zlacker

>>laiysb+(OP)
With how stochastic the process is it makes it basically unusable for any large scale task. What's the plan? To roll the dice until the answer pops up? That would be maybe viable if there was a way to automatically evaluate it 100% but with a human in the loop required it becomes untenable.

>>margor+72
> What's the plan?

Call me old school, but I find the workflow of "divide and conquer" to be as helpful when working with LLMs, as without them. Although what is needed to be considered a "large scale task" varies by LLMs and implementation. Some models/implementations (seemingly Copilot) struggles with even the smallest change, while others breeze through them. Lots of trial and error is needed to find that line for each model/implementation :/

>>diggan+f2
The relevant scale is the number of hard constraints on the solution code, not the size of task as measured by "hours it would take the median programmer to write".

So eg., one line of code which needed to handle dozens of hard-constraints on the system (eg., using a specific class, method, with a specific device, specific memory management, etc.) will very rarely be output correctly by an LLM.

Likewise "blank-page, vibe coding" can be very fast if "make me X" has only functional/soft-constraints on the code itself.

"Gigawatt LLMs" have brute-forced there way to having a statistical system capable of usefully, if not universally, adhreading to one or two hard constraints. I'd imagine the dozen or so common in any existing application is well beyond a Terawatt range of training and inference cost.