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Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
In this demo, we automatically deduce a FlashAttention kernel with ~25 lines of SQL, which just expresses vanilla attention. This is possible due to three portable SQL extensions that we’re building:
- xarray-sql: a library that efficiently represents petabyte scale array datasets in the cloud as tables (relations).
- ddx: a database extension that adds automatic differentiation in the style of JAX to standard SQL, assuming the above array data model.
- einfold: a modular query planner for fast tensor contractions in vanilla SQL, again, assuming the above array data model.<p>My hope is that the relational model and algebra provide fresh perspectives on how we might do accelerator programming. Here, our thesis is that separating the logical layers in a tensor program from the physical layers is both more correct and more scalable.<p>If the Bitter Lesson is a central dogma of machine learning, then why not formalize it with relational algebra?<p>If this interests you and you’d like to tinker with us at the intersection of database theory and ML systems, join us on Discord at <a href="https://xql.systems" rel="nofollow">https://xql.systems</a> (link at the bottom). Cheers.
Show HN: Sketch Stadium – watch your agent play Pictionary
tl;dr: Agents can play Pictionary. You can connect your own agent, or just watch a game in progress here: <a href="https://sketch.wakeshift.ai/games/q5ucru" rel="nofollow">https://sketch.wakeshift.ai/games/q5ucru</a><p>Hi folks! I initially built this as a proof-of-concept for sending push notifications to AI agents. Watching the agents play Pictionary together has been compelling, so I thought I'd ship it as a standalone demonstration.<p>In Sketch Stadium, your agent subscribes to game events. Agents take turns either drawing or guessing. Easy clues are 1 point, medium are 2, and hard are 3. It's entertaining to watch and some of the drawings are impressive (and others are mind-bogglingly bad). This is the real opportunity for the model labs who have been pelican-maxxing to shine.<p>The first version gave agents the ability to draw lines 10 at a time so that it would be a little bit more like real Pictionary. However, it turns out agents are _really_ bad at drawing that way. So instead I let them use a subset of SVG to draw and that's worked much better, even for the lower-cost models.<p>You don't need to create an account to connect your agent, just add the MCP and authorize. But if you don't want to do that, just watch the game linked above.<p>As a bit more background, agents often start processes that take an indeterminate amount of time to complete (e.g. CI, waiting for approval in a separate system, sending an email and waiting for a reply). If an agent needs that process to complete before it can proceed, it can:<p>* poll,<p>* ask the user to tell it when it's done,<p>* use a bespoke built-in integration, or<p>* use a proprietary capability (e.g. Claude Code monitors)<p>Today, there's no easy, consistent way to wake up agents through a webhook or push notification. I dealt with this firsthand with Clawvisor (authorization for agents) where I needed agents to recognize when the operator had clicked an out-of-band approve button. The best-supported way to achieve that at the time was long-polling.<p>This project is basically a tech demo for Wakeshift, which is a cross-harness, simple SDK for providing push notifications for agents. It attaches to your MCP to allow consumers of your MCP to subscribe to real-time events, and uses whatever available hacks per harness to wake an agent when dormant. For most harnesses, Wakeshift requires a hook to be installed for the best experience. It will upgrade automatically as harnesses start adopting MCP events.<p>I'd love any feedback or ideas for other products or games that you'd want to see built on this capability. Or MCP servers that you use today that you think would benefit from push notifications.
Show HN: Lasm – Write formally verified webapps in Lean for Node/serverless/edge
Lasm has been my dream for a good part of 2026.
I firmly believe that formal verification is the next evolution of software engineering and programming languages. This next wave will largely be enabled by the incredibly intelligent LLMs that understand formal verification and its programming languages.<p>Lean is one of the most popular languages that allow for formal verification, and to me is incredibly powerful because it is a general purpose programming language with formal verification built directly into it.<p>You can build general purpose software in Lean, and formally verify it in Lean. Amazing.<p>Lasm brings Lean to the browser, Node, serverless, and edge environments (and more). The idea is that you can now write web apps, frontend to backend, in Lean and formally verify these apps.<p>Imagine an Express server or Hono server, running on Cloudflare Workers, with formal verification of its specification.<p>I'm an agentic engineer with an emphasis in software engineering, and Lasm was built mostly by Claude Opus 5.5 with my high-level direction.
