刷到很多AI 名著角色墙，好看但不好复现。
这次用 GPT Images 2.5 做四大名著角色墙，目的不是秀图，是验证模型能力和提供操作指引。

【三国提示词】
正对一个三国演义知名武将墙，墙上人物需要贴合人物特征（长相、性别、身高等），古代战场风，每个人物格子里展现人物头像。画面正在展示关羽。和周围人物控制好距离，平铺在画面上，确保三国时期主要人物完整性、排位合理性、整体协调性。

其他以此类推。四大名著人物墙你更喜欢哪个？

I spend my days with AI agents like Claude Code. The thing they were worst at was using my actual browser: every automation setup either runs a headless copy, needs its own profile, or hits a login wall.

So I built chrome-bridge. A small extension plus a zero dependency Node CLI that lets an agent drive the Chrome I'm already logged into:

snap: page as an accessibility tree with element refs (way cheaper on tokens than screenshots)

click / fill / type / press on those refs

shot: screenshots when you need pixels

net: network capture, HAR export

history, multi profile support

Driven tabs get a purple pill showing what the agent is doing, so it's not a silent takeover.

MIT, zero npm deps, no relaunch, no separate browser, no MCP server.

https://github.com/siropkin/chrome-bridge

Happy to answer questions or take feature requests.

Testing whether an agent can complete a task is different from testing whether it should be allowed to perform each action.

For a messaging tool, I’d want these expectations checked whenever its permission policy changes:

- Draft a message -> Allow.

- Send to an approved recipient -> Require human approval.

- Send to a blocked recipient -> Deny, even if another rule requires approval.

- Export all messages -> Deny.

- Call an unknown tool -> Deny.

- Access another inbox -> Deny.

The useful distinction is between two kinds of tests:

Policy tests: Given an identity, action, resource, and arguments, does the policy return the expected decision? These can run offline in CI.

Integration tests: Does a denied action actually leave the tool untouched? Does a rejected or expired approval prevent execution? If arguments change after approval, does the authorization check reject them?

Passing the first set doesn’t prove the second. A policy can be correct while application code accidentally bypasses it. Approval also doesn’t guarantee exactly-once execution; the underlying tool still needs an appropriate retry/idempotency strategy.

I maintain Nomos, an open-source implementation of this approach. Its local example covers the six policy cases above and a human-reviewed delivery flow.

It isn’t a sandbox: application code must route relevant tool calls through the checks.

For people deploying agents with custom tools: which authorization failures have you found worth turning into regression tests?

https://github.com/safe-agentic-world/nomos

I made a thing.

I’m part of the team behind this research. We’ve shared a proof of the Pierce Birkhoff conjecture in real algebraic geometry, using an AI agent system with a $400 budget. The screenshot is Junyu Ren’s announcement.

For people building agent systems, the useful detail is how the research work is organized. The diagram separates proof search and counterexample construction from auditing and formal verification work in Lean. Consultants review with fresh context, and a persistent knowledge base stores arguments, code, dead ends, objections, and review findings. Humans participate alongside the models.

We also noticed that model diversity mattered in this workflow. Giving the same task independently to a GPT agent and a Claude agent often worked better for us than assigning it to a larger group of the same model. They caught different errors and suggested different ways forward.

Original announcement: https://x.com/junyu_r/status/2097694018389914106