The best Hacker News stories from Show from the past day
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Show HN: HackerHack – HackerBooks but for Posts Posted on HN
No AI used for this btw.
Show HN: Warsaw in voxels – An interactive 3D map of the city centre
Explore central Warsaw in voxels - slightly inspired by Minecraft, and MagicaVoxel.
You can change the date, time of day and weather, explore the city, or click on a pedestrian to control them (here a bit of inspiration from Dungeon Keeper).<p>Works best on desktop, but I tried to make it usable on mobile too. The initial load can take a while. It's based on several open datasets, including:<p>- OpenStreetMap (<a href="https://www.openstreetmap.org/#map=6/52.02/19.14" rel="nofollow">https://www.openstreetmap.org/#map=6/52.02/19.14</a>) - buildings, streets, sidewalks, tram tracks, etc<p>- GUGiK / Geoportal (<a href="https://www.geoportal.gov.pl/" rel="nofollow">https://www.geoportal.gov.pl/</a>) - LiDAR for building heights and trees, aerial imagery for colours<p>- Warsaw's city tree database (<a href="https://mapa.um.warszawa.pl/" rel="nofollow">https://mapa.um.warszawa.pl/</a>) - individual tree species, used for seasonal foliage<p>Open-source (<a href="https://github.com/stared/warsaw-vibe-voxel" rel="nofollow">https://github.com/stared/warsaw-vibe-voxel</a>), so feel invited to (re)use it as you wish!
Show HN: I've updated my Linux System Call website and API
I first released those in back in 2020 and now I finally added more architectures, Kernel selector, syscall descriptions and a few other niceties. It should work a bit better on mobile devices too but I am no CSS expert.<p>The API is at <a href="https://api.syscall.sh" rel="nofollow">https://api.syscall.sh</a>
Show HN: Wikihack – HackerTube, but for Wikipedia articles mentioned on HN
After seeing Karpathy's Hackertube and Hackerbooks, I just had to make Wikihack.<p>I scraped all mentions of wikipedia on hackernews and ranked them, and turn them into a scrollable feed to make it a bit more easy to digest / interact with. You can see where people submitted the articles and where people mentioned it in comments.<p>The top shows the most frequently linked articles.<p>Random is random but slightly weighted towards the more frequently linked articles (a little bit like Wikipedia actually).<p>Rising is ranked towards the past 12 months.<p>The entire site is served only through the frontend -- I'll add more features if people enjoy this!
Show HN: Subvocal – Talk to your computer without making noise
Hi HN, we're Aarush and Arnav from The Subvocal Company (YC F26) (<a href="https://subvocal.company/">https://subvocal.company/</a>). We’re building smart glasses that let you talk to your computer without talking out loud. You can mouth words silently and discreetly and the glasses decode them to text at the speed of speech.<p>Here's a video of Dave from YC trying it out: <a href="https://www.youtube.com/watch?v=9QdX1eObVYI" rel="nofollow">https://www.youtube.com/watch?v=9QdX1eObVYI</a><p>(Edit: you don't actually have to move your lips or even open your mouth for this to work! Since the RF can see inside your mouth the system can be fine tuned on "internal speech". This isn't the automatic tongue movements that occur when "thinking" words to yourself, but more like mouthing with your lips closed. It's a bit weird to do, but with practice, we have been able to get it down pretty well.)<p>Arnav and I were studying CS at Georgia Tech at the beginning of the year, with no experience in silent speech or sensing. We were both AI-native builders and power users of Wispr Flow. What bugged us was not being able to use these tools in the library (or an office) or in public. At Stanford’s TreeHacks in February, we decided to build the most technically ridiculous thing we could think of. We’d read about AlterEgo, a silent speech system out of MIT’s Media Lab, and wanted to recreate it in 36 hours. We built a crude prototype, sticking EMG electrodes onto my face:<p><a href="https://drive.google.com/file/d/1mwhv5HZMz3UTevdrgwS5O7g2jWV0K9r2/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1mwhv5HZMz3UTevdrgwS5O7g2jWV...</a><p>We got it to kind of work at a letter level in a very janky way. It wasn’t good enough to win the hackathon, but convinced us to go all in. The first time I typed a sentence without anyone noticing, I felt the magic of the technology.<p>We quickly realized EMG (reading muscle electrical signals) wouldn’t work:<p>- Signal changes due to sweat<p>- Electrical interference<p>- Placement variability<p>- Most importantly, no one wants to stick electrodes onto their face!!