The best Hacker News stories from Show from the past day
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Show HN: LastShelf – an emergency map of your family's documents bills& contacts
After my father was diagnosed with Stage 3 kidney cancer, my family was thrown into a tailspin. Getting second opinions, planning surgery, ensuring insurance coverage, coping with the fear. It was a lot to process.<p>In the middle of dealing with all the medical logistics, I realized none of our family could answer if he:
- Had a medical directive?
- How to trigger his life insurance policy?
- Where is his will and who is the executor?
- What bank accounts and credit cards existed?
- What bills are not on auto-pay?
- When these bills due and how are they paid?<p>That wasn’t solved by password managers or budgeting apps. So I built it.<p>LastShelf: automatically discovers, documents and distributes a map of critical life documents, expenses & contacts in the event of an emergency. Register here: <a href="https://www.lastshelf.ai/" rel="nofollow">https://www.lastshelf.ai/</a><p>If you’ve lived through a similar crisis, I really want to hear what would have made the process easier.<p>Anyone who shares their feedback with me will get the first year free. Send a note to support [at] lastshelf.ai
Show HN: I mapped 8.5M research papers into an interactive atlas
When I read papers, I have to jump between multiple tabs to find the dataset, code, videos, peer reviews, and so on. I tried to fix this with this project.<p>It started as a project just for papers on arXiv, but after its initial success on Twitter (got like 1.9k views: the most I have gotten for a post), I have now expanded it to include other openly available papers from PubMed Central, bioRxiv, medRxiv, and eLife. These papers have been linked with their genes, proteins, diseases, drugs, clinical trials, 3D protein structures, code, and cited and similar papers.<p>This project now has four parts:<p>First, a map. I embedded nearly 8.5M papers (with SPECTER2), ran UMAP for 2D representation, and rendered them as a scatterplot. The dots can be clicked to see brief information about the papers, like an LLM TLDR, key findings, peer reviews, linked entities, and more. The clusters are also labeled, though you might have to zoom in.<p>Second, I built a detailed paper page for each paper. They give you the paper's full text, images, videos, peer reviews (from OpenReview), GitHub links, Hugging Face dataset/model links, clinical trials, genes, diseases, 3D protein structures, cited papers, and similar papers. You can also copy the whole page, including the full paper text and image URLs, as markdown for your LLM.<p>Third, I have released an extension so you can read all this information in your sidebar by clicking "open in Tomesphere" that shows up in arXiv, PMC, bioRxiv, Google Scholar, or medRxiv. I have tried to provide as much information as possible in the extension, though for things like viewing all the images or a 3D protein structure, you might still have to go to the paper page using the link provided in the extension.<p>Fourth, all this data is available for your LLM via MCP. The MCP does have a 50-query free limit (this jumps 10x with signup).<p>Note: this project is still in beta, so papers might have some mismatched information. I am rolling out feedback forms soon to improve the data quality. Thank you so much for taking the time to read this.
