Product Catalog
465 products tracked
We put voice agent on our website, learned retrieval isn't bottleneck
Show HN: We put voice agent on our website, learned retrieval isn't bottleneck
Exploiting Slack's video embeds to achieve E2EE communication
Show HN: Exploiting Slack's video embeds to achieve E2EE communication
machine0
Hi HN! Excited to launch machine0, a CLI that makes it easy to create, provision and snapshot persistent NixOS (& Ubuntu) VMs. You can think of machine0 as a modern VPS provider. VMs stay on unless switched-off (with 99.99% uptime), they have static IPs and HTTPS endpoints, 1-60 vCPU, up to 240GB RAM and optionally GPUs. The CLI provides commands to manage lifecycle, snapshots and also provision the VMs using Nix flakes or Ansible playbooks. VMs are priced by the minute of usage. What makes machine0 unique is that it has first class support for NixOS! In a nutshell, NixOS lets you define your entire OS as code (think Terraform but for your Linux). A flake declares your system state (packages, services, firewall rules, users...) and pins all dependencies via a lockfile. Given the same flake.nix and flake.lock, `nixos-rebuild switch` always produces the exact same system. The NixOS ecosystem is mature, and flakes are expressive: at the system level you can define packages, what's in /etc, firewall rules, users & groups etc. At the user level, you can define your shell, aliases, tmux and vim config. Having your entire environment defined as code makes it easy to audit what's installed and how things are set up. You can rollback by reverting the last commit. And agents can write the code for you and test it against disposable machine0 VMs. If you'd like to dive right in, these commands will get you started: npm install -g @machine0/cli machine0 new my-vm --image nixos-25-11 # create a new nixos VM machine0 provision my-vm ./flake#my-profile # provision it using a nix flake machine0 ssh my-vm # ssh in machine0 stop my-vm # stop the VM machine0 images new my-vm my-snapshot # create a snapshot machine0 new my-clone --image my-snapshot # create a new VM from the snapshot - Demo of installation + NixOS provisioning via Claude Code: https://www.youtube.com/watch?v=RT8N0_e3Vfg - Documentation: https://docs.machine0.io/introduction/overview - machine0 NixOS flakes: https://github.com/fdmtl/machine0-nixos If you're in the habit of using VMs, or want to know what the NixOS fuss is about, would love for you to give machine0 a try!
Philosophy for Kids
Sometimes my son asks me 'why' questions that could be answered well by a kid-friendly philosophy article. But I don't know where to find those, so I ask Claude or ChatGPT, and have a specific workflow for getting the type of output I want. I figured other people might find those AI-generated articles helpful, so I put them here: https://philosophy.ocaho.com/ There's a search box at the top.
I built 80 mini-games using Fable before it was shut down
Dear Hacker News, I'm kindly asking for your participation in the open beta for my AI-managed mini-games website. Thank you in advance! For a limited time window, I'm setting the all-free feature flag to true. I hope you have a lot of fun exploring the AI's sense for games! Here and there, I tweaked it to help with visual consistency. I would be deeply grateful if you opted into analytics. $2,300 in API tokens... Cheers!