<p>So we switched to RF and built the glasses you see now. They emit radio waves, similar to Bluetooth, that pass through your face. As they cross the tongue, lips and soft palate, they’re modulated by silent mouthing. We train foundation models on this data to decode these perturbations to text.<p>Despite prior academic research, getting this to work was a huge engineering challenge. First we built an extremely low noise RF sensing stack. Then we had to place antennas so their fields overlapped maximally with the articulators. Our first attempt on glasses failed completely, so we reverted to old habits and taped antennas on our face:<p><a href="https://drive.google.com/file/d/1ks0dS0eZiTV9EqSFsyB_w5BBjpcBAfX-/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1ks0dS0eZiTV9EqSFsyB_w5BBjpc...</a><p>Since taping antennas consistently in the same spot and angle was hard, we created this weird but effective head rig to hold them in place:<p><a href="https://drive.google.com/file/d/1rhzNFpdDzr7oHk0isFY70dkoV4CAF1pT/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1rhzNFpdDzr7oHk0isFY70dkoV4C...</a><p>This worked, but obviously wasn’t a product yet. So we worked deep in simulations to design glasses that could hold the antennas.<p>That evolved into our design now, with antennas embedded within the frames. Once we established there was a signal, the next hurdle was cross-user generalization. Everyone’s facial anatomy, and hence RF signal patterns, are vastly different. We tried algorithmic workarounds but realized the only lever was to massively scale pretraining data.<p>So that’s what we did. We set up booths in our office and paid people to read prompts wearing our glasses, to pretrain our models on. This produced a beautiful scaling curve, with errors falling quickly as we scaled our dataset and cross-user performance becoming genuinely compelling (as you see in the video above; Dave only had a short 20-sentence calibration).<p>We always heard about building things from first principles but never understood it before this. We tried tackling it top down but completely failed. Only when we broke each component (the antenna, electronics, ML) down to its fundamentals to understand and optimize it did we get it to work really well.<p>Over the next few months we’re continuing R&D to turn our prototype glasses into the slick product on our website and ship in Q1 2027. We’re also scaling up our dataset to improve performance on edge cases.<p>For anyone who loves voice dictation but hates using it around people, we’ve opened preorders for our first batch of 500 units, with a $200 discount off the final price. Allocation is running out quickly, so get yours at <a href="https://subvocal.co/preorder" rel="nofollow">https://subvocal.co/preorder</a>!
Show HN: I rebuilt my old canvas 3D library as a small WebGL 2 library – phoria2
Show HN: A language model that lives in a boot sector
All the ideas came from the boot sector demo scene where people had been writing chess and BASIC games in 512 bytes, even a C compiler. I wondered what one can do using a neural network instead of a program and generating boot logs for non-existent hardware was something fun to try.<p>Entire inference engine is 510 bytes of MBR, weights, vocab, and strings reside in the sectors following MBR. Small char-level neural network was trained on dmesg generated by machine, so machine produces Linux boot messages infinitely.<p>train.py performs precisely the same integer arithmetic operations as the asm code does and thus produces an output identical character-by-character to the one produced by QEMU. The difference analysis revealed my favorite bug. The characters at the beginning matched those generated by the simulator, but then the output diverged. I retrieved the hidden layer via QEMU’s debug port and compared the neurons' values with those of the simulator, one neuron at a time. Only the value of the first neuron was correct. The activation involves idiv, and idiv stores its remainder in edx. The pointer to the biases table was stored in edx, which resulted in the first neuron being biased correctly and the following ones using garbage. Now the bias' address is determined from the loop counter, and no pointers have to survive the division. Another bug I found simply by looking at the code before I did my first run-through: the BIOS print routine zeros out bx, but bx contains the predicted character number. This explains the push and pop of bx surrounding the print.<p>sectorllm is the most similar to my project, as it runs an actual 5-layer llama2 with attention mechanism in just 1.2K of assembly code. It is the trade-off in the opposite direction, as my engine needs to fit into one sector and therefore my network has just one hidden layer of int8 operations for dreaming the dmesg.
