VeerHost
Hey HN, We just launched $1/month web hosting for the first year, with only a limited number of spots available. 20GB NVMe, SSL, malware protection, WordPress support, and one website.
Tearline
Show HN: Tearline – wrap any HTML in one tag, get a thermal receipt and a PNG
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