Webctl – Browser automation for agents based on CLI instead of MCP
Hi HN, I built webctl because I was frustrated by the gap between curl and full browser automation frameworks like Playwright. I initially built this to solve a personal headache: I wanted an AI agent to handle project management tasks on my company’s intranet. I needed it to persist cookies across sessions (to handle SSO) and then scrape a Kanban board. Existing AI browser tools (like current MCP implementations) often force unsolicited data into the context window—dumping the full accessibility tree, console logs, and network errors whether you asked for them or not. webctl is an attempt…
What it does
In the maker’s words, at launch
Hi HN, I built webctl because I was frustrated by the gap between curl and full browser automation frameworks like Playwright. I initially built this to solve a personal headache: I wanted an AI agent to handle project management tasks on my company’s intranet. I needed it to persist cookies across sessions (to handle SSO) and then scrape a Kanban board. Existing AI browser tools (like current MCP implementations) often force unsolicited data into the context window—dumping the full accessibility tree, console logs, and network errors whether you asked for them or not. webctl is an attempt to solve this with a Unix-style CLI: - Filter before context: You pipe the output to standard tools. webctl snapshot --interactive-only | head -n 20 means the LLM only sees exactly what I want it to see. - Daemon Architecture: It runs a persistent background process. The goal is to keep the browser state (cookies/session) alive while you run discrete, stateless CLI commands. - Semantic targeting: It uses ARIA roles (e.g., role=button name~="Submit") rather than fragile CSS selectors. Disclaimer: The daemon logic for state persistence is still a bit experimental, but the architecture feels like the right direction for building local, token-efficient agents. It’s basically "Playwright for the terminal."
Does the same job
all alternatives →- REReverse-engineering web apps into agent toolsJul 2026 · ▲96
Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes. The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort. Check out these short demos below that show the agent in software you're probably familiar with: - Jira: https://demo.frigade.com/hn?skill=jira - Spotify:…
- WEWeb-eval-agent – Let the coding agent debug itself2025 · github.com · ▲84
Hey HN! We’ve been building an MCP server to help AI-assisted web app developers by using browser agents to test whether changes made by an AI inside an editor actually work. We've been testing it on scenarios like verifying new flows in a UI, or checking that sending a chat request triggers a response. The idea is to let your coding agent both code and evaluate if what it did was correct. Here’s a short demo with Cursor: https://www.youtube.com/watch?v=_AoQK-bwR0w When building apps, we found the hardest part of AI-assisted coding isn’t the coding—it’s tedious point-and-click…
- ICI created a browser automation tool2022 · rocketride.io · ▲176
I created this tool a few months ago to automate some of my tasks. I use it to collect prices and files and to get notified when something changes on certain websites. The task runner uses Playwright. I'm not sure if it can be useful to anybody else besides me :) Any feedback is welcome.
- TBTabstack – Browser infrastructure for AI agents (by Mozilla)Jan 2026 · ▲130
Hi HN, My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag... Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site. Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
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Automatic AI-powered code reviews the moment you open a PR
Dev tools · Jan 2026 · kilo.ai
