Show HN: Quizquestions.org – A free library for quiz questions https://ift.tt/mACw5tU

Show HN: Quizquestions.org – A free library for quiz questions Hey HN! I'm Salim, a content marketer, and I’m working on a website called [quizquestions.org]( https://ift.tt/qStE81r ). It's my project for building the biggest library of quiz questions. This is not a quiz website per se, but a library for people who make quizzes. You see, I make quizzes occasionally. There are many quiz makers, but not many resources for quizzes. And most of the resources are just blogs. So I've wanted to create a more structured website just for this. Here’s what the site offers at the moment: - A quiz card: Instead of browsing them, you can get quiz questions in a quiz format - Quiz categories: https://ift.tt/NTJv4eO - AI question generator: https://ift.tt/CKDr2QP - A blog page for guides: https://ift.tt/7yc5sK6 - Saving questions: To use them later for creating a quiz - Sending questions: To send your own questions - Statistics about categories: https://ift.tt/UJDmlq0 This is my first website, so any feedback is welcome! https://ift.tt/8kjKV2R September 16, 2025 at 11:53PM

Show HN: AI Code Detector – detect AI-generated code with 95% accuracy https://ift.tt/SQwLIMX

Show HN: AI Code Detector – detect AI-generated code with 95% accuracy Hey HN, I’m Henry, cofounder and CTO at Span ( https://span.app/ ). Today we’re launching AI Code Detector, an AI code detection tool you can try in your browser. The explosion of AI generated code has created some weird problems for engineering orgs. Tools like Cursor and Copilot are used by virtually every org on the planet – but each codegen tool has its own idiosyncratic way of reporting usage. Some don’t report usage at all. Our view is that token spend will start competing with payroll spend as AI becomes more deeply ingrained in how we build software, so understanding how to drive proficiency, improve ROI, and allocate resources relating to AI tools will become at least as important as parallel processes on the talent side. Getting true visibility into AI-generated code is incredibly difficult. And yet it’s the number one thing customers ask us for. So we built a new approach from the ground up. Our AI Code Detector is powered by span-detect-1, a state-of-the-art model trained on millions of AI- and human-written code samples. It detects AI-generated code with 95% accuracy, and ties it to specific lines shipped into production. Within the Span platform, it’ll give teams a clear view into AI’s real impact on velocity, quality, and ROI. It does have some limitations. Most notably, it only works for TypeScript and Python code. We are adding support for more languages: Java, Ruby, and C# are next. Its accuracy is around 95% today, and we’re working on improving that, too. If you’d like to take it for a spin, you can run a code snippet here ( https://ift.tt/9mxh1eN ) and get results in about five seconds. We also have a more narrative-driven microsite ( https://ift.tt/ly2CRp9 ) that my marketing team says I have to share. Would love your thoughts, both on the tool itself and your own experiences. I’ll be hanging out in the comments to answer questions, too. https://ift.tt/9mxh1eN September 16, 2025 at 11:48PM

Show HN: Datadef.io – Canvas for data lineage and metadata management https://ift.tt/AlJxMdF

Show HN: Datadef.io – Canvas for data lineage and metadata management Hi HN, I’ve been working on https://datadef.io , a tool to help data team (engineer, architect, project manager) make sense of their data universe. The problem: - Data models (dbt, SQL, warehouses) often grow into a tangled mess of tables, joins, and undocumented assumptions. - Lineage is either scattered across tools or missing entirely. - Documentation is usually an afterthought (and gets outdated fast). Datadef.io aims to fix that by providing: - Interactive canvas to map tables, relationships, and indicators. - Automatic lineage visualization to trace dependencies. - Metadata management: define table/column-level details, ownership, and KPIs. - AI-generated documentation that stays in sync with your models. - Export/share features so asset managers, analysts, and other teams don’t get lost in spreadsheets or PDFs. It’s still early, and I’d love feedback from the HN community. In particular: What’s missing for you in lineage/metadata/documentation tools? How would you want to integrate a tool like this into your workflow (dbt, Databricks, Power BI, etc.)? I’d really appreciate your thoughts, feature requests, and criticism. Thanks! https://datadef.io/ September 14, 2025 at 11:59PM

Show HN: Allzonefiles.io – download 307M registered domain names https://ift.tt/ZfcqFDI

Show HN: Allzonefiles.io – download 307M registered domain names - 307M registered domain names across 1570 domain zones total (.com, .net, .io, .ai, .sh, etc) - 78M registered domain names across 312 ccTLD domain zones (.uk, .de, .io, .ai, .sh, etc) - daily lists of newly registered domain names - daily lists of expired domain names - download all domain lists as one huge .zip file (1.2 Gb size) https://allzonefiles.io September 16, 2025 at 12:12AM

Show HN: MCP Server Installation Instructions Generator https://ift.tt/xUCOypj

Show HN: MCP Server Installation Instructions Generator Hey HN, we’ve been experimenting a lot with MCP servers lately, and one of the most time-consuming challenges has been connecting MCP clients to remote MCP servers. To solve this, we built a library that generates them on the fly, enabling 1-click installation buttons and links for most clients out there. Feel free to try out the generator and use it to improve the README of your remote MCP server with the generated markdown. You can even configure the library to return HTML instructions if someone accesses your remote MCP server via the web. https://ift.tt/CxQrI5s September 15, 2025 at 08:03PM

Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers https://ift.tt/CQEXONg

Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://ift.tt/qF8taTr ) https://ift.tt/PqmgENG... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers - MCP servers (Brave search, GitHub, filesystem, etc.) as tools - A2A communication between services - Minimal UI for experimentation for Tech Trend - repo analysis I built this repo because most agent frameworks look great in isolated demos, but fall apart when you try to glue agents together into a real application. My goal was to help people experiment with these patterns and move closer to real-world use cases. It’s not production-grade, but would love feedback, criticism, or war stories from anyone who’s tried building actual multi-agent systems. Big questions: Do you think agent-to-agent protocols like MCP/A2A will stick? Or will the future be mostly single powerful LLMs with plugin stacks? Thanks — excited to hear what the HN crowd thinks! https://ift.tt/zApib2C September 15, 2025 at 02:47AM

Show HN: Worried about your pet? Health assessments with instant answers https://ift.tt/z7U5sXE

Show HN: Worried about your pet? Health assessments with instant answers https://petcheckai.com September 15, 2025 at 01:36AM