Brightdeck
Show HN: Brightdeck – an OOXML-compatible AI presentation maker
2 Weeks of Hallucinate
Show HN: 2 Weeks of Hallucinate – The Photo Gallery
Bitboard
We’re Connor and Ambar from BitBoard (https://bitboard.work). BitBoard is an agentic analytics workspace. We give you the infrastructure and visualization layer to analyze data with AI. Today, we’re launching dashboards that you and your agents can work on together. You can connect your coding agent or AI chat to BitBoard and build live reporting. Here’s a demo: https://www.youtube.com/watch?v=HPl0K565a7c. AI tools treat data analysis as ephemeral, making it hard to report or collaborate. Legacy BI tools weren’t intended for AI users, so they bolt on chatbots and can’t offer meaningful control to your agents. Software can now make far more of a business legible than BI ever could, but neither legacy BI nor chat bots are built to handle it. Our original product was AI agents for administrative tasks in healthcare (https://news.ycombinator.com/item?id=44237769), but customers kept pulling us toward their data analysis problems: queries scattered across disparate sources, spreadsheets floating everywhere. We kept building tooling for addressing that, and at a certain point those tools were becoming our product. We ran into several problems. Agents made bad inferences because they had no context on the business. They couldn't be trusted to make decisions because nothing checked their work. And anything one agent or one person figured out was invisible to everyone else. In BitBoard, humans and agents interact with the same data primitives but get tools designed for their own work. We’re building dashboards to make the human reading experience better. These dashboards progressively use intelligence - starting from code or SQL queries and leading to full embedded apps. Humans and agents will need to agree on methods to interpret data, so we’re letting both contribute to canonical sources, entities, and measures (using your favorite semantic model or ours). Every answer comes with provenance, and the same call with the same parameters returns the same number. Looking ahead, these shared primitives let long-running agents operate inside a business, and we're building those agents too. An agent needs a measurable goal and a way to verify its work. BitBoard gives it both. The agent takes a problem like a metric drifting or a funnel leaking and figures out what to do next. Its work becomes datasets, dashboards, and traces that the team can observe and sign off on. Technically, we’re building a collaboration engine with isomorphic updates for humans and AI, columnar analysis (we use DuckDB and Apache Arrow), grounding and verification infrastructure, and enabling long running tasks with agent containers and traces. For agentic work we’re big fans of applying LLM judgement to discover problems, and then generating deterministic software to automate them. Try it out at https://app.bitboard.work. (We require an email so we can set up your account). We’re excited about how data analysis and science can change in the age of LLMs, and welcome all your thoughts!
StackScope
Hey all, I built StackScope, a crawler/catalogue that looks at new product launches and shows what they were built with. It watches launches from Product Hunt, Show HN, and PeerPush, then crawls the public site behind each one. The goal is to show what people actually launched with: hosting, frameworks, analytics, DNS, security headers, legal pages, AI-builder signals, and other public clues. I started building it because most stack-detection sites look at the web as a whole. I was more interested in the current indie launch scene: what people are choosing right now, at the point they first put something in public. A few implementation details: it runs on .NET, uses Playwright for rendered pages, and has a first-party fingerprint catalogue rather than one copied from Wappalyzer/etc. robots.txt is honoured, and the bot identifies itself. Frustratingly, I am still waiting for verified bot status from Cloudflare and currently that knocks out about 10% of all sites. There is also a private readiness check: paste a URL, get the same style of report, fix things, and recrawl. No account or email needed. I'd be interested in feedback on the usefulness of this, the methodology, and any obvious false positives. Jonathan.
Theintercept
Scott Pelley Shows How Legacy Media Got It Wrong – and Bari Weiss Made It Worse
Stillwind
We’ve spent the last couple of months building Stillwind Search, a search engine for electronic components that helps users find parts that fit even the most complex set of specifications. After talking to the people that actually build PCBs we found out that finding the exact part you are looking for, is consuming enormous amounts of times, is very tedious and then often doesn’t yield the best results. So we tried to cut down this search time by just requiring a (broad) description of the specifications and we find the correct part in minutes, not hours. This is possible through our own database of parts and their properties. We used LLMs to extract every parameter about a part into >1k schemas, collectively covering more than 130k properties. This depth of properties could no longer be visualized, so the database is queried interactively by an AI agent (Sonnet 4.6) to find the needle in the haystack of parts. Before results are shown, we use another model to verify the data (that’s why it might take a moment before the first results appear). We currently have almost all microcontrollers, sensors, and other advanced ICs on the market, as well as a wide selection of passives and generic parts. We are working on adding more parts and are more than happy to take suggestions. I know that folks on HN like technical details on how this works, so let me give a short overview: Frontend: SvelteKit + Cloudflare Workers + Hyperdrive Backend: PostgreSQL 18 (with io_uring) database, with extensions on NVMe drives hosted on a beefy server. We appreciate all feedback and are happy to answer any questions :) Btw: We are already working on a way that you can search combinations of parts, finding the optimal combination of parts.
Workplane
A friend and I built this as a side project to help us collaborate on files with our agents. Claude / Codex kept outputting .md and .html files which are great until we needed to share them, so we built this small website to help with that. Agent can either use an HTTP + Skill or an MCP which also uses MCP Apps to add widgets to Claude Desktop / Mobile chat. Would love any feedback and hopefully this helps someone else as it did us!