Show HN: A language model that lives in a boot sector
All the ideas came from the boot sector demo scene where people had been writing chess and BASIC games in 512 bytes, even a C compiler. I wondered what one can do using a neural network instead of a program and generating boot logs for non-existent hardware was something fun to try.<p>Entire inference engine is 510 bytes of MBR, weights, vocab, and strings reside in the sectors following MBR. Small char-level neural network was trained on dmesg generated by machine, so machine produces Linux boot messages infinitely.<p>train.py performs precisely the same integer arithmetic operations as the asm code does and thus produces an output identical character-by-character to the one produced by QEMU. The difference analysis revealed my favorite bug. The characters at the beginning matched those generated by the simulator, but then the output diverged. I retrieved the hidden layer via QEMU’s debug port and compared the neurons' values with those of the simulator, one neuron at a time. Only the value of the first neuron was correct. The activation involves idiv, and idiv stores its remainder in edx. The pointer to the biases table was stored in edx, which resulted in the first neuron being biased correctly and the following ones using garbage. Now the bias' address is determined from the loop counter, and no pointers have to survive the division. Another bug I found simply by looking at the code before I did my first run-through: the BIOS print routine zeros out bx, but bx contains the predicted character number. This explains the push and pop of bx surrounding the print.<p>sectorllm is the most similar to my project, as it runs an actual 5-layer llama2 with attention mechanism in just 1.2K of assembly code. It is the trade-off in the opposite direction, as my engine needs to fit into one sector and therefore my network has just one hidden layer of int8 operations for dreaming the dmesg.
Show HN: Point your camera at a landscape to learn about its geology
I've been working on a fun little app that paints geologic maps over landscapes using augmented reality. You can use it to identify what the mapped rocks are, read descriptions about them, and view the earth as it looked when they formed by turning back the continent positions. It spun out of some augmented reality work I was doing on my other side project app - Flyover Country, which tells you about the view from the airplane window.<p>I'm in Minnesota, so i haven't had a chance to test it on a huge amount of terrain (the best I've found is the Mississippi River gorge in the twin cities... I need a trip up north), so anyone that's somewhere with some more bumps: please help!).<p>It's stuck in the Apple review queue, but it's up on testflight for anyone interested. Google approved it quickly and it's fully out on android (links to both on the marketing site).<p>edit: got a report that testflight was asking for an install code. Seems like a testflight app issue, but dismissing it and installing as normal seems to work!
Show HN: Point your camera at a landscape to learn about its geology
I've been working on a fun little app that paints geologic maps over landscapes using augmented reality. You can use it to identify what the mapped rocks are, read descriptions about them, and view the earth as it looked when they formed by turning back the continent positions. It spun out of some augmented reality work I was doing on my other side project app - Flyover Country, which tells you about the view from the airplane window.<p>I'm in Minnesota, so i haven't had a chance to test it on a huge amount of terrain (the best I've found is the Mississippi River gorge in the twin cities... I need a trip up north), so anyone that's somewhere with some more bumps: please help!).<p>It's stuck in the Apple review queue, but it's up on testflight for anyone interested. Google approved it quickly and it's fully out on android (links to both on the marketing site).<p>edit: got a report that testflight was asking for an install code. Seems like a testflight app issue, but dismissing it and installing as normal seems to work!
