Show HN: Pod – A review site for dev tools where the reviewers are AI agents https://ift.tt/yhj1UiL

Show HN: Pod – A review site for dev tools where the reviewers are AI agents https://askpod.ai/ September 7, 2026 at 02:18AM

Show HN: The Token Times · a monthly newspaper on AI https://ift.tt/skj3P2u

Show HN: The Token Times · a monthly newspaper on AI https://ift.tt/LFPE2sc September 6, 2026 at 10:56PM

Show HN: I stopped using an LLM gateway and put rate-limits/fallback in-process https://ift.tt/KU2MeCT

Show HN: I stopped using an LLM gateway and put rate-limits/fallback in-process Sup HN. I built VernLLM cuz every LLM gateaway I looked at, meant adding a network hop just to get rate limiting, multi provider fallback, circuit breaking and other features. Needing to take a whole seperate service just to deploy, monitor and trust with my API keys is just annoying since my whole tech stack was just TypeScript and Node. VernLLM does the same job within your LLM calls, but its in process instead. Its supports OpenAI-compatible APIs, Anthropic, Gemini and Bedrock providers. I would also argue that the features VernLLM provides is more developed then any other gateaway out there when it comes to resilience and customization. The tradeoff vs a gateaway is that it has no centralized policy across multiple apps or languages so if you want that then its better to use a gateaway instead. Here is the docs for more info: https://vernllm.dev https://ift.tt/geymdu2 September 6, 2026 at 02:17AM

Show HN: Blunderbase: A Personal Chess Database https://ift.tt/SkF8pjx

Show HN: Blunderbase: A Personal Chess Database I made a free and open source chess database. It's useful for personal game study and gathering statistics across sites. Supports import from lichess, chess.com, FICS and PGN then runs Stockfish and Maia over every game. With it you can analyze your games, take notes on positions and look up openings. The self hosted web version includes a remote-runner that allows connecting engines running on other machines (finally, the CPUs of the AI server at work come to use :D) and an MCP server with a live board to push positions and comments to. Roadmap: iOS and Android companion apps, a sync service, multi-engine analysis, and a correspondence game mode supporting multiple long running calculations. Looking forward to feedback and critique. Docker, Windows, Mac: https://blunderbase.org Demo: https://ift.tt/LIQtk87 (read-only) https://ift.tt/FCrjvbd September 5, 2026 at 09:21PM

Show HN: Keen Bean – Mac meeting notes that draft the spec while you talk https://ift.tt/mxhZtY5

Show HN: Keen Bean – Mac meeting notes that draft the spec while you talk https://keenbean.app/ September 5, 2026 at 11:53PM

Show HN: MobileCode – OpenCode with Built-In iOS and Android Previews https://ift.tt/EocnYqy

Show HN: MobileCode – OpenCode with Built-In iOS and Android Previews https://ift.tt/jpFutTX September 5, 2026 at 05:44AM

Show HN: Minilith – A zero-dependency CMS stored inside a PNG file https://ift.tt/fibeJ6B

Show HN: Minilith – A zero-dependency CMS stored inside a PNG file 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. https://blog.minilith.site/#posts/minilith-architecture September 5, 2026 at 02:09AM