And soon enough it would need a project manager and HR to keep all the agents in line.

I hope the future becomes AI b2b sass agents selling b2b sass to other agents, just tools buying tools that in turn buy other tools ad infinitum

T shirt estimation doesn't make any sense for AI dev, not one bit. They get epic long features done in hours, and all the shirt sizing comes from cases where the agent circles the drain and needs to be guided, which isn't predictable.

The shirt sizes now are for manual acceptance testing.

My experience with AI tooling is that while it's really useful and great - I don't think i've ever seen a LLM complete an epic long feature well full stop.

Don't get me wrong, it's definitely improved my workflow and efficiency, but you must be winning at roulette if the model is performing well on anything that can't be googled and implemented witihn a similar amount of time.

unless it's claude, where even simple styling changes seem to become epics just when it wants to spit out an extra few thousands lines of code

If you go back and forth with chatgpt/gemini on architectural details first, then get chatgpt to produce a hyper detailed spec (like, almost a program claude can execute), you can get claude to run for 2-3 hours at a shot (particularly with a hook to prevent early stopping). Require >85% test coverage, and bake very clear e2e test paths into the spec, and Claude can come surprisingly close to one shotting big things.

I found a boost adding LikeC4 to the spec.

Fair enough! I'll need to give it a try - I tend to mostly use these agents as idea testers as have found them limiting beyond those concepts, but it sounds like they may be quite useful using this spec, thanks!

> T shirt estimation doesn't make any sense for AI dev

It doesn't make sense for NI (natural intelligence) dev, either. Even SCRUM doesn't make much sense. The only Agile thing that really makes sense is Kanban, which is actually known to computer science as dispatch queue.

In the 60s, OS researchers spent time figuring out how to optimally schedule resources for computation. Today, almost nobody uses these techniques. (This is known as "waterfall" in PM parlance.)

It turns out, the cheapest way to schedule computing resources is a simple dispatch queue. Why spend extra time figuring out in what order things need to be done, or how long they will take, if they need to be done anyway? It never made sense and it doesn't matter whether the agent is NI or AI.

People are already doing waterfall with their quest for the perfect prompt that will produce a complete, fully functional implementation.

(These people dont realise that there a lot of tradeoffs to be made that pop up during implementation)

Yeah this is my problem with building my cathedral up front in a prompt, I just don’t work that way. I need to see some interactions and mess with it to tell how it should work.