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> In the past (before LLMs) it was already hard to keep up, but now it feels like there's 10x more things waiting at any given time, and there could be 10x more if everyone just "optimized" and streamlined processes fed the AI even more tasks in parallel faster. I find LLMs to help me manage the unrealistic workload I have, because at least now it's feasible instead of just getting more work piled on top of me with a never ending backlog (that people actually expect me to thin, not let grow). Add on top of that colleagues that would have death by commitee'd many ideas and now just have to argue against actual MVPs that work instead of ideas (or can be proven to not work and discarded without wasting time on them in some cases), and I don't even hate my job as much! It's just that to ensure that the technology is not a net negative, I need millions upon millions of tokens every single day (tool runs, adversarial reviews, testing), but once you get that inflection point, alongside needing a good enough model, the floor for which currently I'd say GLM 5.2 on Max reasoning reaches, or use something like SOTA Anthropic/OpenAI models, it becomes a pretty good way of working. That said if you have missing pieces there (e.g. using cheap models that aren't very good), the curve of getting stuff done can go downwards and you'll just end up with a lot of slop - useless docs, bad code and an ever increasing amount of technical debt. On average, each task that I do, needs about 15 minutes to 2 hours of planning and making the agents explore the codebase and refine the plans first. Curiously, in my case this leads to less burnout cause I can actually pause and grab a drink, meal or go for a walk, while parallel agents do the work, once I've planned things well enough and have dispatched something that will work for 1-4 hours. I don't have to review their output immediately once they finish but can just batch things. |