I still believe a correct framing on LLM usage is that they can get you 80% of the way upfront, but that last 20% starts to become impossible to overcome. The proper way to use an LLM is to do the difficult manual 20% upfront. Once you have some decent structure, decent API surface area, decent data structures laid out, decent base libraries. Then you can largely prompt your way through the last 80%. While the first 20% which you did manually, intentionally and fully comprehend. That 20% essentially becomes part of your development harness, almost a prompt itself. Where you can steer its overall architecture, or data design, or other critical pieces by keeping a grip on that first manually done 20%.
Something real concerning with this. Knowing what to do in that first 20% requires advanced knowledge of the problem domain you are going to deal with. It is a staff or principal level engineer domain. Which most simply can't do.
Which means most people are going to run the gauntlet heavily relying on prompts, and are going to cover some incredibly impressive ground very quickly. Because 80% of the way is quite a long ways there. It looks like it could almost be done. But then they will get stuck in that last 20% and will never be able to make it perfectly polished. Nor perfectly sable. Nor remove all the odd quirks. Nor overcome the last pieces of performance or memory issues. It will basically hit a plateau where they lose control of it. Or it becomes unwieldy to take it in a new complex direction, or add some novel complex functionality to it. It will be very hard to tell from the outside if any given company or project is aimed at getting stuck in that last 20%. As they could appear to be kicking some major butt in that first 80%. But then it will reach a plateau where they can no longer do anything substantial without breaking something else, or seem unable to fix every little quirk or issue. A lot of companies will probably get stuck here. With dozens of very impressive things 80% of the way, but seem unable to get over the last final 20% to really perfect it and polish it.
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I made a tutorial about building a collaborative and persistent text editor using Lexical, Yjs, and Postgres.
Full video: https://t.co/LJMM3GHBpI
Blog: https://t.co/W5nxARgxjf
Source code: https://t.co/LiX0GUDG5y
How to become expert at thing:
1 iteratively take on concrete projects and accomplish them depth wise, learning “on demand” (ie don’t learn bottom up breadth wise)
2 teach/summarize everything you learn in your own words
3 only compare yourself to younger you, never to others
@valigo Peter Thiel said it best. Any subject called "science" is an embellishment, like CS or Political Science. Chemistry and Physics are self-evident practices.
A better description would be Algorithmonomy or "the order of understanding step-by-step procedures"
I love this question. Before starting my own startup, I would choose 'envision and build' in an attempt to avoid the mess of deconstruction. Now, I choose 'deconstruct and evolve' since it implies starting from something that already works.
Which creative type are you? I'm the alchemist - "You’re drawn to deep inquiry, stripping away illusion and revealing hidden truths." #mycreativetype
https://t.co/BXfxXQ1YtI