted into whatever field you were in. Everything runs locally and never leaves your computer. Fully OSS and MIT licensed.<p>With macOS 26, Apple recently added two new APIs, SpeechAnalyzer and SpeechTranscriber, that do streaming on-device speech to text using models the OS ships and manages. So the app ships no model of its own and loads nothing before the first word. A recent benchmark put Apple's model slightly ahead of Whisper Small on accuracy and about 3x faster (see: <a href="https://news.ycombinator.com/item?id=48894752">https://news.ycombinator.com/item?id=48894752</a>). On Mac, there's really no need anymore to download models or pay for expensive APIs.<p>A lot of existing dictation tools do one of a few things I wanted to avoid with this OSS project. They either:<p>- cost money (for something that's literally built into the OS)<p>- bundle memory-intensive models (e.g. Whisper or Parakeet)<p>- webapps wrapped in Electron<p>- Intel macs straight up don't work<p>- closed source<p>- use third-party APIs that will have access to all your transcripts<p>It's around 3,000 lines of native Swift in a 4 MB app and idles near 60 MB of memory. Audio comes off AVAudioEngine into SpeechAnalyzer with volatile results turned on for the live preview, history is stored in SwiftData. There's no network code in it at all.<p>Repo and a demo available here: <a href="https://github.com/FrigadeHQ/yap" rel="nofollow">https://github.com/FrigadeHQ/yap</a><p>Happy to answer questions and would love to hear any feature requests!
Show HN: I was tired of opening 2 tabs for every HN link, so I made a userscript
HN is great for the links people share, but a big part of the value I get comes from reading the discussion around them. I realized I was always opening the article in one tab and the comments in another, constantly switching back and forth.<p>I figured there was probably a simpler way, so I threw together this userscript to merge the two.<p>1. Clicking a link from Hacker News opens the article with a side panel containing the discussion. It doesn't require your credentials, is resizable, and is easy to tweak if you want to customize it.<p>2. If you land on an article that has previously been shared on HN, the script finds the existing discussion and adds a button in the top-right to open the panel.<p>Feedback welcome.
Show HN: Formally verified 3D CSG: Trust 93 lines spec, not 1000 lines AI code
To my knowledge, this is the first formally verified implementation of a 3D constructive solid geometry (CSG) operation: mesh intersection, implemented in Lean 4 and verified against a concise specification that pins down the surface of the resulting mesh exactly and guarantees practical well-formedness conditions on the triangulation.<p>This project is also an experiment in avoiding having to trust AI-generated code. A human reviewer only needs to read 93 lines of formal specification and run the Lean checker to certify the correctness of the kernel, skipping the intricate 1000+ lines of AI-written implementation. To prove correctness, AI autonomously wrote over 60,000 lines of Lean proofs, which also never have to be inspected by a human. The Lean checker guarantees conformance to the specification at compile time, with zero trust placed in any LLM. This allows us to treat the implementation and proofs as a black box. I guided the agent through the milestones described in the readme to arrive at the result presented here.<p>Also take a look at the web demo <a href="https://schildep.github.io/verified-3d-mesh-intersection/" rel="nofollow">https://schildep.github.io/verified-3d-mesh-intersection/</a>, which runs the verified mesh intersection kernel compiled to WebAssembly in your browser.
Show HN: XY – A Fast, composable, GPU-accelerated interactive plotting library
Show HN: Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard)
Hey HackerNews,<p>I built this project over the last few weeks as a palette cleanser from a failed game launch.<p>I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate.<p>Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size).<p>Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a loop. The models have no access to coordinates, map-data, or external memory scratches - they must navigate using only immediate local neighbourhood observations.<p>There is a model dropdown and you can see how the model has progressed over each phase, constantly getting smaller and increasing its solve rate. Total trained models number in the thousands - I just expose the winning models from each phase.<p>Overall a fun experiment, with much implementation help from AI agents to scaffold and implement the code (I'm a lazy software dev).
Show HN: Infinite Jigsaw Game
Had the idea forever, finally got it working.
