Talking the problem to death with the AI before implementation is a nice zone for me. I feel productive, get good results out of the AI, and still largely understand the code. That’s the part of the AI revolution that I feel has made me a better engineer because I argue about design and architecture all day with a robot.

yes exactly. Too many people ask AI to one-shot complex tasks, and wonder it behaves like a junior asked to rush something.

I have my own skill: 5 rounds of research/planning/test-planning. Interactive with me in loop for all important decisions. Starts with high level shape, then details. Planning can take 2-3 days of my time, then the implementation agent can take many hours (Opus 4.7). It splits the implementation across many phases/commits, each with its own code-review fix loop. Deep code review at the end can take another hour or two. It opens a PR, Gemini reviews, it reads out and resolves those issues.

Projects still take days or weeks, but 5x faster than doing it all myself.

Edit: the skill - https://github.com/scosman/vibe-crafting

When I use ai to code this is pretty close to my workflow too but I find it ends up taking at best just as long as if I were to write the code myself. If m some cases I’ve thrown away what the ai has done and just done it myself. I think that’s just a skill people need to learn - at a certain point you have to cut your losses. I’ve seen some coworkers argue back and forth with an llm trying to get it to do something. Especially true on simpler changes.

And then Anthropic has an outage and you what...have a coffee break until then? All that time babysitting the AIs just to be a little faster but probably with less knowledge/control over what they did?

Similar approach, but I also go a step further with some basic manual architecture/high level contract/stubs setups, just to keep it consistent with other systems (and easier reading as well).

You helpfully cite Claude w/ Opus 4.7 max and Codex w/ GPT5.5 xhigh fast, but what "AI" do you use for the initial design?

You've essentially promoted yourself from coder to engineering manager, trading syntax fatigue for the mental marathon of refereeing specialized AI developers to ship v3-quality code on the first try.

maybe it's dumb question, but how do you feed the results of one agent to another? do you copy and paste manually? or how do you do it programmatically?

At this point one might as well code by themselves

I have a very similar workflow, and experience similar temperaments from the agents. I also find anecdotally that they are moderately competitive - you get very different attention from them when you say "competitor X wrote this - please find all bugs" than when you say "you just wrote this - please find all bugs".

I've noticed the following really helps (most important at end):

1. Have claude form the plan and converse with a simple "Note any concerns with this plan" type plan-critic agent.

2. Let it run.

3. After (with everything in context) have it make a future_recommendations.md.

4. Have it make a plan.md to implement those future recommendations, conversing with the plan critic..

5. Clear context. Repeat with 1. Do this loop a few times, with some feedback from actual review thrown in.

But, most importantly, because Claude will aggressively try to maintain code "as is", and happily build on it's previous crap, while preferring to hand roll implementations of everything, add something like this to memories/directives:

* When evaluating designs, default to "pull in the library" over "hand-roll it." Hand-rolling is much worse than a dependency.

* "Precedent" / "matches house style" / "reuses existing pattern" / "consistent with what we already do" are not valid engineering arguments.

* This project is still in the development stage with no real deployments. Mitigation costs and existing precedence are not a concern.

With these, in the last week that I've started using them (after inspecting the insane justifications for leaving crap design decisions in the plans), Claude went from junior level slop that required more oversight than it was worth to something very reasonable, using standard libraries, requiring nudges for architecture rather than pure "wtf!?".

I think they've fine tuned heavily towards "don't rewrite the codebase" tuning, which completely rational from multiple perspectives, but also not appropriate for new code.

I do enjoy a considerable daily token allowance, so this may not apply to everyone.

This all sounds insane. If it requires so much back and forth with the AI why on earth wouldn't you just write the code yourself? At least then you build the mental model of the code and keep your brain healthy. Reading the comments in here about all the hoops people are having to jump through just to do the same thing they were doing a year ago without AI... and spending a fortune to do it! I think you've all got AI psychosis.

That sounds too much like three weeks of work saving you three hours of planning.

In my experience, software engineering is a matter of knowledge. Understanding it and then coming up with a solution. The latter is a flash of insight that comes mostly from experience. Then you gather more information to flesh it out, or brainstorm it with your colleagues.

What you're describing sounds more like a ritual of doing busy work than anything practical. Because tasks vary so much. A feature may be huge, but you take care of it in a day with copy pasting because you already have all the building blocks in other files. And something may be twenty lines of code, but you spent the whole week sweating on it (concurrency stuff maybe). Those ritualistic workflows sounds more like someone imagining software development than actually doing it.

Low frequency defensive long drawn out back and forth bullet dodging vibe coding should be called "serpentine coding".

The In-Laws (1979): Getting off the plane in Tijuara:

https://www.youtube.com/watch?v=A2_w-QCWpS0