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

Show HN: F1 COSMOS – Live timing and data dashboard for F1 fans https://ift.tt/GMrn6Zz

Show HN: F1 COSMOS – Live timing and data dashboard for F1 fans Hey everyone! I'm a huge F1 fan and got tired of juggling multiple tabs and apps during race weekends, so I built F1 COSMOS. What it does: - Live timing: Real-time data updates in milliseconds - sector times, telemetry, team radio, you name it. No more refreshing pages or waiting for delayed updates. - Replay feature: Missed qualifying or fell asleep during practice? You can replay the live timing from any past session. Pretty handy when you're in a bad timezone. - Proper data visualization: I went beyond just showing lap times. There's race analysis with telemetry data, championship standings, technical updates, and a bunch of other stats that make watching races way more interesting. Oh, and the race calendar automatically adjusts to your timezone because I was sick of doing timezone math in my head. - Multi-device setup: Here's the thing - I watch races on my TV but wanted data on my phone as a second screen. So I spent ages making the mobile experience smooth for exactly this use case. Desktop has customizable widgets if you're into that. Technical stuff: Built with modern web stack, focused heavily on real-time performance. The trickiest part was getting the data pipeline right for millisecond updates without everything falling apart. Why I built this: Honestly, existing F1 apps either suck or cost money or both. I wanted something that just works and gives me all the data I actually care about in one place. Been using it myself all season and figured others might find it useful. Currently supports English, Spanish, Japanese, and Korean (partially) - still working on expanding language support. Would love to hear what you think if you check it out during the next race weekend. https://f1cosmos.com/ August 2, 2025 at 09:58PM

Show HN: WebGPU enables local LLM in the browser – demo site with AI chat https://ift.tt/q1RoMfu

Show HN: WebGPU enables local LLM in the browser – demo site with AI chat Browser LLM demo working on JavaScript and WebGPU. WebGPU is already supported in Chrome, Safari, Firefox, iOS (v26) and Android. Demo, similar to ChatGPT https://andreinwald.github.io/browser-llm/ Code https://ift.tt/ow06cYE - No need to use your OPENAI_API_KEY - its local model that runs on your device - No network requests to any API - No need to install any program - No need to download files on your device (model is cached in browser) - Site will ask before downloading large files (llm model) to browser cache - Hosted on Github Pages from this repo - secure, because you see what you are running https://andreinwald.github.io/browser-llm/ August 2, 2025 at 07:39PM

Shorter, Smoother Rides on Muni Metro: How a Milestone Grant Will Help Us Improve Your Trips

Shorter, Smoother Rides on Muni Metro: How a Milestone Grant Will Help Us Improve Your Trips
By Mariana Maguire

Learn how new funding for our Train Control Upgrade Project will help make your Muni Metro trips more reliable and faster overall. We are working hard to improve your trips on Muni Metro, and a milestone grant will help us make even more progress. We recently won a $41 million state grant from the California Transportation Commission. Its highly competitive Solutions for Congested Corridors Program funds capital infrastructure projects. This grant will provide a vital source of support for our Train Control Upgrade Project (TCUP). The project will overhaul and expand Muni Metro’s outdated...

Published August 01, 2025 at 05:30AM
https://ift.tt/yu8xRHp

Show HN: Tambo – a tool for building generative UI React apps with tools/MCP https://ift.tt/PD1U4Xy

Show HN: Tambo – a tool for building generative UI React apps with tools/MCP Hey! We're working on a React SDK + API to make it simple to build apps with natural language interfaces, where AI can interact with the components on screen on behalf of the user. The basic setup is: Register your react components, tools, and MCP servers, and a way for users to send messages to Tambo, and let Tambo respond with text or components, calling tools when needed. Use it to build chat apps, copilots, or completely custom AI UX. The goal is to provide simple interfaces for common AI app features so we don't have to build them from scratch. Things like: - thread storage/management - streaming props into generated components - MCP and custom tool integration - passing component state to AI plus some pre-built UI components to get started. Would love feedback or contributions! https://ift.tt/TBeLK3s August 2, 2025 at 12:11AM

Show HN: TraceRoot – Open-source agentic debugging for distributed services https://ift.tt/agDiNmB