Show HN: Distill and serve models with frontier quality for half the cost
Hi HN, we built world-model-optimizer, an open source tool to continually improve models specialized to agents.<p>Agent traces you already capture are opportunities to get signal on how to make your model cheaper, faster, better.<p>We do this by continuously a) distilling relevant chain of thought from larger open source models into smaller ones, b) model routing to frontier + OS models, and c) token compaction to remove noise and save on tokens.<p>`wmo build` allows you to build a simulation to optimize against with your agent traces with your OpenRouter key<p>`wmo optimize` trains a router, compaction, and distills chain of thought from a larger model into your specialized model<p>`wmo serve` gives you an endpoint for your model<p>When you call your model, behind the scenes a router decides which tasks should go to the frontier versus your model. Tinker continually trains as new traces arrive.<p>We're also working on a hosted solution that does continual training + serving for you <a href="https://experientiallabs.ai">https://experientiallabs.ai</a>
Show HN: Descript wanted $24/mo, I built an open-source alternative in a weekend
Descript costs $24/mo, so I built this over a single weekend with Fable!<p>Introducing Rescript: edit videos by simply editing the transcript text. Drop in a video and it is transcribed locally with per-word timestamps and speaker labels. Delete words in the transcript and the corresponding clip is cut from the video.<p>Runs fully in the browser: Local, free, offline and open source.<p>→ Github <a href="https://github.com/wassgha/rescript" rel="nofollow">https://github.com/wassgha/rescript</a><p>→ App <a href="https://wassgha.github.io/rescript" rel="nofollow">https://wassgha.github.io/rescript</a>
Show HN: Let's Seal – Let's Encrypt for document signing, free and self-hosted
TLDR, Let's Seal gives the finger to Adobe and every doc signing tool (docusign, google, etc) who pay to play with the Adobe Approved Trust List and then charge you for something that should be free.<p>Currently even the person checking if a document/contract is sealed or code is authentic has to also be inside the same Adobe walled garden too. Verification, the part that should be free is the part everyone charges for. Thats the shape Let's Encrypt fixed for TLS, and I wanted the same thing for documents and files.<p>The core idea therefore needed to go a bit beyond e signatures and i created an open standard (SEAL), plus free tools that implement it.<p>When you seal a file, three independent things happen.<p>1. it gets a signature from a certificate authority, chaining to a public root.
2. its record is appended to an RFC 6962 transparency log. and
3. its SHA256 is timestamped on a public blockchain (Bitcoin) via OpenTimestamps.
Those three give you integrity, transparency and a timestamped proof. And importantly, none of those depend on Let's Seal and none are gated.<p>You can verify with the tools you already have, no Let's Seal account and no Let's Seal software. A sealed PDF carries a standard PAdES signature, so any PDF reader validates it. A sealed build artefact carries a cosign compatible signature and a SLSA provenance attestation. The Bitcoin timestamp verifies with stock ots.<p>3 ways to use it.<p>1. The free web app. We kindly have backing from Backblaze to cover storage costs for the foreseeable. So you can upload or issue any number of documents, get a public proof page at /d/<hash> and verify it at <a href="https://verify.letsseal.org" rel="nofollow">https://verify.letsseal.org</a> for free. Multiple accounts, multiple seats, enterprise functions. Free.<p>2. Self host the whole thing. Apache-2.0, one Next.js app plus a signing service that holds the CA key on localhost. Storage is any S3-compatible bucket or local disk. If you'd rather run your own root of trust, you can.<p>3. Programmatically. via the CLI and a hosted API. This is the Let's Encrypt/certbot angle. Seal or anchor things from CI, or have a backend seal every invoice or report as its generated.<p>The CLI is sealbot. It runs anywhere Node runs (npx sealbot) and there are native binaries for macOS, Linux and Windows with no runtime needed.<p>Theres a GitHub Action wrapping the same tool, so a release workflow can seal its own artifacts. Its what proves our own releases.<p>KYC is semi-handled (to a degree) it's hard to do for free (at least for now), but issuers (your companies or websites) domains can be authenticated with a DNS record added, which proves the issuer has control over a domain. Sign-in can be authenticated to an email via Google Sign in and a few others will be added to the web app in time (Same as Docusign currently). Ideas welcome on future KYC should there be a demand.<p>Feedback welcome on the standard (SPEC.md in the repo).<p>Repo: <a href="https://github.com/letsseal/letsseal" rel="nofollow">https://github.com/letsseal/letsseal</a>
Site: letsseal.org<p>Thx
Show HN: Let's Seal – Let's Encrypt for document signing, free and self-hosted
TLDR, Let's Seal gives the finger to Adobe and every doc signing tool (docusign, google, etc) who pay to play with the Adobe Approved Trust List and then charge you for something that should be free.<p>Currently even the person checking if a document/contract is sealed or code is authentic has to also be inside the same Adobe walled garden too. Verification, the part that should be free is the part everyone charges for. Thats the shape Let's Encrypt fixed for TLS, and I wanted the same thing for documents and files.<p>The core idea therefore needed to go a bit beyond e signatures and i created an open standard (SEAL), plus free tools that implement it.<p>When you seal a file, three independent things happen.<p>1. it gets a signature from a certificate authority, chaining to a public root.