Show HN: OmniDiff – Opensource, super fast, content aware Git difftool, in Rust
I have been researching what the ultimate diff tool would look like, based on actual diffs in open source repositories.<p>OmniDiff can diff: code (25 languages), config, images, PDFs, archives, cursors, fonts...<p>It's fast. It can diff C++ files of tens of thousands of lines in under 100ms, on a 2023 ThinkPad T14s.<p>It's open source. AGPLv3.<p>It's based on real diffs. I looked at the 7400 repositories that are listed in Gentoo packages, sampled 2000 diffs and solved them manually to get a good understanding of what is needed, before putting together the diff pipelines for each content type. Incidentally, this also forms the basis of the CI pipeline, so the robustness of the codebase is quite high.<p>I'm still developing it. It's v0.3.0 at this time. But I have been using it daily as my git difftool for months now, and it works well.<p>AI Disclosure: Yes, I do use AI. No, I do not vibecode.
Show HN: OmniDiff – Opensource, super fast, content aware Git difftool, in Rust
I have been researching what the ultimate diff tool would look like, based on actual diffs in open source repositories.<p>OmniDiff can diff: code (25 languages), config, images, PDFs, archives, cursors, fonts...<p>It's fast. It can diff C++ files of tens of thousands of lines in under 100ms, on a 2023 ThinkPad T14s.<p>It's open source. AGPLv3.<p>It's based on real diffs. I looked at the 7400 repositories that are listed in Gentoo packages, sampled 2000 diffs and solved them manually to get a good understanding of what is needed, before putting together the diff pipelines for each content type. Incidentally, this also forms the basis of the CI pipeline, so the robustness of the codebase is quite high.<p>I'm still developing it. It's v0.3.0 at this time. But I have been using it daily as my git difftool for months now, and it works well.<p>AI Disclosure: Yes, I do use AI. No, I do not vibecode.
Show HN: HackerBooks – HackerTube, but for books mentioned on HN
Saw Karpathy's HackerTube and wanted the same thing for books. So I had Claude build one (Opus 5.5).<p>It's ~16.5k books that at least two different HN commenters brought up, from 2007 till now. Each book links back to the comments that mentioned it. Data is downloadable from the about page.<p>To be clear: Claude wrote almost all of it, including the data pipeline and the site. I mostly steered and made calls, bought the domain name, etc.<p>It counts mentions, so it's not perfect (praising a book and trashing it both count). If your favourite book is missing or matched to the wrong thing, tell me.
Show HN: HackerBooks – HackerTube, but for books mentioned on HN
Saw Karpathy's HackerTube and wanted the same thing for books. So I had Claude build one (Opus 5.5).<p>It's ~16.5k books that at least two different HN commenters brought up, from 2007 till now. Each book links back to the comments that mentioned it. Data is downloadable from the about page.<p>To be clear: Claude wrote almost all of it, including the data pipeline and the site. I mostly steered and made calls, bought the domain name, etc.<p>It counts mentions, so it's not perfect (praising a book and trashing it both count). If your favourite book is missing or matched to the wrong thing, tell me.
Show HN: NES Contra (1987) port (ASM –> C and then C –> WebAssembly)
Based on vermiceli's annotated disassembly of Contra: <a href="https://github.com/vermiceli/nes-contra-us" rel="nofollow">https://github.com/vermiceli/nes-contra-us</a>. Thanks for the great work.<p>I ported it in two steps. First to C: the game logic is translated routine by routine from the 6502 assembly, and checked frame by frame against the original in the Mesen emulator. Then the C code to WebAssembly so it runs in the browser.<p>The cherry on top: online multiplayer, so two players can play co-op over the internet.<p>Source: <a href="https://github.com/epoyraz/nes-contra-us" rel="nofollow">https://github.com/epoyraz/nes-contra-us</a>
Show HN: A USB-C LTE modem that gives my computer a phone line
Show HN: A USB-C LTE modem that gives my computer a phone line
Show HN: Ghostman – A fast, keyboard-driven TUI API client and webhook inspector
Show HN: Ghostman – A fast, keyboard-driven TUI API client and webhook inspector
Show HN: Ghostman – A fast, keyboard-driven TUI API client and webhook inspector