My Best Resume Material Was Hidden in 700 Conversations, But I Was Too Lazy to Dig

Here is what happened.

I was preparing an internship resume for an AI Coding product manager role. My daily development depends almost entirely on AI. I switch between Claude Code and Codex, and in three months I burned through 3.95 billion tokens. If these experiences went into my resume, they would be very solid material.

The problem was that all this material was scattered across hundreds of chat windows.

700 sessions, 96 projects. What I said to AI in each project, how I collaborated with it, what traps I stepped into, all buried inside jsonl files and git commits. Go through them one by one? Just thinking about it made me want to die.

So I did the most natural thing: I let AI go dig through it itself.

I sent out 7 subagents at the same time. One scanned my token usage data. One went through Codex conversations to find how I built my electronic diary. One reconstructed the real bug-fixing timeline from git log. One read my global config files to understand why I chose one model over another. Every agent had a clear search scope and a clear task target.

A few minutes later, 19 research reports came back.

Let me give you a few examples so you can feel what I mean by "accurate."

Report R10 told me that Codex accounted for 60% of my total token consumption, while Claude Opus 4.6 accounted for 78% of my total conversations. The former handled code, the latter handled writing. I knew I had this habit, but I had never quantified it. The agent went straight into data.json, broke it down by model, and calculated it.

Report R4 reconstructed a bug story. My cross-device file transfer tool Folip had a problem where uploads succeeded but Android could not download the file. AI kept circling around the client code and could not find the cause. At the time, I did one thing: I asked AI to read the Alibaba Cloud OSS access logs itself. It found the issue instantly. The pre-signed URL had missed Content-Type in the calculation. The agent reconstructed that entire timeline clearly from git commits.

Report R8 dug up a detail from my electronic diary project. When Codex helped me proofread handwritten diary entries, it would "take initiative" and polish them, expanding "但她很 normal" into "但是她也很 normal." Because of that, I wrote 7 "do not" rules and folded them into the prompt. The agent found the exact jsonl file path and timestamp.

If I had gone through all this myself, it might have taken a whole day, and I probably would have remembered the details wrong. Letting AI dig through its own memory was both fast and accurate.

But that was not the most interesting part.

The most interesting part was the correction process between me and AI. In the drafts the agents brought back, several places were reversed or overstated. For example, one agent got my logic for using Codex and Claude backwards, and I immediately corrected it. Another wrote about a technical detail called "15% context optimization," and I said, "I don't really understand this myself," then deleted it directly. Another described my bug discovery process as "manually checking logs," and I said no, that was wrong. I had asked AI to read the logs.

Every round of correction moved the resume closer to reality. In the final version, every number, every story, and every judgment was something I had actually done, actually thought through, and could actually explain clearly in an interview.

Honestly, I think this is what an AI-native resume-writing process should look like. It is not asking AI to invent a polished resume for you. It is asking AI to dig the best material out of your own real data, then having you judge what is truly you and what is not.

AI has a better memory than you do, but only you know which memories are yours.