Solyto
Show HN: Solyto – a free, open-source all-in-one personal management app
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.
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!
Ilha
Show HN: Ilha – a UI library that fits in an AI context window
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.
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?
Kelet
I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail. AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions. Kelet automates that investigation. Here's how it works: 1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.) 2. Kelet processes those signals and extracts facts about each session 3. It forms hypotheses about what went wrong in each case 4. It clusters similar hypotheses across sessions and investigates them together 5. It surfaces a root cause with a suggested fix you can review and apply The key insight: individual session failures look random. But when you cluster the hypotheses, failure patterns emerge. The fastest way to integrate is through the Kelet Skill for coding agents — it scans your codebase, discovers where signals should be collected, and sets everything up for you. There are also Python and TypeScript SDKs if you prefer manual setup. It’s currently free during beta. No credit card required. Docs: https://kelet.ai/docs/ I'd love feedback on the approach, especially from anyone running agents in prod. Does automating the manual error analysis sound right?
Ithihāsas
Hi HN! I’ve always found it hard to explore the Mahābhārata and Rāmāyaṇa online. Most content is either long-form or scattered, and understanding a character like Karna or Bhishma usually means opening multiple tabs. I built https://www.ithihasas.in/ to solve that. It is a simple character explorer that lets you navigate the epics through people and their relationships instead of reading everything linearly. This was also an experiment with Claude CLI. I was able to put together the first version in a couple of hours. It helped a lot with generating structured content and speeding up development, but UX and data consistency still needed manual work. Would love feedback on the UX and whether this way of exploring mythology works for you.
Turn your favorite YouTube channels into a streaming experience
A minimalist way to watch YouTube with cinematic previews, an immersive interface, and zero distractions. Free, no accounts or subscription needed.
ParseBench
Show HN: ParseBench – Document parsing benchmark for AI agents
boringBar
Hi HN! I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together. I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine). I’ve been dogfooding it for a few months now, and it finally felt polished enough to share. It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile. I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else. P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
Oberon System 3 runs natively on Raspberry Pi 3 (with ready SD card)
Show HN: Oberon System 3 runs natively on Raspberry Pi 3 (with ready SD card)