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Yeah, all those losers in the office that couldn't find their way out of a paper bag suddenly look as productive as you do.
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They actually don't. A lot of people still have no clue how to drive these tools.

Honestly I think that is more of a symptom of how slapped together the AI software stacks feel to an outsider.

All my dev friends swear this is the future, all my scientist coworkers just say it's a useful tool.

I have heard the pitch and have people explained how it works... But I have yet been handed a guide on how to set myself up properly to get the most out of AI. Like I have both a corporate openai gui agent as well as cli setup with a monthly allotment of tokens. But there is no clear standard guide on how I should be setting things up, like we do for python in books "here are the building blocks for python"/aka learn python the hard way


I've been wanting a guide like you are but a question I haven't been able to answer is: what would go in it?

LLM and harnesses are changing so fast that if I had a guide from 6 months ago I'm not sure how relevant it would be today.

I'm guessing that when things calm down and the landscape isn't changing as much then we will get the guides.


In my experience AI widens the gap, not closes it. The best engineers are 10x more productive, the worst engineers maybe 2x.



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