Show HN: FableCut – A browser video editor AI agents can drive (zero deps)
Show HN: Analog Watch
Show HN: Yamanote.fun – A complete soundscape for Tokyo's Yamanote line
After visiting Japan for the first time a decade ago I became completely enamoured with Tokyo's Yamanote Line railway loop. Particularly the sonic experience of it. Like so many others I fell in love with the charming departure melodies and enjoyed discovering experiences like Yamanot.es (<a href="https://news.ycombinator.com/item?id=45045307">https://news.ycombinator.com/item?id=45045307</a>) here on Hacker News when I returned home.<p>But it wasn't until my second trip to Tokyo that I truly appreciated how much the door chimes, on-board announcements and train noise were contributing to the rich soundscape that I loved.<p>I returned home and found myself playing YouTube videos of Yamanote Line journeys as I worked. The combination of sonics, ambience and softly spoken Japanese was incredibly soothing to me.<p>But these recordings were often incomplete, poorly captured or out of date, and I wanted something far more comprehensive.<p>So I gathered up all of the constituent parts from Reddit threads, YouTube videos and Japanese fan sites, and set about recreating the experience of riding the Yamanote Line in Logic Pro X. Melody, door chimes and announcement, all stitched together under a bed of train noise and ambience.<p>I turned those soundscapes into an Alexa Skill (<a href="https://www.amazon.co.uk/Paul-Jackson-Yamanote-Line/dp/B07S18QRMV" rel="nofollow">https://www.amazon.co.uk/Paul-Jackson-Yamanote-Line/dp/B07S1...</a>) in 2019 and began to think about a companion website to share the soundscapes with a wider audience.<p>Seven years later and that website is Yamanote.fun: <a href="https://www.yamanote.fun/" rel="nofollow">https://www.yamanote.fun/</a>.<p>It's a small installable web app that plays the soundscapes like a playlist. All 30 stations and in both directions, since the inner and outer loops use different melodies. You can skip forward or back a station, and there's a scrub bar broken into melody / chime / ambience / announcement so you can jump straight to the bit you want. Each station has its own shareable link (yamanote.fun/jy13-ikebukuro-inner) that unfurls with the right station name and artwork when you share it.<p>It's a progressive web app too, so you can add it to your home screen and it behaves like a native app. There's an option to offline the audio too.<p>Under the hood it's relatively basic stuff: plain HTML, CSS & JS, audio served from Cloudflare R2 and the site hosted on Netlify. I was impressed to see how far I could get with the free tiers of these services. I designed the whole thing in Figma (I'm a Product Designer) and used Claude Code to architect and deliver the polished UI, PWA plumbing, offline caching and share-link infrastructure.<p>I would love feedback, particularly from anyone who's ridden the real thing.
Show HN: Getting GLM 5.2 running on my slow computer
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me.<p>But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility.<p>I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context. How it responds in int4 and whether the quality is maintained or not. Until I got to the point, on my computer with 32GB of RAM, I was able to communicate with GLM 5.2 with times that, of course, aren't high in cold start, but even then, we're talking about 0.1 tok/s, but that wasn't important to me. The important thing was the journey to reach this goal. I just wanted it to work at all costs, even slowly.<p>So I created Colibrì, which was born from a very simple idea, to be honest, but tested in every way, where a 744B Mixture-of-Experts model activates only ~40B parameters per token—and only ~11 GB of those change from token to token (the routed experts). So:<p>The dense part (attention, shared experts, embeddings—~17B params) stays resident in RAM at int4 (~9.9 GB); The 21,504 routed experts (75 MoE layers × 256 experts + the MTP head, ~19 MB each at int4) live on disk (~370 GB) and are streamed on demand, with a per-layer LRU cache, an optional pinned hot-store, and the OS page cache as a free L2.<p>The engine is a single C file (c/glm.c, ~1,300 lines) plus small headers. No BLAS, no Python at runtime, no GPU.No GPU or serious hardware because I don't have that hardware so I can't test it on hardware that is more powerful than my computer.Colibrì is a one-person project, written and tested entirely on a 12-core laptop with 25 GB of RAM — the numbers above are the ceiling of what I can measure at home.<p>Any feedback is welcome! (and if anyone wanted to participate in the project I would be delighted)<p>Repo: <a href="https://github.com/JustVugg/colibri" rel="nofollow">https://github.com/JustVugg/colibri</a>
Show HN: 18 Words
Show HN: Chiptune Radio
I built a chiptune song generator and its broadcasting algorithmically generated chiptune music.
Show HN: Fortress – a stealth Chromium so your agents stop getting blocked
Show HN: Neil the Seal Game
Neil the seal now has a game to destroy Battery Point.
Show HN: Neil the Seal Game
Neil the seal now has a game to destroy Battery Point.
Show HN: Follow London Trains in 3D
deck.gl based visualiser of the TFL Api + National Rails to be able to track (with minimal drift) a train along the way in London and to the nearest airports. If you pick one from any platform in <a href="https://nexttrain.london" rel="nofollow">https://nexttrain.london</a> basically you can share your train journey along the rails. Build to test Cloudflare workers and their infra along with deck.gl performance that is incredible in my opinion.
