Okay, this is insanely cool.

这个价位已经很划算了吧

不提錢，我好像沒什麼動力更新

The biggest mistake in content creation is confusing activity with progress. Over the past few weeks, I've been pushing my X account pretty hard. Nearly 200 posts and 300 replies a day. The results were encouraging. Daily impressions reached 10K, sometimes much higher. Engagement improved, and my audience kept growing. But I started noticing something. I was spending more time posting, replying, and checking analytics than actually learning about AI. I started this account to explore research, test tools, build things, and share what I learn. Instead, I was becoming better at producing content than developing my own understanding. That's not what I wanted. So I've decided to cut my X workload to 20%. Less posting. Less replying. More reading, experimenting, building, and thinking. My impressions might drop. Growth might slow down. That's fine. I'd rather spend my time becoming someone with something worth sharing than constantly trying to have something to post.

让AI给我搜日报，总结后讲给我听

你知道的太多了，击毙你

一天才24h，这挤挤那挤挤，感觉像小四爷那个段子，你知道我一天吃几顿饭啊

Human Approval Is Not an Agent Safety System The biggest mistake in agent safety is treating human approval as a kill switch. It isn't. Imagine an agent researching a bug. It can search docs, query data, run tests and draft a patch on its own. Then it wants to deploy code, send an email, modify a database or process a payment. Now you stop it. A human gets: Approve. Reject. Edit. This is the Human-in-the-Loop pattern, and it makes sense. Let agents handle cheap, reversible work. Put humans in front of expensive or irreversible actions. But there is a problem. Approval only controls the actions you remembered to put behind an approval gate. It does not answer a more basic question: What can this agent reach in the first place? Suppose the agent is running in an environment with access to the production database, internal APIs, shared files and the open internet. You may have carefully gated the "delete customer" tool. Great. But the agent still has a huge blast radius if something goes wrong through another path. That is why approval and isolation solve different problems. Human approval asks: "Should this action happen?" Sandboxing asks: "If the agent behaves badly, how far can the damage spread?" And there is another layer that I think agent systems still underuse: per-run network scope. A GitHub issue triage agent may need GitHub and a package registry. It probably does not need your production database. A documentation agent may need the web and your docs. It probably does not need your internal admin APIs. Permissions should follow the task, not the agent. Give each run only the network access it actually needs. Then revoke it when the task ends. This sounds obvious, but convenience is the enemy. During development someone says: "Just give it shared network access for now." The prototype works. The prototype becomes useful. People start depending on it. Six months later, "for now" is production architecture. So I think useful agent safety has at least four layers: 1. Separate low-risk reads from high-risk writes. 2. Put human approval in front of irreversible actions. 3. Sandbox execution to limit the blast radius. 4. Scope network and tool access per run. None replaces the others. And the default matters. Security that engineers have to remember to turn on later usually arrives too late. The safest agent is not the one you trust to behave perfectly. It is the one that can still be useful when you assume it eventually won't.

你怎么知道我考了电工证