82 years. 52 weeks per year. ~4200 boxes.
Your whole life on one screen.
It hits different when you start counting the ones already behind you.
https://t.co/Y6g1WcT133
@thsottiaux Could you add an option to get insights into our prompts and flows to spotlight areas for improvement? It would be good to have a side agent that can review our flows and give advice.
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Thanks a lot for the reply. I get the value of a separate file to track progress, but what I'm really struggling with is how to actually constrain the LLM to follow very specific directions over long sessions.
Even with a progress file, I often end up after hours of work realizing the output has slowly drifted, it took shortcuts, ignored some architectural constraints, or the quality/style became inconsistent. Then I have to spend a lot of time fixing it.
How do you prevent that? What concrete techniques do you use to keep the model tightly aligned with what you want (specific rules, output style, quality bar, etc.) across many iterations?
Maybe I'm missing the point here.
Honestly, learning to code 100% by hand, no. But understanding how everything works and what agents actually write when you prompt them, yes, absolutely. I've been developing every day for almost 10 years now, and when you have an agent that can churn out 1000 lines of code in a few minutes, you better be able to read whether it's good or not. Otherwise it's really easy to end up with a completely unmaintainable codebase