Maptoolkit.org

CA

Capgo

Мгновенные обновления для Capacitor-приложений. Выпускайте исправления за минуты, а не недели. Отправляйте OTA-обновления пользователям без задержек App Store.

OP

OpenAlternative

OpenAlternative — каталог open-source альтернатив проприетарному софту. На сайте собраны проекты из разных категорий с информацией о возможностях, стеке технологий и метриках GitHub. Платформа монетизируется через платные размещения и партнёрские ссылки.

MI

Minilith

I enjoy the constraints of retro development. While building games in PICO-8, I took a interest in the way it packs data into the virtual cartridge. I dug into steganography and started thinking: could I apply the same logic to a content management system? Do we really need all the bloat just to host a micro blog? Minilith is a database free CMS where the entire state (posts, pages, and settings) are compressed and stored directly in the pixels of a 256xd256 PNG file. Minilith has zero dependencies. There is no Node, no bundlers, and no external DBs. It relies entirely on browser APIs to compress, secure, and render a site directly from the image. The Minilith Architectural Lifecycle Instead of a traditional backend and database, the flow looks like this: 1. Payload Creation: The editor data (JSON) is signed with a private key and compressed using the browser's native deflate algorithms. 2. Injection: The compressed bits are injected into the image's RGB channels using steganography. A raw tEXt chunk is injected right before the IEND marker to wrap the extraction script, turning the image into an executable polyglot. 3. Distribution: The result is a single image file. It can either be pushed directly to Cloudflare KV or downloaded locally. 4. Extraction & Rendering: A client side loader fetches the image, draws it to a hidden canvas, extracts the bits, verifies the signature and visual fingerprint, and runs the decompressed payload. Since the entire database is just a picture, deployment and portability are extremely simple: - Cloudflare Edge: Publish directly from the editor to Cloudflare KV, overwriting a single key-value pair for instant updates. - Self Hosting: Generate a .zip archive from the editor and host on any basic web server. - Offline Portability: The editor utilizes service workers. Once you have visited the editor once it is cached. You can view, edit, and generate new images completely offline. The obvious flaw is aggressive image processing and optimization destroying the polyglot logic and data. (Though fun fact: texting the raw image over iMessage preserves the payload) The code is experimental and the linked post is my architectural breakdown of the process. I'd love feedback and to hear your thoughts and ideas.

OE

Open-Source eInk Bike Computer

Hey all, i just launched my Eink Bike computer project and think it is cool. Another tidbit, in the crazy things that AI has done... It has helped create a ANT (common sensor wireless protocol used in workout/biking) implementation for ESP32 by messing around with undocumented registers: https://github.com/RaemondBW/esp32-ant

MI

Mireye

Hi HN, I'm Ansh, founder of Mireye. I'm building the infrastructure AI agents use to make decisions about physical places: data, enrichment, tools, and signals for any US location, behind one API and MCP server. Here's a demo video: https://youtu.be/haqO6UbUqU0 To try it, paste https://www.mireye.com/skills.md into your agent, and grab a free key at mireye.com (5,000 credits, no card). Docs are at https://docs.mireye.ai. The fastest way in is to ask the API any question about any US location. Test it on a place you know, cold and grade it against what you know. Before Mireye I was building construction agents and hit this wall myself: my agent could reason about anything online but knew nothing about the ground under it. Then a Fortune 500 insurer told me their engineers had given up on underwriting agents for the same reason. Frontier models keep hallucinating when asked a specific question about a specific place. My first product was a niche site-screening app. Customers tested it on places they knew and the answers held up, but nobody cared about the app. They wanted the engine underneath. Usage agreed: 311 of the 317 fields in the catalog get queried and no use case dominates. So I killed the app and started building the infrastructure instead. Mireye is not a dataset with an API on top, because facts alone are not a decision. An agent runs the whole job through it: cited facts, a bare address enriched into owner, acreage, structures, and nearby power, tools for the operations models get wrong, and signals when something changes, like a rezoning filing. Data, enrichment, tools, signals. The tools came from watching agents fail. We build agents on our own infra, and the same things kept breaking: an agent would eyeball a distance instead of computing it, grab the wrong parcel for an address, or burn its whole budget halfway through a batch. Each failure became something an agent can call: deterministic geometry and drive-time tools, parcel resolution, a quote endpoint that prices a job before it runs, and skills that package whole workflows like screening a site or underwriting an address. The hard part surprised me. Every source has to be gathered (sometimes county by county, in whatever format each county publishes), normalized into one schema, contracted (where it comes from, what each value means, how often it refreshes), and then kept fresh forever. We run that loop for 366 fields today, served multi-tenant from one index, and every source fought back differently. Maryland publishes a dataset literally titled "Hidden Property Owner Names." I filed public records requests in North Carolina because nobody indexes sewer mains. The deeper problem is meaning. Two counties publish a field with the same name and it means different things. And the most dangerous value is null. Does it mean "no flood zone here" or "this county never mapped floods"? Put a model in front of that gap and it fills the silence with a plausible number. So we type absence. Every field returns ok, absent, or failed. Customers have told me the refusals are why they trust it. The newest piece is on-demand indexing. Ask for a field we don't have and a long-running agent researches sources, collects the data, tests it against ground truth, and indexes it, usually within a day. I'll take a few field requests from this thread and report back with what it built. People have built things I didn't plan for: insurance teams screening portfolios for flood, wind, and wildfire, a proptech cleaning messy listing addresses through enrichment, a wellness brand scoring street corners for poster spots, a robotics company sourcing warehouses, data center site selection, drone deployment planning, school bus routes for a city, signals for human trafficking investigations. The mission is to index every inch of the earth and make it as queryable as the web. Pricing is public: the free tier is 5,000 credits a month, $19/month for 25,000, $99/month for 120,000, custom for enterprise. I'd love to hear where the agents you're building touch the physical world. And I'd love it more if you run Mireye on a place you know and tell me what we got wrong.