Show HN: Follow London Trains in 3D
deck.gl based visualiser of the TFL Api + National Rails to be able to track (with minimal drift) a train along the way in London and to the nearest airports. If you pick one from any platform in <a href="https://nexttrain.london" rel="nofollow">https://nexttrain.london</a> basically you can share your train journey along the rails. Build to test Cloudflare workers and their infra along with deck.gl performance that is incredible in my opinion.
Show HN: Microsoft releases Flint, a visualization language for AI agents
Data visualizations are the bridge between user and data.<p>But building AI agents that can generate visualizations reliably can be very tricky:<p>- simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults;
- complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability<p>We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple high-level specs. The result is also very human understandable and adaptable. Flint powers data formulator for generating visualizations (another open source project from microsoft <a href="https://data-formulator.ai/" rel="nofollow">https://data-formulator.ai/</a>).<p>Flint is available open source, and we built a MCP server that you can directly plug flint in your favorite agent app to play with data.
Show HN: Microsoft releases Flint, a visualization language for AI agents
Data visualizations are the bridge between user and data.<p>But building AI agents that can generate visualizations reliably can be very tricky:<p>- simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults;
- complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability<p>We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple high-level specs. The result is also very human understandable and adaptable. Flint powers data formulator for generating visualizations (another open source project from microsoft <a href="https://data-formulator.ai/" rel="nofollow">https://data-formulator.ai/</a>).<p>Flint is available open source, and we built a MCP server that you can directly plug flint in your favorite agent app to play with data.
Show HN: InstantVideos.org – short documentaries in ~30 seconds
Hiya! So I've been playing around with having Claude make videos for a bit now even had some success posting the results to TikTok (and setup a whole pipeline so Claude can generate and post autonomously). With the release of Nano Banana 2 Lite, I was curious show fast I could make the generation, so last night I gave it a whirl and got down to around 30s for short-form video.<p>It uses GLM-5.2 fast via Fireworks to generate the scripts and image prompts and, like I said, Nano Banana 2 Lite for the images, gpt-4o-mini-tts for the narration, and ffmpeg to string it all together and add the Ken Burns zoom effect (which still has a shake I haven't been able to get rid of). The video compilation proved to be the blocker once the rest was in place, but I was able to speed that up by putting it on a 64 vCPU EC2.<p>The cost might be the most interesting aspect as the short form videos tend to be about 25 cents. Almost 90% of that is the images, which are 3.336 cents a piece. Of course, running the big 64 core EC2 to allow for the creation isn't cheap.<p>It seems like on-demand AI video is coming, and I thought this was an interesting demo of how close it might be in at least one narrow video domain.
Show HN: Shellular – run Claude Code, Codex, Pi from your phone
Show HN: Docx-CLI: agents read/edit Word docs using 1/2 the time and tokens
Show HN: Docx-CLI: agents read/edit Word docs using 1/2 the time and tokens
Show HN: PostgreSQL performance and cost across 23 EC2 instance types
Hey! I'm Andrei.<p>I got frustrated by how people tend to build overcomplicated backend systems, being "motivated" by big tech case studies and popular books.<p>So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction.<p>Here I want to present one of the tools – Sizing tool for PostgreSQL. I've benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instances can give you. And I've built a tool to visualize this data, which I welcome you to explore.<p>So, you can put your usual input parameters, like needed RPS and disk size as input, and find out which instance will be the most cost-efficient for your needs.<p>You can read about the methodology here: <a href="https://postgres.saneengineer.com/about" rel="nofollow">https://postgres.saneengineer.com/about</a><p>I've tested one workload – mixed 90/10 read/write, and only selected configurations. But it is extensible, and I (and you – benchmark is open source: <a href="https://github.com/anivaniuk/sanebench" rel="nofollow">https://github.com/anivaniuk/sanebench</a>) can run more configurations to have more data represented.<p>Does it look interesting? What workload should I benchmark next?