Here's a question, to people who delegate all their coding to AI agents now: What are you doing to keep your mind exercised in all this suddenly new free time you have? Personally I find talking with agents extremely tedious and unrewarding; I would rather just write the code, even if it's simply because it's more interesting than playing Sudoku or whatever the fuck you do when you have nothing to do with your brain.
And talking to AI agents is not the same as using your brain. It's frustrating, it seems to be intelligent, you think you're using reason with them, but it rapidly degrades your ability to reason. It encourages you to accept answers without checking them thoroughly. So much for the skills of accuracy and integrity you built up. It's a sugar rush.
>Here's a question, to people who delegate all their coding to AI agents now: What are you doing to keep your mind exercised in all this suddenly new free time you have?
If you have a lot more free time you're not taking full advantage of agents. The time saved coding allows working on 4-6 projects simultaneously, and the majority of work is the hard design/product decisions that the agents can't do well themselves. Working on the hard parts of half a dozen projects simultaneously is far more mentally taxing than just hand-coding a single project, and with that many agents there'll usually always be one with a question for you so you won't have much extra free time.
The majority of my time coding is not coding; it's thinking through the hard design/product decisions, as you said (or how business logic flows through the UI). The time coding is very, very explicitly putting those decisions into UI and backend logic in a way that would take me longer to explain perfectly to a bot than it does to code. LLMs have not saved me time on any serious project, aside from helping to scaffold things, because the results when I ask them to write a block are sub-par.
I don’t „talk” to my agents. I tell them what to do. If I need to „talk” it means something is going wrong and we need to figure out why agents are doing bad work - and this is intellectually challenging.
The effect you describe I had at times but with older/dumber models where you could spend a ton of time and the model was either too dumb to do it, or had a model dementia later in the context. Or when I didn’t really understand why it did a bad job and tried to stumble around in the dark - very mentally numbing.
Nowadays the model usually just had some bad tools or information required to do the job.
If you're not even asking the questions, you probably don't know what it's writing. That's essentially the same as saying you don't care about the quality of what you produce.
That's not new, it's just not something most people would've admitted to until recently.
Of course I care deeply about the output, I work with it daily.
I just don't care how it looks to a human - LLMs have their own criteria of what is readable and manageable to them, and if you try to enforce human readability you will bump into a ton of antipatterns that will cause more and more issues.
Also, the default code management tools from Codex/CC are either basic (Grep/Edit), or - if you try to use some open source ones - unlikely to be used by agents. It took some time, but once I got agents to navigate the codebase efficiently, and produce code that is accessible to them, I stopped having problems with bugs or increasing complexity.
If you begin treating human interventions as an error in a process - and do after action reviews and so on after each such intervention - then after a few months you end up with quite a smooth-working system.
> I don’t „talk” to my agents.
Though, if you're still typing, you might want to try recent STT software.
Oh yes, Whisper/Whisperflow in some places, and Groq from Elevenlabs in some other ones. It's hard to get back to typing.
The biggest challenge is in an office, or other places where I want to maintain privacy. Ideas?
By "telling" vs "talking" I meant - I have a setup that is 95% noninteractive - oneshotting most tasks, with a decent verification setup and a good memory/hooks to be confident the system does what I want and is reliable.
When something off happens, I spin up an after action review session that pinpoints the guilty sessions, interviews them, the whole investigation, and either handles improvements in the process by itself (most of the cases), or asks me for feedback. For the last three months I don't think I had a place where my input was really needed. But the beginnings were brutal.
"talking" happens only when:
- I truly need to iterate on the output
- There is some frontier-level research (i.e. working on truly novel algorithms/math)
- New UX - even Sonnet5.5 and Fable are still quite schematic and they have difficulty figuring things out from first principles. Astra is smarter but has it's own problems
- Infrastructure problems
Doing more, understanding more the tech of the project, exploring more ideas, the list is infinite
Who got more time thanks to AI ? I think a lot of us are working more instead
You need to switch into a product manager/QA/UI,UX mindset.
Is what is being built the right thing? Does it work? Is it usable? Has it been built in an appropriate way? (Note this is not a code thing, it’s an architectural question)
Traditionally developers have outsourced these things to other people.
There’s a further thing you should be doing - if the questions above didn’t pass - why not? What went wrong in your process?
> keep your mind exercised
OK, so I'm still very low on the spectrum of "I just command it and sit back", but I think the final toolbox will at least include adversarial questioning of the bot. For example, "Why does that code do A, wouldn't B be better?" or "Would this code break if a C event occurred?"
Sometimes it reveals a bug or a questionable implicit assumption, other times it leads me to see another part of the code, and sometimes the thing just lies at me... But it does help you keep some critical-thinking practice.
Another task might be to find and refactor/rename parts of the code that are just... not easy for humans to grasp. Some readers are thinking: "No, the next edit will be by LLM anyway, leave it so the bot understands", however I think that's short-sighted: Either these things are going to become good-enough that they should cater to human comprehension, or else they've reached a plateau and we'll want something that can resist the chaos-rot.
I think I'm where you are. Reading all the code it spits out, and then a lot of adversarial questioning. (This is what I mean about "talking to the AI", which was brushed off by another commenter). Naming conventions, decisions about classes or inline code, all these things I would not do.
So then I spend all this time getting a mental map of the code it wrote, and making my own decisions about that code. Then I ask it to rearrange it in consistent ways. Then I find bugs or oversights. Etc.
What I'm trying to say is that while all of that process with the LLM is modestly intellectually stimulating, it's not as stimulating as just writing the code myself, and it's also incredibly frustrating to have to go through the agent. And yes, I could just rewrite the functions my own way, but once I'm in a project that's LLM-driven, the temptation is to make the LLM fix it instead of fixing it myself. Whereas in a project that I'm writing from scratch, I wouldn't make those mistakes or have to think about those stylistic things, I would have already seen the bugs that it missed before I wrote the code, and I would be using my mind to its fullest potential instead of delegating my thinking to a stupid robot.
Running 20 agents at the same time means a lot more task switching, its closer to team management, you may have one agent doing basic grunt work, so can be let alone, another one may be planning and designing, so needs a little bit more q&a, and others may be problem solving, and that does take a lot more supervision. But I have never felt like I have any free time, or down time, at the end of the day I feel far more mentily exhausted (in a good way) than any day before agentic development.