Wrapped and Ready for Your Holiday Fun: The Merry Days of Muni

Wrapped and Ready for Your Holiday Fun: The Merry Days of Muni
By Glennis Markison

Mittens and presents and San Francisco landmarks all wrapped around your 49 Van Ness / Mission? That’s right – the Merry Days of Muni have just begun! This festive bus wrap marks the start of a full campaign of merry moments for Muni riders. It’s our way of bringing local communities a little extra joy this holiday season. Through the end of the year, we’re finding fun ways to connect riders as they take Muni to their favorite people, places and traditions across the city. Meet the SFMTA designer behind our first holiday wrap – and see where the festive ride can take you. Muni's first holiday...

Published November 05, 2025 at 05:30AM
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Show HN: ReadMyMRI DICOM native preprocessor with multi model consensus/ML pipes https://ift.tt/H4txQBC

Show HN: ReadMyMRI DICOM native preprocessor with multi model consensus/ML pipes I'm building ReadMyMRI to solve a problem I kept running into: getting medical imaging data (DICOM files) ready for machine learning without violating HIPAA or losing critical context. What it does: ReadMyMRI is a preprocessing pipeline that takes raw DICOM medical images (MRIs, CTs, etc.) and: Strips all Protected Health Information (PHI) automatically while preserving DICOM metadata integrity Compresses images to manageable sizes without destroying diagnostic quality Links deidentified scans to user-provided clinical context (symptoms, demographics, outcomes) Uses multi-model AI consensus analysis for both consumer facing 2nd opinions and clinical decision making support at bedside Outputs everything into a single dataframe ready for ML training using Daft (Eventual's distributed dataframe library) Technical approach: Built on pydicom for DICOM manipulation Uses Pillow/OpenCV for quality-preserving compression Daft integration for distributed processing of large medical imaging datasets Frontier models for multi model analysis (still debating this) What I'm looking for: Feedback from anyone working with medical imaging ML Edge cases I haven't thought about Whether the Daft integration actually makes sense for your use case or if plain pandas would be better HIPAA/privacy concerns I am not thinking about Happy to answer questions about the architecture, HIPAA considerations, or why medical imaging data is such a pain to work with. https://ift.tt/mKvQlWo November 5, 2025 at 04:17AM

Show HN: Barcable – We Built Agents That Automatically Load Test Your Back End https://ift.tt/suyI3FV

Show HN: Barcable – We Built Agents That Automatically Load Test Your Back End Hey HN, we’re Iyan and Datta, founders of Barcable. Barcable connects to your backend (HTTP, gRPC, GraphQL) and uses autonomous agents to generate and run load tests directly inside your CI/CD. No configs, no scripts. It scans your repo, understands your API routes, and builds real test scenarios that hit your endpoints with realistic payloads. Docs: https://ift.tt/aJ0N3zW We built this out of frustration. Every team we’ve worked with ran into the same issue: reliability testing never kept up with development speed. Pipelines deploy faster than anyone can validate performance. Most “load tests” are brittle JMeter relics or one-off scripts that rot after the first refactor. Barcable is our attempt to automate that. It: - Parses your OpenAPI spec or code to discover endpoints automatically - Generates realistic load tests from PR diffs (no manual scripting) - Spins up isolated Cloud Run jobs to execute at scale - Reports latency, throughput, and error breakdowns directly in your dashboard - Hooks into your CI so tests run autonomously before deploys Each agent handles a part of the process—discovery, generation, execution, analysis—so testing evolves with your codebase rather than fighting against it. Right now it works best with Dockerized repos. You can onboard from GitHub, explore endpoints, generate tests, run them, and see metrics in a unified dashboard. It’s still a work in progress. We’ll create accounts manually and share credentials with anyone interested in trying it out. We’re keeping access limited for now because of Cloud Run costs. We’re not trying to replace performance engineers, just make it easier for teams to catch regressions and incidents before production without the setup tax. Would love feedback from anyone who’s been burned by flaky load testing pipelines or has solved reliability differently. We’re especially curious about gRPC edge cases and complex auth setups. HN has always been a huge source of inspiration for us, and we’d love to hear how you’d test it, break it, or make it better. — Iyan & Datta https://ift.tt/ZkircDx https://ift.tt/36IOlEc November 5, 2025 at 04:55AM

Show HN: Agor → Figma for AI Coding (Open Source) https://ift.tt/I9AbxRW

Show HN: Agor → Figma for AI Coding (Open Source) https://agor.live November 4, 2025 at 07:29PM

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) https://ift.tt/14pnsOW

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) Hey HN! I’m a Product Manager and made a DIY doggy cam (using Claude and a Raspberry Pi) to help train my dog with separation anxiety. I wrote up a blog post sharing my experience building this project with AI. https://ift.tt/IPFErgQ November 3, 2025 at 05:34AM

Show HN: Give your coding agents the ability to message each other https://ift.tt/8X4dfOg

Show HN: Give your coding agents the ability to message each other I submitted this earlier but it didn’t get any traction. But it’s blowing up on Twitter, so I figured I would give it another shot here. The system is quick and easy to setup and works surprisingly well. And it’s not just a fun gimmick; it’s now a core part of my workflow. https://ift.tt/gnBr30D November 3, 2025 at 03:09AM

Show HN: Carrie, for what Calendly can't do https://ift.tt/jz98xi4

Show HN: Carrie, for what Calendly can't do Hey everyone, Through my career, I've spent too many hours and too much mental load on busywork like scheduling and following up on people's availabilities. So, I built Carrie. You simply cc her into your emails, and she sorts out meeting times across time zones, finds what works best for everyone, confirms the meeting and sends the invite. She handles scenarios beyond what Calendly can handle and it’s been freeing me up from the back-and-forth of juggling different meeting requests. I’ve been testing this with a beta group of users and am now looking to expand the user pool (please feel free to join the waitlist if you're interested). Would also love feedback on whether this seems useful and what seems to be missing to make this part of your workflow. Thanks! https://getcarrie.com/ November 2, 2025 at 08:10PM