BlogViral

Tap to Pay Ushers in a New Era of Flexibility on Muni By Melissa Culross Muni vehicles and station fare gates now accept plastic. You can tap a credit or debit card at Clipper readers to pay. Paying your Muni fare is now even easier and more convenient. And it’s becoming less expensive to move throughout the entire Bay Area on public transit. The next generation of Clipper card service is here. Clipper is the electronic fare payment system that all Bay Area transit agencies use. And this upgrade includes a host of new features. Tap a credit card, debit card or your phone to pay your fare Enjoy free or discounted transfers between transit agencies Buy a wider... Published December 11, 2025 at 07:00AM https://ift.tt/xWsn4ke

Show HN: Serif – a zero-dependency, DataFrame for Python OP here. I built this library out of frustration with messy, day-to-day data: CSVs with duplicated columns, APIs returning nested JSON, Excel sheets, and various ad-hoc ETL requests. Tools like Polars and DuckDB are great for heavy analytical workloads, but sometimes I just want a lightweight, Pythonic table I can iterate over. I want the ability to for `row in table:` without surprising semantics. Serif takes a vector-first, zero-dependency approach aimed at everyday data tasks. https://ift.tt/bKLnj7R This is an early release, so I'm looking for feedback on ergonomics and places where the API is either missing features or could be simpler. https://ift.tt/bKLnj7R December 11, 2025 at 10:05PM

Show HN: MCPShark – Traffic Inspector for Model Context Protocol https://ift.tt/QRKTVH4 Site: https://mcpshark.sh/ I built MCPShark, a traffic inspector for the Model Context Protocol (MCP). It sits between your editor/LLM client and MCP servers so you can: • See all MCP traffic (requests, responses, tools, resources) in one place • Debug sessions when tools don’t behave as expected • Optionally run “Smart Scan” checks to flag risky tools / configs December 11, 2025 at 12:27AM

Show HN: Bloodhound – Grey-box attack-path discovery in Rust/Go/C++ binaries We originally set out to solve complex debugging headaches and useless alerts caused by traditional security scanners in our own projects. Static Analysis (SAST) flagged too much noise because it couldn't verify runtime context, while Dynamic Analysis (DAST) missed internal logic bugs because it treated the app like a black box. We built a CLI tool to bridge this gap using grey box testing from a red team approach. We use internal knowledge of the codebase to guide parallel execution, allowing us to find complex or hidden logic errors and attack paths standard linters/scanners miss. The Tech (Grey Box Graphing & Execution): - Internal Graphing (The Map): It ingests the codebase to build a dependency graph of the internal logic. - Parallel Execution (The Test): The code is then tested on parallel engines. We spin up copies of your local dev environment to exercise the codebase in thousands of ways. This...

Show HN: ZON-TS 50–65% fewer LLM tokens zero parse overhead better than TOON/CSV hey HN — roni here, full-stack dev out of india (ex-gsoc @ internet archive). spent last weekend hacking ZON-TS because json was torching half my openai/claude budget on dumb redundant keys — hit that wall hard while prototyping agent chains. result: tiny TS lib (<2kb, 100% tests) that zips payloads ~50% smaller (692 tokens vs 1300 on gpt-5-nano benches) — fully human-readable, lossless, no parse tax. drop-in for openai sdk, langchain, claude, llama.cpp, zod validation, streaming... just added a full langchain chain example to the readme (encode prompt → llm call → decode+validate, saves real $$ on subagent loops). quick try: ```ts npm i zon-format import { encode, decode } from 'zon-format'; const zon = encode({foo: 'bar'}); console.log(decode(zon)); ``` github → https://github.com/ZON-Format/ZON-TS benches + site → https://zonformat.org YC’s fall rfs nailed it — writing effective agent...

Show HN: Detail, a Bug Finder Hi HN, tl;dr we built a bug finder that's working really well, especially for app backends. Try it out and send us your thoughts! Long story below. -------------------------- We originally set out to work on technical debt. We had all seen codebases with a lot of debt, so we had personal grudges about the problem, and AI seemed to be making it a lot worse. Tech debt also seemed like a great problem for AI because: 1) a small portion of the work is thinky and strategic, and then the bulk of the execution is pretty mechanical, and 2) when you're solving technical debt, you're usually trying to preserve existing behavior, just change the implementation. That means you can treat it as a closed-loop problem if you figure out good ways to detect unintended behavior changes due to a code change. And we know how to do that – that's what tests are for! So we started with writing tests. Tests create the guardrails that make future code changes safer....