Show HN: I built a multiplayer game where you're an aerial fighter cat
Hi everyone!<p>I had the idea for catcopter on my mind for a long time (got the domain ~13 years ago). It's a web multiplayer game where you can fly a dangerous cat and engage in competition with other players and critters (pterodactyls, chickens and angry penguins). I made it initially for me and my friends but it was quite fun so I thought I'll share it here too.<p>The game is intentionally dorky and it was fun to develop. The tools I used were mostly Opus 5.5 (objects / rigging / motion), Tripo3d for some elements I struggled with and Fable for optimisation (cpu / networking). Backend physics are done with Rapier. Luckily I had a lot of elements / game dynamics done before from another project of mine (<a href="https://flat.social" rel="nofollow">https://flat.social</a>) so the actual iteration here took me around a week.
Show HN: I Turned DOOM into a Transformer
Instead of trying to run DOOM on things, I have instead decided to use DOOM (many, many, many doom games actually) to create a transformer. For a lot of the math required I essentially had to use DOOM game mechanics and voodoo dolls (DOOM is turing complete btw) and exchange them with the actual operations, essentially creating an alternative to NumPy (a 65000x slower one). Thus, I am proud to present DOOMFORMER. Here are a list of things DOOMFORMER can do :<p>It can classify MNIST digits, even if you write these digits on the map!<p>It can talk(kinda)<p>You can use it to make decisions, so, it can play DOOM. Yes! You can play DOOM with thousands of DOOM games now! The marine's mind can be controlled by DOOM, playing on a map generated by DOOMFORMER. Truly, the possibilities are endless, and even larger-scale training of DOOMFORMER could be done on a measly T4.<p>If anyone would like to help me scale this up and make crazier things, I'd love to work together.
Show HN: A working Nintendo in ~1GB of Qwen weights
Show HN: Squishy 3D helpers powered by Three.js
Show HN: Overtone – Transcribe, correct and cut captions for any screen shape
Hi HN, I'm Ruud, co-founder of Lont. We turn company emails into personalized videos.<p>Video captions cost us manual work twice: fixing misheard words, then fixing layout, like lines ending on “the”.<p>Overtone uses multiple models to listen to the audio: gpt-transcribe, Azure STT, and Elevenlabs Scribe. When they return conflicting results, Overtone sends small clips to the models again, and then lets an LLM decide what was actually said.<p>The LLM is like a human doing a sanity check on the transcription model. Humans would easily recognize a word to be different based on context. The LLM basically does the same, using an optional context prompt + its world knowledge. Because of this the Word Error Rate (WER) on many test files is lower when using Overtone, compared to just using a model without it.<p>Sure, the cost is higher because we use up to 3 different STT models + an LLM, but it still removes mistakes, reducing the time needed to fix captions to a minimum, and reducing the chance you'll miss something. For us it's worth it. For ultra high volume use cases it may not be.<p>The rules-based cutter nicely cuts the transcript into captions that look good, depending on screen size: 16:9 is different from 9:16, and the Overtone Format we created supports any type really. Pauses become breaks, captions are grouped per speaker, and names/numbers are never spread out over multiple captions.<p>How to add these captions to your videos? A videojs plugin can do this automatically, so you don't burn in your captions. The plugin lets you cycle between styled captions, plain captions, and off. You can burn them into an MP4 file with overtone burn.<p>Quickly try it out just the cutter? Try:<p><pre><code> npx overtone-captions recut yoursub.srt
</code></pre>
Transcribing audio with API keys works too. Use --dry-run to show a cost estimate before you send.<p>We open-sourced this first version (MIT) because we believe many people can benefit from this, especially if your captions just need to be good. There's plenty of room to improve though, so we've made it easy to help contribute to add style packs, languages, and providers/models.<p>You can find a demo at: <a href="https://overtone.lont.ai/" rel="nofollow">https://overtone.lont.ai/</a><p>Find the code and benchmarks here: <a href="https://github.com/lonthq/overtone-captions" rel="nofollow">https://github.com/lonthq/overtone-captions</a>
Show HN: Overtone – Transcribe, correct and cut captions for any screen shape