2. its record is appended to an RFC 6962 transparency log. and
3. its SHA256 is timestamped on a public blockchain (Bitcoin) via OpenTimestamps.
Those three give you integrity, transparency and a timestamped proof. And importantly, none of those depend on Let's Seal and none are gated.<p>You can verify with the tools you already have, no Let's Seal account and no Let's Seal software. A sealed PDF carries a standard PAdES signature, so any PDF reader validates it. A sealed build artefact carries a cosign compatible signature and a SLSA provenance attestation. The Bitcoin timestamp verifies with stock ots.<p>3 ways to use it.<p>1. The free web app. We kindly have backing from Backblaze to cover storage costs for the foreseeable. So you can upload or issue any number of documents, get a public proof page at /d/<hash> and verify it at <a href="https://verify.letsseal.org" rel="nofollow">https://verify.letsseal.org</a> for free. Multiple accounts, multiple seats, enterprise functions. Free.<p>2. Self host the whole thing. Apache-2.0, one Next.js app plus a signing service that holds the CA key on localhost. Storage is any S3-compatible bucket or local disk. If you'd rather run your own root of trust, you can.<p>3. Programmatically. via the CLI and a hosted API. This is the Let's Encrypt/certbot angle. Seal or anchor things from CI, or have a backend seal every invoice or report as its generated.<p>The CLI is sealbot. It runs anywhere Node runs (npx sealbot) and there are native binaries for macOS, Linux and Windows with no runtime needed.<p>Theres a GitHub Action wrapping the same tool, so a release workflow can seal its own artifacts. Its what proves our own releases.<p>KYC is semi-handled (to a degree) it's hard to do for free (at least for now), but issuers (your companies or websites) domains can be authenticated with a DNS record added, which proves the issuer has control over a domain. Sign-in can be authenticated to an email via Google Sign in and a few others will be added to the web app in time (Same as Docusign currently). Ideas welcome on future KYC should there be a demand.<p>Feedback welcome on the standard (SPEC.md in the repo).<p>Repo: <a href="https://github.com/letsseal/letsseal" rel="nofollow">https://github.com/letsseal/letsseal</a>
Site: letsseal.org<p>Thx
Show HN: FeyNoBg – Automatic background removal model and training library
Hey HN, I’m Shreyash from Feyn. We help companies build custom models from their data.<p>Today, we’re releasing FeyNoBg, an automatic background removal model. Alongside it, we're open-sourcing NoBg, the Python library we built to train and run it.<p>Try the model here: <a href="https://huggingface.co/spaces/feyninc/feynobg" rel="nofollow">https://huggingface.co/spaces/feyninc/feynobg</a>. Check out the library here: <a href="https://github.com/feyninc/nobg" rel="nofollow">https://github.com/feyninc/nobg</a><p>Some sample outputs:<p>(1) Soccer Freekick: <a href="https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9QPCbwj/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q...</a><p>(2) Hair in wind: <a href="https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLLdGK8Z/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLL...</a><p>(3) Bicycle with visible spokes: <a href="https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZyQ-QJ/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZ...</a><p>(4) Live Demo video: <a href="https://youtu.be/b1heHPvY8BM" rel="nofollow">https://youtu.be/b1heHPvY8BM</a><p>Background removal separates an image's subject from its surrounding. We've all tried it at some point. Often it is to reuse the subject in a different artifact. Nowadays, it is common to make chat stickers out of it. It is one of the most common but under-appreciated uses of AI. It is also surprisingly complex. Models can be easily confused by camouflage, motion blur, or fine structures like hair.<p>The task requires two skills. First, a model has to identify the foreground. Second, it has to trace the foreground’s boundary and estimate an opacity value for each pixel. Generally, these skills are taught with different datasets. That creates a failure point. A poor training mix can improve one skill at the expense of the other. We saw this in our controlled evaluation. A training run with just the MaskFactory dataset improved on the CAMO benchmark but regressed on DIS5K.