I built an on-chain economy where AI agents transact autonomously
Show HN: I built an on-chain economy where AI agents transact autonomously
Wikigraph
Hi! This is a visualization I've always wanted but never quite found. It's a navigable map of the Wikipedia link graph structure, with search and shortest-path finding. Offline, I parsed the May 2026 English Wikipedia full-text dump into a directed graph, used cuGraph on a GPU to run PageRank, Leiden clustering, and ForceAtlas2 for the layout. I did some post processing to get rid of lingering overlapping nodes and rendered a tiled map of raster base images (using Skia) and JSON metadata. Tiles are bundled into PMTiles. The frontend is Deck.gl. Everything is hosted on Cloudflare. Search and shortest-path are served by a Rust backend in CF Containers which uses Tantivy and bidirectional BFS. Happy to answer any questions!
DropLock
Show HN: DropLock – E2EE secret sharing web app with no backend
Build Your Own AI Agent CLI in 150 Lines
I can't tell if HN is the right kind of place for this stuff anymore since people are so advanced in their use but I thought it was interesting to leverage my existing Go microservices framework and turn it into the core of what would provide tools for an agent cli or whatever beyond that. Extensibility is key. Thought I'd share and get a conversation going.
Form an LLC or C-Corp from Claude/Cursor via MCP
Show HN: Form an LLC or C-Corp from Claude/Cursor via MCP
NUA an agent that tests for product correctness
We’ve been using background Claude loops a lot recently, and we would wake up to PRs that didn’t solve the problem we wanted, made on assumptions that were wrong. Furthermore, the tests that the agents wrote were usually tautological, and didn’t test for intent. We wanted an agent that took all the context a company has, and writes tests that check for product correctness as well. For example, we work in reg tech, so bugs aren’t always technical. What we often see is things like insider trading alerts that should’ve fired that didn’t. We wanted an agent that turns laws and regulations into tests. For now, users can upload PDF, MD, TXT, and DOCX files, but we’re planning integrations like Slack, Notion, Linear, and Zoom in the future. We’re early on, so we would love to know what you all think!
Textile
Hi all, I'm excited to show off Textile, a desktop app I recently built. Textile can combine bits of text using various inputs, such as commands on your computer, the contents of your clipboard, and hard-coded strings that you provide. It lets you carefully build up and modify a dynamic string, step by step, until it's exactly how you need it. The saved steps can then be executed on demand, with the click of a button or using a keyboard shortcut. I built Textile because I was often constructing complicated, dynamic URLs from various sources that all existed on my computer. I got tired of manually switching between different apps, copying and pasting various chunks of text, and assembling them all together somewhere. I've also found Textile to be quite useful as a kind of repository for obscure bits of static text, such as ½ and other fraction characters, when I can't be bothered to remember their built-in keyboard combinations. I also built Textile because I wanted to learn Electron, although I expect there will be some gnashing of teeth about this here. :) I think desktop development is quite interesting, in part because it doesn't require me, the developer, to pay for an API server and database in the cloud. The app itself is both the UI and the "server," and the local drive is effectively the "database." I knows this trades away syncing with the cloud but, on the other hand, there's something nice about knowing that your files are on your drive and not on somebody else's server. I realize that something like Textile may already exist, and may have much more functionality but, again, I wanted to learn. I must say that multi-sequence keyboard shortcuts are hard, and there are cases that don't work right in Textile. I feel vulnerable admitting that my approach has much room for improvement! For what it's worth, I did not use an LLM to write any code for Textile (although I did ask many questions of an LLM, as an alternative to Googling). Textile is open source, free to use, and does not require sign up, email, phone, or other such barriers. Try it and let me know what you think! (Note: I don't have access to hardware running Windows or Linux, so Textile is only available for macOS at the moment.)
I made a Gemma 4 Mac app that names screenshots with local AI
I made my first macOS utility app that ships with a bundled Gemma 4 model, specifically the Gemma E4B one. It made my app DMG have 5.3 GB in size, but I think it is a small size for the power that this free local model can provide. It runs fine on CPU, but can also run on Apple Silicon GPU, although I did not notice any performance improvements with GPU (tested on a M5 chip). I think these local lightweight and multimodal models will open multiple possibilities for new software tools where privacy is essential.
500 years of Joseon court omens as an observability dashboard
Show HN: 500 years of Joseon court omens as an observability dashboard
TV Explorer. Adding advanced UI to free online TV
Show HN: TV Explorer. Adding advanced UI to free online TV
Integuru
Hey HN! We’re Alan and Richard from Integuru (YC W24). We generate fast, reliable integrations for platforms lacking official APIs. About 2 years ago, we released the first agent that reverse-engineers network traffic to build integrations (https://github.com/Integuru-AI/Integuru). Since then, we’ve developed a new approach to reverse-engineer platforms’ source code directly. This solution also includes authentication support. Here’s a demo: https://youtu.be/4l2L8fILC2g?si=nbWbDiFrWZIWRPM7. Many AI products need to integrate with web apps, but platforms often lack official APIs. So far, there are two main ways to integrate: browser automation and via network requests. We set out to build the original agent because we ourselves suffered from RPA’s latency, reliability, and throughput issues. The original agent solved many of the prior issues, but it wasn’t perfect either. The original agent did things the obvious way: (1) have a human do the action; (2) the agent observes the network requests and (3) recreates them. That got us far, but it only supported the path the user triggered. In production, we saw all the uncovered cases: different states, missing fields, permission differences, hidden validations, and request changes we could never catch in a single run. So we started building a new solution from the ground up. Our first step was to add agents that trigger many variations of the same action. To protect the platform’s data integrity, we added a gating layer that blocks outbound requests. This lets us observe the exact request structure, branching behavior, and platform logic without accidentally mutating the live system. But this still wasn’t enough. Some logic is hard to surface by execution alone. A lot of the business rules live in the frontend bundle. So we set out to analyze the true “answer sheet” for each platform: the source code. After experimenting, we got this working. We built a source-code analysis layer that deobfuscates and traces the code associated with each action. In practical terms, our system can handle most tricky edge cases without triggering all flows. Together, these two layers result in much better coverage of the production surface area. They support more edge cases, fail less often, and avoid a lot of the brittle one-off fixes that usually come later. Finally, we added auto-healing and API doc generation to improve reliability and the UX. We also offer a 24/7 on-call maintenance team for companies on the production plan. We now spend most of our time supporting vertical AI companies and helping them connect to their customer systems. We offer a free plan for integrating with one platform and charge for additional platforms, accounts, and overage API calls. For instance, we help healthcare AI companies connect to EHRs and payer portals, and logistics companies connect to TMSs and ERPs. Some companies are now running more than 1M monthly requests per platform. Across our production users, API calls complete in ~3 seconds at 99.9%+ success rate on average. We’re also building a library of APIs that users can use out of the box. That said, this version still has limitations we want to iterate on. Although we already tackle some anti-bot mechanisms, the agent still struggles to generate integrations with heavily anti-botted platforms. When the agent fails, our on-call team steps in to improve the agent or build the integration manually if the customer requests it. Also, the UX for generating an integration is still quite manual. Our next step is to build a CLI experience, so people and their agents can create, test, and use integrations in a much more flexible manner. This also prevents humans from having to wait for Integuru to finish its tasks. We want to one day allow developers and agents to integrate with all platforms instantly. Integuru is an ongoing effort. We’re passionate about automating integrations and would love your feedback!
Context-aware Japanese furigana using Sudachi and ModernBERT
Show HN: Context-aware Japanese furigana using Sudachi and ModernBERT