Hi HN, I'm Ruud, co-founder of Lont. We turn company emails into personalized videos.<p>Video captions cost us manual work twice: fixing misheard words, then fixing layout, like lines ending on “the”.<p>Overtone uses multiple models to listen to the audio: gpt-transcribe, Azure STT, and Elevenlabs Scribe. When they return conflicting results, Overtone sends small clips to the models again, and then lets an LLM decide what was actually said.<p>The LLM is like a human doing a sanity check on the transcription model. Humans would easily recognize a word to be different based on context. The LLM basically does the same, using an optional context prompt + its world knowledge. Because of this the Word Error Rate (WER) on many test files is lower when using Overtone, compared to just using a model without it.<p>Sure, the cost is higher because we use up to 3 different STT models + an LLM, but it still removes mistakes, reducing the time needed to fix captions to a minimum, and reducing the chance you'll miss something. For us it's worth it. For ultra high volume use cases it may not be.<p>The rules-based cutter nicely cuts the transcript into captions that look good, depending on screen size: 16:9 is different from 9:16, and the Overtone Format we created supports any type really. Pauses become breaks, captions are grouped per speaker, and names/numbers are never spread out over multiple captions.<p>How to add these captions to your videos? A videojs plugin can do this automatically, so you don't burn in your captions. The plugin lets you cycle between styled captions, plain captions, and off. You can burn them into an MP4 file with overtone burn.<p>Quickly try it out just the cutter? Try:<p><pre><code> npx overtone-captions recut yoursub.srt
</code></pre>
Transcribing audio with API keys works too. Use --dry-run to show a cost estimate before you send.<p>We open-sourced this first version (MIT) because we believe many people can benefit from this, especially if your captions just need to be good. There's plenty of room to improve though, so we've made it easy to help contribute to add style packs, languages, and providers/models.<p>You can find a demo at: <a href="https://overtone.lont.ai/" rel="nofollow">https://overtone.lont.ai/</a><p>Find the code and benchmarks here: <a href="https://github.com/lonthq/overtone-captions" rel="nofollow">https://github.com/lonthq/overtone-captions</a>
Show HN: A proof-of-concept formalization of the IRS federal tax forms
The linked repo is 100% vibe coded, but it was built on a handful of principles:<p>- Most tax software derives trust from the party who maintains it (e.g. Intuit, the IRS, etc.)<p>- Vibe coded software lacks this trustworthiness.<p>- Therefore any formalization should build in verifiability and mechanisms that make it possible for users/maintainers to trace claims/interpretations back to authoritative source documents.<p>It's very much a back-burner passive project right now (do not use this for anything real!), but I feel strongly that something like this ought to exist for the public good. I built it to get a sense of what the shape of what such a thing might be.
Show HN: A proof-of-concept formalization of the IRS federal tax forms
The linked repo is 100% vibe coded, but it was built on a handful of principles:<p>- Most tax software derives trust from the party who maintains it (e.g. Intuit, the IRS, etc.)<p>- Vibe coded software lacks this trustworthiness.<p>- Therefore any formalization should build in verifiability and mechanisms that make it possible for users/maintainers to trace claims/interpretations back to authoritative source documents.<p>It's very much a back-burner passive project right now (do not use this for anything real!), but I feel strongly that something like this ought to exist for the public good. I built it to get a sense of what the shape of what such a thing might be.
Show HN: UK Farming Simulator
Show HN: Auto Dismiss Mac OS "Allow <app> to find devices on local networks?"
This popup has been a constant gripe of mine for years. I almost NEVER want to allow this permission and there is no system setting to disable it globally and prevent the popup from interrupting your typing, etc. This tiny swift app I built will autoclose it in 1/4th of a second and leave behind a little notification that it did so. Would love to hear feedback!
Show HN: Auto Dismiss Mac OS "Allow <app> to find devices on local networks?"
This popup has been a constant gripe of mine for years. I almost NEVER want to allow this permission and there is no system setting to disable it globally and prevent the popup from interrupting your typing, etc. This tiny swift app I built will autoclose it in 1/4th of a second and leave behind a little notification that it did so. Would love to hear feedback!
Show HN: Popkern – open-source CAD kernel with G2 corner blends, browser IDE
Show HN: A Lego-like composable Framework for RSI
Show HN: Yavashark: I've Built a JavaScript Engine Every Day for 950 Days
Feel free to give me feedback or discuss what you have on your mind. I had lots of fun building this, so I hope you have too reading this!
Show HN: Yavashark: I've Built a JavaScript Engine Every Day for 950 Days
Feel free to give me feedback or discuss what you have on your mind. I had lots of fun building this, so I hope you have too reading this!
Show HN: Nasscad – local-first 3D CAD for browser and Windows
Show HN: Nasscad – local-first 3D CAD for browser and Windows