🦄 Somewhere between studying for a certification, building an agent I only understand in theory, and waiting on a pipeline that isn't automated like I want it to be, I kept hitting the same dead air: AI is coding, and I'm... what, exactly? So this post is me answering my own question — and inviting you to throw in more ideas. 🕰️✨ PS: @francistrdev the memes are special since you asked nicely last time 😆
This is for those of you who have decidedly finished hand-holding AI at every step of the process. If you're still prompting to change a method or implement a new environment variable, then this post is not for you.
If you're still reading then I'm guessing you've ran into the same problem that I have.
That can take 5 minutes or 20, and it fundamentally changes the idea of the deep-focus time blocks we previously needed to be truly productive. We're not deep diving into the codebase regularly anymore, so what do you do with that time?
Here's some ideas and my thoughts on them, covering both personal projects — meaning there's no deadline — and work, where enterprise wants the deployment yesterday. Slow Down 🐢🧘
Honestly, I'm terrible at this option. I want to be doing something at all times.
However, I recognize that sometimes I'm responsible for the same terrible output we accuse AI of: going with the first thing that makes sense and not stopping to consider all options every step of the way.
I'll catch myself stopping generations or retracting a previous statement when I could have sat with the problem statement a bit longer and came up with a better answer than the first one I had.
It's also the only option on this list that makes the next run better instead of just filling the time — sitting with the story a bit longer means I already know what I'm going to reject before the diff shows up. Watch the Direction, Not the Diff 🧭🩻
This one is also difficult for me to accomplish because it feels like doing nothing.
I honestly do much less of this now than I used to. Before, I would watch the output, ready for something to go wrong at any point.
Then the trust grew — and yes, it still messes up sometimes — but that doesn't mean I'm back to watching it every step of the way.
If I am watching it, I'm looking more at the direction and less at the code itself. Is it touching the right portion of the file system? If I asked for a color change, but AI ended up changing a data file, that's a clue something went wrong somewhere. And keep an eye on the tests, because it will happily change them to go along with the code. The Paperwork Nobody Automated 🗃️🪦
PR reviews, enterprise production requests, testing documentation — all the things that go along with a deploy when the pipeline isn't automated like you want it to be goes here — you know what I mean... Study in the Cracks 🎓⏳
I'm currently studying for the GCP Architect Certification, so a lot of my free 5-minute time blocks are spent watching a short section of a video or taking a practice quiz — or a part of one, more likely.
It's nearly impossible to put aside time that doesn't get overtaken by a higher priority, but small, focused learning is doable in short intervals. The "Quick" Slack Question 💬🪤
Helping the team usually means keeping up with my Slack messages for the day and monitoring channels that are particularly interesting — namely the AI ones. There's usually a quick question to answer or a quick call to make.
The challenge is making that "quick" call not take an hour while your AI is finished and now waiting on you.