Product Catalog
468 products tracked
Artificial Analysis tool to create custom benchmarks for any use case
Show HN: Artificial Analysis tool to create custom benchmarks for any use case
Stackdome
Hello Hacker News, this is Ashish. We started building Stackdome in 2024 as a hobby project. The idea was to package the tooling in the CNCF/cloud-native ecosystem into an opinionated platform, so that developers can get a well engineered platform without learning Kubernetes YAMLology or a dozen other projects from the CNCF landscape. Railway and Render did this well for their hosted products. I wanted to bring that level of polish and DX to something you can self-host. Stackdome uses Kubernetes underneath, but it's completely transparent to users. (I have some ideas for an expert mode that exposes more of the bells and whistles.) My day job involves working with cloud native technologies, and it made me appreciate how much is already solved by Kubernetes and the tooling around it. If you are already running Kubernetes, Stackdome is something you can adopt incrementally. (I have some ideas around adopting already-running workloads.) Features: - First class support for multi-service applications, shown on a canvas in the UI (inspired by Railway). You can edit, wire up, and create resources from the canvas. - First class support for multiple clusters. Stackdome acts as the hub and places workloads across clusters (hub and spoke). - Self hostable, from a small VPS to a large cluster. - Managed Postgres with WAL archiving, HA, backups, and PITR (CNPG underneath). - A release system for the whole stack. A release targets everything, and you can roll it back if something breaks. - Source to image, with an in cluster registry (Zot). - Disposable preview environments, which can also be multi-service. - Declarative apps via a Stackfile, deployed with the CLI. Website: https://stackdome.com Docs: https://docs.stackdome.com Repo: https://github.com/Stackdome/Stackdome Set it up on a vps: `curl -fsSL https://get.stackdome.com/install.sh | sudo sh` Or Try it on our cloud, no credit card and no compute to bring: https://cloud.stackdome.com Happy to hear your feedback and questions.
I asked Claude to write DDIA as a live comic
Show HN: I asked Claude to write DDIA as a live comic
UTC Time
Show HN: UTC Time - live clock, ISO 8601, Unix timestamp
Ballet
Show HN: Ballet – Workflow automation that writes integrations against any API
Programmable timer web app (for gym workouts or stretching sessions)
Over the last couple of months, I’ve been building a timer web app for myself that I use for workout and stretching sessions. My main use-case is gym routines that consist of repeatable sequences, e.g. where you are holding certain positions for a set time (rinse and repeat). The app counts down the program, beeps, and reads the activities out loud. Two things (I suppose) are special about it: - The timers are “programmable”, so you can freely express your own routines and procedures in a declarative notation. - The app is all static (no backend): the entire program is encoded in the URL and can be bookmarked or shared/transferred via QR-code. You can check it out at https://timer.jotaen.net, optionally with a demo program pre-loaded: https://timer.jotaen.net/#demo. Source code is at https://github.com/jotaen/timer. I’ve also written up a small behind-the-scenes on my blog: https://www.jotaen.net/SAKxq
What US households pay for electricity and gas, by state, since 2001
Show HN: What US households pay for electricity and gas, by state, since 2001
Meteor shower, planet alignment, eclipse kit
I made this little walkthrough for finding the visible planets tonight and a meteor shower shooting start counter. Plus a little model of the solar system to try to wrap my head around what's going on. It's all based on your location if you provide it. lifesprites.com/solar.html doesn't require account creation. The rest does. It's free, but you need an account.
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?
