Show HN: Cargo-rail: graph-aware monorepo tooling for Rust; 11 deps https://ift.tt/3cSnmX9

Show HN: Cargo-rail: graph-aware monorepo tooling for Rust; 11 deps https://ift.tt/UGYAD3F December 11, 2025 at 02:19AM

Show HN: I added a print edition to my indie blog https://ift.tt/WVOyHwB

Show HN: I added a print edition to my indie blog https://ift.tt/jGXk1vW December 11, 2025 at 12:11AM

Show HN: A 2-row, 16-key keyboard designed for smartphones https://ift.tt/2uh0Tg9

Show HN: A 2-row, 16-key keyboard designed for smartphones Mobile keyboards today are almost entirely based on the 26-key, 3-row QWERTY layout. Here’s a new 2-row, 16-key alternative designed specifically for smartphones. https://ift.tt/4wubGeD December 10, 2025 at 11:19PM

More Merry Days of Muni: Help Us Name Our New Festive Wraps

More Merry Days of Muni: Help Us Name Our New Festive Wraps
By Glennis Markison

Bringing joy, stop after stop – one of our new vehicle wraps for the Merry Days of Muni. The Merry Days of Muni fun continues with new holiday-themed wraps on our buses and a train. Now, you can take a festive ride on five different Muni routes across the city. And we’re making it easy to catch our merriest vehicles on your next ride. Introducing: the Merry Days tracker. And there's more fun in store -- we want your help naming three of the festive vehicles. See the full list of festive routes below and how to suggest names! Help us name Muni's merriest rides [video:https://www.youtube.com...

Published December 09, 2025 at 05:30AM
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Show HN: Advent of Back Ends https://ift.tt/UcWpYy3

Show HN: Advent of Back Ends Build AI Agents, Workflows, Backend systems live every day for 30 days. https://adventofbackends.vercel.app/ December 9, 2025 at 11:56PM

Show HN: Agentic Reliability Framework – Multi-agent AI self-heals failures https://ift.tt/PrsvOKH

Show HN: Agentic Reliability Framework – Multi-agent AI self-heals failures Hey HN! I'm Juan, former reliability engineer at NetApp where I handled 60+ critical incidents per month for Fortune 500 clients. I built ARF after seeing the same pattern repeatedly: production AI systems fail silently, humans wake up at 3 AM, take 30-60 minutes to recover, and companies lose \$50K-\$250K per incident. ARF uses 3 specialized AI agents: Detective: Anomaly detection via FAISS vector memory Diagnostician: Root cause analysis with causal reasoning Predictive: Forecasts failures before they happen Result: 2-minute MTTR (vs 45-minute manual), 15-30% revenue recovery. Tech stack: Python 3.12, FAISS, SentenceTransformers, Gradio Tests: 157/158 passing (99.4% coverage) Docs: 42,000 words across 8 comprehensive files Live demo: https://ift.tt/NwIlyVc... The interesting technical challenge was making agents coordinate without tight coupling. Each agent is independently testable but orchestrated for holistic analysis. Happy to answer questions about multi-agent systems, production reliability patterns, or FAISS for incident recall! GitHub: https://ift.tt/cF4utlq (Also available for consulting if you need this deployed in your infrastructure: https://lgcylabs.vercel.app/ ) https://ift.tt/cF4utlq December 9, 2025 at 10:25PM

Show HN: Fanfa – Interactive and animated Mermaid diagrams https://ift.tt/mohBy0f

Show HN: Fanfa – Interactive and animated Mermaid diagrams https://fanfa.dev/ December 4, 2025 at 06:46PM