Show HN: TraceRoot – Open-source agentic debugging for distributed services Hey Xinwei and Zecheng here, we are the authors of TraceRoot ( https://ift.tt/LEpFMcW ). TraceRoot ( https://traceroot.ai ) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents. At the heart are our lightweight Python ( https://ift.tt/q93d1Si ) and TypeScript ( https://ift.tt/8wAyQqL ) SDKs - they can hook into your app using OpenTelemetry and captures logs and traces. These are either sent to a local Jaeger ( https://ift.tt/s8nmbOE ) + SQLite backend or to our cloud backend, where we correlate them into a single view. From there, our custom agent takes over. The agent builds a heterogeneous execution tree that merges spans, logs, and GitHub context into one internal structure. This allows it to model the control and data flow of a request across services. It then uses LLMs to reason over this tree - pruning irrelevant branches, surfacing anomalous spans, and identifying likely root causes. You can ask questions like “what caused this timeout?” or “summarize the errors in these 3 spans”, and it can trace the failure back to a specific commit, summarize the chain of events, or even propose a fix via a draft PR. We also built a debugging UI that ties everything together - you explore traces visually, pick spans of interest, and get AI-assisted insights with full context: logs, timings, metadata, and surrounding code. Unlike most tools, TraceRoot stores long-term debugging history and builds structured context for each company - something we haven’t seen many others do in this space. What’s live today: - Python and TypeScript SDKs for structured logs and traces. - AI summaries, GitHub issue generation, and PR creation. - Debugging UI that ties everything together TraceRoot is MIT licensed and easy to self-host (via Docker). We support both local mode (Jaeger + SQLite) and cloud mode. Inspired by OSS projects like PostHog and Supabase - core is free, enterprise features like agent mode multi-tenant and slack integration are paid. If you find it interesting, you can see a demo video here: https://www.youtube.com/watch?v=nb-D3LM0sJM We’d love you to try TraceRoot ( https://traceroot.ai ) and share any feedback. If you're interested, our code is available here: https://ift.tt/LEpFMcW . If we don’t have something, let us know and we’d be happy to build it for you. We look forward to your comments! https://ift.tt/LEpFMcW August 1, 2025 at 10:28PM

Show HN: Sourcebot – Self-hosted Perplexity for your codebase https://ift.tt/0JzCoOU

Show HN: Sourcebot – Self-hosted Perplexity for your codebase Hi HN, We’re Brendan and Michael, the creators of Sourcebot ( https://ift.tt/ZNEwFoI ), a self-hosted code understanding tool for large codebases. We originally launched on HN 9 months ago with code search ( https://ift.tt/6rlk1HZ ), and we’re excited to share our newest feature: Ask Sourcebot. Ask Sourcebot is an agentic search tool that lets you ask complex questions about your entire codebase in natural language, and returns a structured response with inline citations back to your code. Some types of questions you might ask: - “How does authentication work in this codebase? What library is being used? What providers can a user log in with?” ( https://ift.tt/xJDHUX1 ) - “When should I use channels vs. mutexes in go? Find real usages of both and include them in your answer” ( https://ift.tt/4R8JIHY ) - “How are shards laid out in memory in the Zoekt code search engine?” ( https://ift.tt/jEQ8i2d ) - "How do I call C from Rust?" ( https://ift.tt/WP2i1GR ) You can try it yourself here on our demo site ( https://ift.tt/JSQXPpl ) or checkout our demo video ( https://youtu.be/olc2lyUeB-Q ). How is this any different from existing tools like Cursor or Claude code? - Sourcebot solely focuses on code understanding . We believe that, more than ever, the main bottleneck development teams face is not writing code, it’s acquiring the necessary context to make quality changes that are cohesive within the wider codebase. This is true regardless if the author is a human or an LLM. - As opposed to being in your IDE or terminal, Sourcebot is a web app. This allows us to play to the strengths of the web: rich UX and ubiquitous access. We put a ton of work into taking the best parts of IDEs (code navigation, file explorer, syntax highlighting) and packaging them with a custom UX (rich Markdown rendering, inline citations, @ mentions) that is easily shareable between team members. - Sourcebot can maintain an up-to date index of thousands of repos hosted on GitHub, GitLab, Bitbucket, Gerrit, and other hosts. This allows you to ask questions about repositories without checking them out locally. This is especially helpful when ramping up on unfamiliar parts of the codebase or working with systems that are typically spread across multiple repositories, e.g., micro services. - You can BYOK (Bring Your Own API Key) to any supported reasoning model. We currently support 11 different model providers (like Amazon Bedrock and Google Vertex), and plan to add more. - Sourcebot is self-hosted, fair source, and free to use. Under the hood, we expose our existing regular expression search, code navigation, and file reading APIs to a LLM as tool calls. We instruct the LLM via a system prompt to gather the necessary context via these tools to sufficiently answer the users question, and then to provide a concise, structured response. This includes inline citations, which are just structured data that the LLM can embed into it’s response and can then be identified on the client and rendered appropriately. We built this on some amazing libraries like the Vercel AI SDK v5, CodeMirror, react-markdown, and Slate.js, among others. This architecture is intentionally simple. We decided not to introduce any additional techniques like vector embeddings, multi-agent graphs, etc. since we wanted to push the limits of what we could do with what we had on hand. We plan on revisiting our approach as we get user feedback on what works (and what doesn’t). We are really excited about pushing the envelope of code understanding. Give it a try: https://ift.tt/I7xk32c . Cheers! https://ift.tt/k7j2Yvd July 30, 2025 at 08:14PM