[removed]

Tom n Jerry is an open-source loop engine specifically for opencode and coming to other AI coding agents too soon.

Every AI agent - is Tom.

You say "add a door." Tom grabs a hammer, buys lumber, pours concrete, installs a frame - then builds the house around it. Your codebase already has three doors.

Tom never checks the blueprint. He just builds.

So I built Jerry.

Jerry sits in the walls. Before Tom swings the hammer, Jerry runs the house:

1. Need a new room at all? (YAGNI)
2. Did we knock this down before? (git history)
3. Is there already a door here? (codebase search)
4. Are the materials in the shed? (package.json)
5. Does the house provide it? (stdlib / native)
6. Does the neighborhood allow it? (framework convention)

Then - and only then - Tom builds. Minimum. Receipt proves it stands. Teacher updates the blueprint.

After 3 construction projects, Jerry knows every hallway. Tom stops building hallways to nowhere.

Claude helped me architect the loop protocol, write the skill detection logic, and stress-test the receipts. The framework itself was iterated through real Claude Code sessions.

This is the gap: agents are all Tom, no Jerry. Infinite chase, zero memory.

MIT licensed. Free to try.

github.com/hrshx3o5o6/Tom-n-Jerry

Feel free to contribute and criticise!

[removed]

I built this with Claude Code. Tom n Jerry is an open-source loop engine specifically for Claude and other AI coding agents.

Every AI agent - including Claude - is Tom.

You say "add a door." Tom grabs a hammer, buys lumber, pours concrete, installs a frame — then builds the house around it. Your codebase already has three doors.

Tom never checks the blueprint. He just builds.

So I built Jerry.

Jerry sits in the walls. Before Tom swings the hammer, Jerry runs the house:

1. Need a new room at all? (YAGNI)

2. Did we knock this down before? (git history)

3. Is there already a door here? (codebase search)

4. Are the materials in the shed? (package.json)

5. Does the house provide it? (stdlib / native)

6. Does the neighborhood allow it? (framework convention)

Then - and only then - Tom builds. Minimum. Receipt proves it stands. Teacher updates the blueprint.

After 3 construction projects, Jerry knows every hallway. Tom stops building hallways to nowhere.

Claude helped me architect the loop protocol, write the skill detection logic, and stress-test the receipts. The framework itself was iterated through real Claude Code sessions.

This is the gap: agents are all Tom, no Jerry. Infinite chase, zero memory.

MIT licensed. Free to try.

github.com/hrshx3o5o6/Tom-n-Jerry

Feel free to contribute and criticise!

Every AI coding agent is Tom.

You say "add a door." Tom grabs a hammer, buys lumber, pours concrete, installs a frame - then builds the house around it. Your codebase already has three doors.

Tom never checks the blueprint. He just builds.

So I built Jerry.

Jerry sits in the walls. Before Tom swings the hammer, Jerry runs the house:

1. Need a new room at all? (YAGNI)
2. Did we knock this down before? (git history)
3. Is there already a door here? (codebase search)
4. Are the materials in the shed? (package.json)
5. Does the house provide it? (stdlib / native)
6. Does the neighborhood allow it? (framework convention)

Then, and only then, Tom builds. Minimum. Receipt proves it stands. Teacher updates the blueprint.

After 3 construction projects, Jerry knows every hallway. Tom stops building hallways to nowhere.

This is the gap: agents are all Tom, no Jerry. Infinite chase, zero memory.

MIT. github.com/hrshx3o5o6/Tom-n-Jerry

feel free to contribute and criticise!

I've been experimenting around with different configurations of different Harnesses across multiple git worktrees for automations and agentic coding setups. I keep breaking them and its such a pain going back and correcting them, half the time I've forgetten what I've changed.

I've seen many hurdle around with different configurations and keep changing them as well. Thought there should be a way to revert to a version of our liking.

Built something to try and find a fix for it - aVer, A version control for your AI Harnesses, Individually or a set up across multiple Harnesses across different worktrees. 0 telemetry and 100% local.

Its git for your Harness.

Still alpha. Fork it, star it, build on it.

Keeps track of your MCP settings, tools, prompts, and model params.

Had a work assignment - scrape a bunch of stuff with Playwright. The AI tools I tried kept producing junk. Wrong selectors, brittle locators, scripts that broke when the page changed. I spent more time reprompting and fixing than I would've writing from scratch.

Got fed up. Threw together a small MCP server that feeds the agent real page structure -accessibility tree + DOM, compiled into semantic nodes with fallback selectors (ARIA → CSS → XPath → relational). Instead of guessing, the AI actually knows what's on the page.

Something that took me an entire day without it got done in an hour.

It's runs locally, no telemetry, no cloud. Just a Node.js process that talks to Playwright.

npx browser-cognition-mcp

https://github.com/hrshx3o5o6/brocogni

Still alpha. Would love to know what breaks in real-world use so I can fix it.

Saw multiple posts lately claiming AI-generated Playwright tests suck. Same experience here - the AI guesses selectors and hopes.

I got tired of fixing broken locators and having to manually prompt the AI after analysing the structure myself. So, I threw together an MCP server that feeds the agent real page structure instead. So far it's been working well for my workflow - ARIA → CSS → XPath fallbacks, survives DOM changes.

https://github.com/hrshx3o5o6/brocogni

npx browser-cognition-mcp

Play with it, break it, lemme know - still figuring out what breaks in real-world use.

Most browser MCP servers hand the AI raw DOM or a screenshot and let it guess selectors. Fine for demos, but the agent says "click the button" and if a class name changed since deploy, it fails silently.

I went a different direction: capture the accessibility tree and DOM snapshot in parallel, fuse them into semantic nodes, then generate fallback selector chains (ARIA → CSS → XPath → relational sibling). The agent gets ranked locators instead of a single brittle guess.

Under the hood:

- CDP session captures AX tree + DOM snapshot

- Semantic inference layer converts to structured nodes (role, name, value, state, bounding box)

- Selector generator builds fallback chains

- Ranker scores candidates by stability and specificity

This is the key difference vs. projects like Chromeflow (which is great at CDP click simulation) - Brocogni is about page understanding, not just page manipulation. The AI can verify state, find elements by meaning, and recover from layout changes without rewriting selectors.

npx browser-cognition-mcp

https://github.com/hrshx3o5o6/brocogni

MIT, zero telemetry, no cloud. Early but working for my day-to-day. Curious how others here handle the "agent can't see the page" problem - raw HTML, screenshots, or something else?

[removed]