We built an AI Agent to reproduce bugs
At Metabase, we built an AI agent called Repro-Bot that reads our GitHub issues and attempts to reproduce reported bugs automatically. It started as a hackathon project and is now part of our daily workflow, so we wrote about it and open-sourced the code as an example for others. How have similar tools been working for you? What has worked well and what has not?
Every CEO and CFO change at US public companies, live from SEC
Built this solo. It watches SEC filings for executive and board changes, extracts the data, and shows it in real time. 2,100+ changes in the last 30 days. The comp data is interesting: average new CEO total comp is $8.4M across 284 appointments. The /explore page is fully open, no login needed.
Ilha
Show HN: Ilha – a UI library that fits in an AI context window
Zatanna
Hey! I am Alex and together with my co-founder Tarun built Kampala (https://www.zatanna.ai/kampala). It’s a man-in-the-middle (MITM) style proxy that allows you to agentically reverse engineer existing workflows without brittle browser automation or computer use agents. It works for websites, mobile apps, desktop apps. Demo: https://www.youtube.com/watch?v=z_PeostC-b4. Many people spend hours per day in legacy dashboards and on-prem solutions reconciling data across platforms. Current attempts at automation use browser automations or computer use agents which are brittle, slow, and nondeterministic. I come from a web reverse engineering background and spent the last 7-8 years building integrations by hand for sneaker/ticket releases, sportsbooks logins, and everything in\ between. During that time I consulted for several companies and brought them off of browser based infrastructure into the requests layer. When we started Zatanna (that’s our company name) we worked in dental tech, which meant we had to deal with tons of insurance payer dashboards and legacy dental-practice solutions. Our superpower (as a fairly undifferentiated voice agent/front desk assistant company) was that we could integrate with nearly any system requested. During this time we built extensive tooling (including what we’re now calling Kampala) to allow us to spin up these integrations quickly. Existing MITM proxies and tooling didn’t work for a few reasons: (1) They manipulated the TLS and HTTP2 fingerprint over the wire which was detected by strict anti-bots. (2) They had bad MCPs which did not adequately expose necessary features like scripts/replay. (3) They did not allow for building workflows or actions given a sample or sequence of requests. As the tools we built got more powerful, we began to use them internally to scrape conference attendees, connect to external PMS systems, and interact with slack apps. I even sent it to my property manager mom, who (with a lot of help from me lol), automated 2-3 hours of billing information entry in Yardi. At that point we realized that this wasn’t really about dentistry :) Because Kampala is a MITM, it is able to leverage existing session tokens/anti-bot cookies and automate things deterministically in seconds. You can either use our agent harness that directly creates scripts/apis by prompting you with what actions to make, or our MCP by manually doing a workflow once, and asking your preferred coding agent to use Kampala to make a script/API to replicate it. Once you have an API/script, you can export, run, or even have us host it for you. We think the future of automation does not consist of sending screenshots of webpages to LLMs, but instead using the layer below that computers actually understand. Excited to hear your thoughts/questions/feedback!
Spice simulation → oscilloscope → verification with Claude Code
I built MCP servers for my oscilloscope and SPICE simulator so Claude Code can close the loop between simulation and real hardware.
Solyto
Show HN: Solyto – a free, open-source all-in-one personal management app
A lightweight way to make agents talk without paying for API usage
Show HN: A lightweight way to make agents talk without paying for API usage
CyberWriter
Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet. - Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token cost. - NLContextualEmbedding (Natural Language framework, macOS 14+) -- a BERT-style 512-dim text embedder. Exactly what OpenAI and Cohere sell, sitting in Apple's SDKs since iOS 17. - SFSpeechRecognizer / SpeechAnalyzer - on-device speech-to-text including live dictation. Solid accuracy on Apple Silicon. I built cyberWriter, a Markdown editor, on top of all three, mostly as a test and showcase to see what it can do. I actually integrated local and cloud AI first, and then Apple shipped the foundation model, it stacked on super easy, and now users with no local or API AI knowledge can use it with just a click or two. Well the real reason is because most markdown editors need plugins that run with full system access, and I work on health data and can't have that. Vault chat / semantic search. The app indexes your Markdown folder via NLContextualEmbedding (around 50 seconds for 1000 chunks on an M1). The search bar gets a "Related Ideas" section that matches by meaning - typing "orbital mechanics" surfaces notes about rockets and launch windows even when those exact words never appear. Ask the AI a question and it retrieves the top 5 chunks as context. Plain RAG, but the embedder, retrieval, chat model, and search all run locally. AI Workspace. Command+Shift+A opens a chat panel, Command+J triggers inline quick actions (rewrite, summarize, change tone, fix grammar, continue). Apple Intelligence is the default; Claude, OpenAI, Ollama, and LM Studio all work if you prefer. The same context layer - document selection, attached files, retrieved vault chunks - feeds every provider through the same system-message path. Because the vault context is file and filename aware, it can create backlinks to the referenced file if it writes or edits a doc for you. Voice notes and dictation. Record a voice note directly into your doc, transcribe it with SpeechAnalyzer, or just dictate into the editor while you think. Audio never leaves the Mac. The privacy story is straightforward because the primitives are already private. Vectors live in a `.vault.embeddings.json` file next to your vault, never sent anywhere. If you use Apple Intelligence, even the retrieved text stays on-device. For cloud models there is a clear toggle and an inline warning before any filenames or snippets leave the machine. Honest limitations: - 512-dim embeddings are solid mid-tier. A GPT-4-class embedder catches subtler relationships this will miss. - 256-token chunks can split long paragraphs mid-argument. - Foundation Models caps its context window around 6K characters, so vault context is budgeted to 3K with truncation markers on the rest. - Multilingual support is English-only right now. NLContextualEmbedding has Latin, Cyrillic, and CJK model variants; wiring the language detector across chunks is Phase 2. The developer experience for these APIs is genuinely good. Foundation Models streams cleanly, NLContextualEmbedding downloads assets on demand and gives you mean-poolable token vectors in a handful of lines. Curious what others here are building on this stack - feels like low-hanging fruit that has been sitting there for a while. https://imgur.com/a/HyhHLv2 The Apple AI embedding feature is going live today. I'm honestly surprised it even works out of the box.
Daemons
For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams. The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development. Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
Graph Compose
Hey HN. Graph Compose is a hosted platform for orchestrating API workflows on Temporal. You define workflows as graphs of nodes (HTTP calls, AI agents, iterators, error boundaries) and everything runs as a durable Temporal workflow under the hood. Three ways to build the same graph: a React Flow visual builder, a typed TypeScript SDK (@graph-compose/client), and an AI assistant that turns plain English into a graph. Open-core: the execution foundations and integrations service are AGPL-3.0. The platform orchestrator, visual builder, and AI assistant are proprietary. Longer backstory on why I built this in the first comment. Would love feedback, especially from anyone who's dealt with the "services work fine, the glue between them doesn't" problem. Docs: https://graphcompose.io/docs
Clawemon, A Pokemon-style MMO for your agents
Team up with your agent to trade, battle, and collect every Clawemon! This has been a fun side project for us, and we had a great time playing it with friends over the weekend. We’ll keep adding more towns and expanding the game in our spare time. Let us know your thoughts and what you’d like to see next - thank you! Send your agent to clawemon.com and join the world of Clawemon!
Spectrum
how can we make AI agents more accessible? it has been a crazy journey seeing how AI agents gets smarter every single day but interaction layer is still missing we are launching Spectrum TODAY: one Unified API connect your AI Agents into iMessage, WhatsApp, Telegram and more