Show HN: Built a fast, no-fluff CRM built for startups who want to close https://ift.tt/H1M8jKk

Show HN: Built a fast, no-fluff CRM built for startups who want to close https://leadchee.com August 5, 2025 at 11:26PM

Show HN: FFlags – Feature flags as code, served from the edge https://ift.tt/A5MnhEs

Show HN: FFlags – Feature flags as code, served from the edge Hi HN, I'm the creator of FFlags. I built this because I wanted a feature flagging system that gave me the performance and reliability of an enterprise-scale solution without the months of dev time or the vendor lock-in. The core ideas are: 1. Feature Flags as Code: You define your flag logic in TypeScript. This lets you write complex rules, which felt more natural as a developer myself than using a complex UI for logic. 2. Open Standard: The platform is built on the OpenFeature standard (specifically the Remote Evaluation Protocol). The goal is to avoid vendor lock-in and the usual enterprise slop. You're not tied to my platform if you want to move. 3. Performance: It uses an edge network to serve the flags, which keeps the wall-time latency low (sub-25ms) for globally distributed applications. I was trying to avoid the heavy cost and complexity of existing enterprise tools while still getting better performance than a simple self-hosted solution. There's a generous free tier ($39 per million requests after that, with no flag/user limits). I'm looking for feedback on the developer experience, the "flags-as-code" approach, and any technical questions you might have. Thanks for taking a look. https://fflags.com August 5, 2025 at 12:43AM

Show HN: A tiny reasoning layer that steadies LLM outputs (MIT; +22.4% accuracy) https://ift.tt/C0bskRf

Show HN: A tiny reasoning layer that steadies LLM outputs (MIT; +22.4% accuracy) We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes). ΔS “semantic jump” check to catch fluent nonsense when the draft answer drifts from retrieved context. Layout-aware anchoring so chunking across PDFs/tables doesn’t silently break routing. A minimal semantic trace for incident review (tiny, not full transcripts). Bench snapshot (same model, with vs. without gates): Semantic Accuracy ↑ 22.4% · Reasoning Success Rate ↑ 42.1% · Stability ↑ 3.6×. Traction (last ~50 days) ~2,400 downloads of the PDF. ~300 cold GitHub stars on related material (no marketing burst). Also received a star from the creator of tesseract.js, which was nice validation from the OCR world. Why this might be useful to you You don’t need to swap models or vendors. The PDF describes checks you can drop into any RAG/agent/service pipeline. No servers, SDKs, or proxy layers—just logic you can copy. Link is Git Repo Happy to answer HN-style questions (what breaks, where it fails, ablations, how we compute ΔS, etc.). If you try it and it doesn’t help, I’m also interested in the counter-examples. with Terrseract (OCR legend) starred it verify it, we are WFFY on top1 https://ift.tt/SZ6GQ2X https://ift.tt/jxHIvnV August 4, 2025 at 08:38PM

Show HN: Mathpad – Physical keypad for typing 100+ math symbols anywhere https://ift.tt/uB1sXe6

Show HN: Mathpad – Physical keypad for typing 100+ math symbols anywhere Here's something different than your usual fare: A physical keypad that lets you directly type math! Ever tried typing mathematical equations in your code IDE, email, or on Slack? You might know it can be tricky. Mathpad solves this with dedicated keys for Greek letters, calculus symbols, and more. Press the ∫ key and get ∫, in any application that accepts text. It uses Unicode composition, so it works everywhere: Browsers, chat apps, code editors, Word, you name it. Basically, anywhere you can type text, Mathpad lets you type mathematics. I built Mathpad after getting frustrated with the friction of typing equations in e.g. Word, and what a pain in the ass it was to find the specific symbols I needed. I assumed that a product like Mathpad already existed, but that was not true and I had to build it myself. It turned out to be pretty useful! Three years of solo development later, I'm launching on Crowd Supply. One of the trickiest parts of this project was finding someone who could manufacture custom keycaps with mathematical symbols. Shoutout to Loic at 3dkeycap.com for making it possible! Fully open source (hardware + software): https://ift.tt/r8q1OQS Campaign: https://ift.tt/vwD8Eom Project log: https://ift.tt/1qXfSLo https://ift.tt/vwD8Eom August 3, 2025 at 02:13AM

Show HN: Enforce TDD in Claude Code https://ift.tt/nFsX5Zz

Show HN: Enforce TDD in Claude Code https://ift.tt/JudCajg August 3, 2025 at 10:55PM

Show HN: Fast Elevation API with memory mapped tiles https://ift.tt/wpxiYuW

Show HN: Fast Elevation API with memory mapped tiles I recently wrote and launched a high-performance Elevation API, built from the ground up, in C. I was highly inspired by the handmade community and I was intrigued by the idea of handling fairly large datasets and optimizing caching and smart prefetching, and to cream out maximum performance in terms of latency and handling large loads. The whole thing is built from scratch. I wanted to roll my own high performance server that could handle a lot, mostly for the technical challenge but also because it brings down hosting costs. At the core is a hand made TCP server where a single thread handles all I/O via epoll, distributing the events to a pool of worker threads. The server is fully non-blocking and edge-triggered, with a minimal syscall footprint during steady-state operation. Worker threads handle request parsing and perform either direct elevation lookups for single- or multi-points, or compute sample points along polyline paths. The elevation data is stored as memory mapped geotiff raster tiles, The tiles are indexed in an R-tree for fast lookup. Given a coordinate, the correct tile is located with a bounding-box search algorithm through the tree, and the elevation value is extracted directly from the mapped memory. If the tile is missing the data, underlying tiles act as fallback. I also implemented a prefetching mechanism. That is, to avoid repeated page faults in popular areas, I employ a strategy where each tile is divided into smaller sub-tiles. Then, I have a running popularity count per sub-tile. This information is then used to guide prefetching. More popular sub-tiles trigger larger-radius prefetches around the lookup point, with the logic that if a specific region is seeing frequent access, it’s worth pulling in more of it into RAM. Over time, this makes the memory layout adapt to real usage patterns, keeping hot areas resident and minimizing I/O latency. Prefetching is done using linux madvise, in a separate prefetch thread to not affect request latency. There’s a free option to try it out! https://ift.tt/HtVm9T1 August 3, 2025 at 02:42AM

Show HN: Open-sourced my prompt management tool for LLM-powered apps https://ift.tt/1kGQol0

Show HN: Open-sourced my prompt management tool for LLM-powered apps https://ift.tt/J0yNXzx August 3, 2025 at 01:42AM