Product Catalog
141 products tracked
Interactive component to map fibre breaks (OTDR)
Added an OTDR trace component as the latest in my component library for datacentre/network apps. Mainly for my own use in rackout.
Wordle for Metro Stations
One station to find per day, 2 or 3 gamemodes depending on the city (if your city is not live yet, or you can't find it in the selector, try https://dailymetro.live/cityname/mode/classic ) Like https://dailymetro.live/sf/mode/classic for San Francisco Got huge traction in France, so I thought maybe you'll like it here too :) Made with Claude code but still took at least 50-100h of dev/design/taking user feedbacks ahahah Hope you like it
A public AI whose memory is shared across all users
Show HN: A public AI whose memory is shared across all users
APIMart: Discounted AI API Aggregator for GPT-5, Sora 2
Show HN: APIMart: Discounted AI API Aggregator for GPT-5, Sora 2
Is AI Dumber Today? An index of AI model experience from user's opinion
Show HN: Is AI Dumber Today? An index of AI model experience from user's opinion
Artificial Analysis tool to create custom benchmarks for any use case
Show HN: Artificial Analysis tool to create custom benchmarks for any use case
Discoveredmaterials
Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ). We build AI agents that discover new materials for the semiconductor industry. GPUs today have a heat problem. Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat. This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation. The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it. For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board). However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation. Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates. However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death". At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips. We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties. This was surprising to us - it would generally take a PhD student a couple of weeks of work to discover the kind of materials that these models find over an 8 hour run! However, computational discovery is the easy part. A material discovery is only valid if the material can be made and tested in a lab (As an example, graphene’s properties were predicted in 1947 but it was made for the first time in 2004). Today’s models are not good at coming up with synthesis recipes to make materials in a lab. Even if they do get better at it, we're uncertain about how much that will help - making a new material is a highly empirical process involving trial and error over many experiments. Human experts themselves cannot "one-shot" the task, but we expect that a highly capable model will reduce the number of experimental iterations required to make a new material. We’ve seen some evidence of this over the 3 months of our Y Combinator batch - we simulated, synthesized and tested thermal interface materials (TIMs) that match the performance of TIMs the world's largest chemical companies have guarded as trade secrets for over 20 years. We’re releasing hundreds of hundreds of new materials discovered by frontier AI models, as well as our benchmark which measures model ability on material discovery here (also linked in the thread url): https://discoveredmaterials.com/research. It covers what we discuss above, as well as a variety of strange behavior that we observe from the models, such as Claude's propensity to reward hack or GPT-5.6 occasionally losing its mind after ~50M tokens. Our business model: We aim to license and sell IP on the materials we discover, as well as the IP on how to make these materials. We're also exploring an alternate business model where we sell the harness+tools we use to discover materials to semiconductor and chemical companies, allowing them to discover materials on their own. We're leaning towards the latter to start, but we expect that we'll do both in the long run. Our backstory: Akash has a PhD in Material Science from Stanford University, and has spent the last 11 years studying new materials for semiconductor chips. His work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025. Advaith studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs. We are very interested in your opinion! The semiconductor industry is quite secretive, and your thoughts on the roadmap of the industry or the materials we should go after would be very helpful. We would also love to hear from people who have run experiments in labs - what can we learn from your experience doing empirical science?
Voice driven murder mystery, Interview AI suspects with your voice
Hey HN! I'm excited to show off this really fun project I put together. I originally built this project 2-3 years ago, AI was already booming at the time, however voice AI agents were still very early. I loved my proof of concept at the time, but wasn't quite happy with it. I recently had the desire to check out the tech again, and know many of you will be interested. Interviews are speech to speech with OpenAI's gpt-realtime-2.1 over WebRTC. This model is... expensive, and because of that, I have to add some amount of restrictions, conversations are tied to a authenticated Clerk user id. I have also added a 30 minute timer because well, I really don't want to go broke while I sleep tonight. Each suspect has a tool they call when you make a direct accusation. It captures who you accused and a faithful list of the evidence you actually stated. A separate gpt-5-mini judge then decides which of the case's required evidence facts you genuinely presented. Paraphrasing counts, vague suspicion and fishing don't. The rest is Next.js, MongoDB, and Clerk. Let me know whether the suspects hold up under a real interrogation.
Alphabet Soup, a multiplayer game, build the longest word to win
I've been working on a simple multiplayer word game called Alphabet Soup. It's a bit like NYT Spelling Bee, except it's multiplayer and only your best (longest) word counts toward your score. So, instead of trying to find as many words as possible, the game is more about finding one unusually good word. You can play with friends in a private match, or, you can join a public Arena match to play against other players who are online. If no one else is around, you'll be matched with a bot. The game works well with just 2 players, but, also works with bigger groups since the game isn't turn-based. Would love to hear any thoughts or questions :)
Hope, 1-on-1 AI Tutor for kids Grades 2-10 learning math
Hey HN! My friend and I have been working for the last 3 months on HOPE, a platform that provides students with 1 on 1 math support to improve their confidence and teach them by doing and asking questions. We would really appreciate anyone who's down to try it out with their kids and give their thoughts.
Science for Kids
There’s a really good kids’ science magazine called Oyla, but it’s aimed at kids 12+ I subscribed a couple of years ago thinking that it would be fine for my son (then 8yo, but an advanced reader) but, even now (10yo), the articles are too dense for him and don’t hold his interest. Last year, the same company launched ‘Oyla Junior’ aimed at younger kids ~8yo. But I read a couple of the articles and I think they’re too oversimplified. I wanted something in between, but didn’t find anything suitable online. So I used AI to create something, and did a bunch of iterations to tune the sentence length, the flow of the articles, how much info is introduced in each paragraph etc. I still want to make it better, but it’s already good enough for my son to read.
Silo
Show HN: Silo – S3-compatible object storage, a maintained fork of MinIO