Show HN: CSV Mail Sender – Send personalized email campaigns from a CSV https://ift.tt/ZfcvYB5

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Supporting Trips to School and Work: Muni Service Changes Start Aug. 30

Supporting Trips to School and Work: Muni Service Changes Start Aug. 30
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Reducing crowding on the 49 Van Ness/Mission is one of our top priorities as we update service on a few routes later this month. We are changing Muni service on a few routes later this month to make it even easier to get to school and work in the morning. These changes are based on direct feedback from riders and operators. They aim to improve your Muni experience by: Addressing weekday crowding on routes used by students Expanding express service for downtown commuters Improving reliability and connections to key destinations including regional transit Weekend changes start Saturday, Aug. 30...

Published August 06, 2025 at 05:30AM
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When is the next caltrain? (minimal webapp) https://ift.tt/rSukOKw

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Show HN: Write lead sheets in a Markdown way and transpose in a second https://ift.tt/AxRDZep

Show HN: Write lead sheets in a Markdown way and transpose in a second Hey HN, I'm a software engineer with a passion for playing guitar. ( https://ivanhsu.co ) In the software industry, we use clever plain-text syntaxes like Markdown and Mermaid to handle complex layouts. This lets us focus on the content itself and quickly produce beautifully formatted documents. Isn't sheet music and chord charts just another form of documentation in the world of music? That's why I created Cord Land https://ift.tt/jL48nN3 ! It's a website where you can quickly generate lead sheets and draw chord charts using plain text. Even better, it can automatically transpose songs! Just write in one key, and it can be instantly converted it to any of the other 11 keys you want. I've implemented a new syntax called Corduroy, an extension of ChordPro syntax specifically designed for guitarists. Besides showing chord names above lyrics, you can also customize chord charts. For example, `%x32o1o%` will automatically draw a C major chord in the first position! Feel free to try it out here: https://ift.tt/nVKdD0z For more usage details, please refer to: https://ift.tt/qe5D3LS The name "Cord Land" comes from "Cord" and "Chord" being homophones, representing chords. Let's keep our passion for playing guitar alive, even after work! Ivan Hsu https://ift.tt/jL48nN3 August 3, 2025 at 08:08PM

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

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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