<p>For FeyNoBg, we took an interpretability-first approach to training. We first studied how BiRefNet’s stages contribute to finding the foreground and reconstructing its boundary. We discovered that the third stage of it's feature extractor holds a lot of information. Both localization and boundary reconstruction depend heavily on the feature map produced here.<p>This led us to expand this stage from 18 to 24 blocks while preserving the pre-trained weights. We then trained FeyNoBg on 26.1K diverse examples assembled from 10 datasets. The goal was to improve foreground identification and boundary precision without sacrificing either one.<p>Across eight benchmarks, FeyNoBg achieves the best published score on four and comes within 2% of the leader on the rest.<p>Building FeyNoBg also exposed a tooling problem. Image matting models are usually released as isolated repositories with incompatible preprocessing, training, and evaluation code. We built NoBg to solve this. NoBg puts these workflows behind one Python interface. It supports BiRefNet today, with more architectures coming. We hope you build something exciting with it!<p>Happy to answer any questions!
Show HN: FeyNoBg – Automatic background removal model and training library
Hey HN, I’m Shreyash from Feyn. We help companies build custom models from their data.<p>Today, we’re releasing FeyNoBg, an automatic background removal model. Alongside it, we're open-sourcing NoBg, the Python library we built to train and run it.<p>Try the model here: <a href="https://huggingface.co/spaces/feyninc/feynobg" rel="nofollow">https://huggingface.co/spaces/feyninc/feynobg</a>. Check out the library here: <a href="https://github.com/feyninc/nobg" rel="nofollow">https://github.com/feyninc/nobg</a><p>Some sample outputs:<p>(1) Soccer Freekick: <a href="https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9QPCbwj/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q...</a><p>(2) Hair in wind: <a href="https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLLdGK8Z/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLL...</a><p>(3) Bicycle with visible spokes: <a href="https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZyQ-QJ/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZ...</a><p>(4) Live Demo video: <a href="https://youtu.be/b1heHPvY8BM" rel="nofollow">https://youtu.be/b1heHPvY8BM</a><p>Background removal separates an image's subject from its surrounding. We've all tried it at some point. Often it is to reuse the subject in a different artifact. Nowadays, it is common to make chat stickers out of it. It is one of the most common but under-appreciated uses of AI. It is also surprisingly complex. Models can be easily confused by camouflage, motion blur, or fine structures like hair.<p>The task requires two skills. First, a model has to identify the foreground. Second, it has to trace the foreground’s boundary and estimate an opacity value for each pixel. Generally, these skills are taught with different datasets. That creates a failure point. A poor training mix can improve one skill at the expense of the other. We saw this in our controlled evaluation. A training run with just the MaskFactory dataset improved on the CAMO benchmark but regressed on DIS5K.<p>For FeyNoBg, we took an interpretability-first approach to training. We first studied how BiRefNet’s stages contribute to finding the foreground and reconstructing its boundary. We discovered that the third stage of it's feature extractor holds a lot of information. Both localization and boundary reconstruction depend heavily on the feature map produced here.<p>This led us to expand this stage from 18 to 24 blocks while preserving the pre-trained weights. We then trained FeyNoBg on 26.1K diverse examples assembled from 10 datasets. The goal was to improve foreground identification and boundary precision without sacrificing either one.<p>Across eight benchmarks, FeyNoBg achieves the best published score on four and comes within 2% of the leader on the rest.<p>Building FeyNoBg also exposed a tooling problem. Image matting models are usually released as isolated repositories with incompatible preprocessing, training, and evaluation code. We built NoBg to solve this. NoBg puts these workflows behind one Python interface. It supports BiRefNet today, with more architectures coming. We hope you build something exciting with it!<p>Happy to answer any questions!