Woxi
Woxi is an interpreter for the Wolfram Language written in Rust. It comes with Woxi Studio, a Mathematica-like GUI built with iced, but you can also use Woxi through a CLI, Jupyter kernel, Python package, npm package, or WASM module. Compared with wolframscript / Mathematica, the main differences are: - Free and open source - Very fast startup - Typically milliseconds rather than seconds for the Wolfram kernel, making Woxi practical for shell scripts, one-liners, and other short-lived processes - Embeddable - It can run in a browser via WASM or be embedded into another application as a scripting language A more detailed comparison with Mathematica is available here: https://woxi.ad-si.com/docs/comparison/mathematica/. Conformance is ensured with ~26'000 unit tests and ~900 .wls script snapshot tests. The current focus is on fixing remaining edge cases, improving performance, and growing the community. If you use the Wolfram Language, I'd be particularly interested in feedback on compatibility and missing functionality. Contributions and bug reports are also very welcome: https://github.com/ad-si/Woxi
Trykeet
Hi HN! We’re Zack and Tommy the Co-Founders of Keet (https://trykeet.com). We are building a mobile app that generates courses on any topic, with short videos for explanation and games for reinforcement. Courses mirror a real curriculum to help you learn over an extended period of time. Tommy and I met in linear algebra class in college and spent the next 4 years taking classes together. Our friendship was formed around learning new things. In school, someone else designs the curriculum, delivers the content, and writes the assessments. All you have to do is show up. Learning without this structure was frustrating, and there was a lot of friction to get started. Zack has a coffee obsession and struggled to assemble youtube videos, conversations with ChatGPT, and the books he was reading into a coherent understanding of all of the different variables that go into brewing a cup of coffee. His attempts gave him the freedom to follow his curiosity, but the instructional design was difficult. Every piece of content either presumed some prerequisite knowledge or none at all. Keet is our attempt at providing the autonomy to teach yourself anything while adding a structure conducive to learning. It finds a custom starting point and sequences lessons in a logical order. We have found Keet most useful in the following scenarios: - You have a subject matter interest that you enjoy passively learning about. (i.e. you really enjoy learning about medieval history and generate courses on medieval engineering) - You want to explore a niche topic of a subject area you already know a lot about. (i.e. You know a lot about biology but want to explore how migratory animals sense Earth’s magnetic field.) - You see a really niche topic get mentioned somewhere you want to explore more (i.e History of Penny Universities, Double Entry Book keeping or Robert Moses and the creation of the BQE) When you create a course we ask some questions about how difficult it should be, how much depth the course should go into, and a few optional intent questions to understand what content you want to learn about. In the future, we’d like to have a global prerequisite map so that we can understand what each user already knows, so we can generate tailored courses to someone’s prerequisite knowledge. If someone majored in Computer Science, for example, their course should look pretty different to someone who might have no background in STEM. After understanding your goals and intent, we categorize the course based on Biglan categories. Biglan categories have four quadrants: - Hard–Pure (e.g., mathematics, theoretical physics) - Hard-Applied (e.g., engineering, and applied sciences) - Soft–Pure (e.g., history, literature, philosophy) - Soft-Applied (e.g., policy, management, education, social work) We use these categories to adjust instructions to better suit the topic. One instance of this is when searching for examples. A hard-pure course will look for worked problems, proofs, and real world cases, whereas soft-pure courses use primary source narratives and contrasting perspectives. We tried building this as a website and then as a mobile app with just text. The website was bad because we couldn’t engage with the content from anywhere, and when we did engage, the text was boring and couldn’t add clarity to complex ideas. Vox and 3B1B videos made complex ideas accessible, which inspired us to move towards video based lessons. There was never one aha moment that made it work. Over the course of the past year, we’ve been using and iterating on the product until we enjoyed using it. We have two types of explainer videos that are built using Manim (https://www.manim.community/) and Remotion (https://www.remotion.dev/). Manim is used for videos that require math visualizations while the Remotion videos are intended to animate processes and show primary source material to mimic something like a Vox video. We are also building more engaging ways to complete assessments via custom activities. For example, in a genetics course, learners might interact with a Watson-Crick DNA model rather than answering a multiple choice question about base pairs. You can download the Test flight beta (https://testflight.apple.com/join/wkWW2enA) today. We are giving 3 free course generations to every user. You can use the code “KEETHN” on the waitlist screen. When you download the app, try to generate a course on a niche interest/something very specific. The coolest courses that we’ve seen are generated in those categories. [ Notes ] - Personalization is still early. Today, Keet mostly adapts around the topic and course goal; we want it to adapt much more around a learner’s background, pace, and weak spots. - Course generation is slow. We made a deliberate trade off to sacrifice generation time in order to gain higher quality courses, since users are going to be taking these courses for an extended period of time. - We are actively working to improve reinforcement quality. It is easy to generate quizzes, but much harder to generate interactions drive home the concept. [ Pricing ] We plan to have a monthly subscription that gives users credits they can use to generate courses. This will be a model similar to Suno (https://suno.com/).
Alchemize
Hey HN, we’re Robert and Sam. We’re building Alchemize, a code review platform that simplifies PRs to help you ship faster. Here’s a demo video: https://www.tella.tv/video/simplify-pr-reviews-with-alchemiz... Sample to try: https://app.tryalchemize.com/example Agentic coding has 10x’d code output. Teams are opening larger PRs more frequently, but the current tools don’t support this new coding paradigm. GitHub still presents files without structure. As engineers reviewing these massive diffs more frequently, we spent hours painstakingly reconstructing where changes began, mapping out data flows, and figuring out where human judgement is actually needed. Instead of adding another bug bot or just summarizing the PR, Alchemize analyzes diffs and pulls in prompting sessions to help reveal the author’s (human and agents) intent. PRs are broken down into smaller, manageable chunks that highlight exactly where the reviewer needs to jump in. After authorizing your GitHub account, Alchemize has a two way sync so that you never have to review an unstructured PR on GitHub again. We’ve been thinking a lot about whether helping human reviewers is the future of development, or if the trajectory of code review is to review less and less until it’s 0. Would love for y’all to give this a try and hear your thoughts.