Show HN: Physically accurate black hole you can put in your room
We have a black hole at home — with actual relativistic physics, live in your browser.<p>I'm Sasha (Alexander) Plavin, an astrophysicist at Harvard's Black Hole Initiative studying quasars and black hole environments. I work with raytracing/radiative transfer simulations professionally, and wanted to make one anyone can play with — so I built this app.<p>Put the black hole onto your screen (any browser), or directly into your room with AR or VR (requires WebXR, for example Chrome on Android, or any VR headset). When looking around, pay attention to unintuitive relativistic effects: rays bending around the black hole, and special relativistic "Doppler boosting" changing brightness depending on the viewing angle — zoom out to see the fast jet where the latter effect is especially pronounced.
Faint markers show where other viewers are standing right now.<p>Alternatively, put the black hole in front of your camera and watch it lens
your actual surroundings. Light winds around the hole, so you see both what's in front of you and what's behind you at once (when the device and browser allow both camera feeds). The closer to the black hole, the stronger the bending — and the longer the light-travel delay: wave at it and watch the changes propagate inward.
(unfortunately, WebXR restrictions make AR passthrough and the camera feed mutually exclusive)<p>No signup, basic features work on every device, no data uploaded — the camera feed never leaves your device. Source code: <a href="https://github.com/aplavin/blackhole.plav.in" rel="nofollow">https://github.com/aplavin/blackhole.plav.in</a>.<p>Enjoy having a black hole in your room, or use it for education/outreach — any questions, feedback, or suggestions are welcome!
Show HN: Physically accurate black hole you can put in your room
We have a black hole at home — with actual relativistic physics, live in your browser.<p>I'm Sasha (Alexander) Plavin, an astrophysicist at Harvard's Black Hole Initiative studying quasars and black hole environments. I work with raytracing/radiative transfer simulations professionally, and wanted to make one anyone can play with — so I built this app.<p>Put the black hole onto your screen (any browser), or directly into your room with AR or VR (requires WebXR, for example Chrome on Android, or any VR headset). When looking around, pay attention to unintuitive relativistic effects: rays bending around the black hole, and special relativistic "Doppler boosting" changing brightness depending on the viewing angle — zoom out to see the fast jet where the latter effect is especially pronounced.
Faint markers show where other viewers are standing right now.<p>Alternatively, put the black hole in front of your camera and watch it lens
your actual surroundings. Light winds around the hole, so you see both what's in front of you and what's behind you at once (when the device and browser allow both camera feeds). The closer to the black hole, the stronger the bending — and the longer the light-travel delay: wave at it and watch the changes propagate inward.
(unfortunately, WebXR restrictions make AR passthrough and the camera feed mutually exclusive)<p>No signup, basic features work on every device, no data uploaded — the camera feed never leaves your device. Source code: <a href="https://github.com/aplavin/blackhole.plav.in" rel="nofollow">https://github.com/aplavin/blackhole.plav.in</a>.<p>Enjoy having a black hole in your room, or use it for education/outreach — any questions, feedback, or suggestions are welcome!
Show HN: HART OS – an open-source AI OS built so frontier AI needs no datacenter
Show HN: SpinWin – A macOS menu bar app to visually rotate or spin any window
SpinWin is an open source menu bar app to visually rotate any individual window. You can rotate / spin as many as you want one by one.<p>I was scrolling reels when one came on where the content creator suggested "turning the phone upside down" for a portion. I was sitting at my mac, and decided to see if I could find something that did this. To my surprise, nothing exists, so I decided to build it.<p>Turns out macOS doesn't provide any public API to do anything like this, so I wound up using the Accessibility API to move the target window offscreen, where it keeps rendering. Then we capture it's contents with ScreenCaptureKit, and display that on a transparent, borderless overlay window which has a CALayer to rotate it.<p>It's written in Swift, open source, MIT license, and signed/notarized.<p>Have fun - would love to hear your comments.
Show HN: CheapSecurity – Lightweight, Self-Hosted CCTV for Linux SBCs