"I have a hard time imagining a future where knowing how to solve problems with computers and how to control the complexity of those solutions is less valuable than it is today, so I think it will continue to be a viable career even with the advent of AI tools."
Exactly! So the nature of what we do might change; but, solving problems with computers will always be the thing. And the most capable people of doing that will be those we call programmers - what does it mean exactly, the nature of exact activities they perform will continue to change (as it arguably always have been), but there always be somebody who is significantly more adept at solving problems and create solutions using software - that somebody we will continue to call a computer programmer/software developer.
That's a valid point - I largely agree with you, I can make it generate mostly corrected & acceptable code with a solid level of quality, given smaller & scoped features and detailed prompts. I'm just not at all convinced that it's a net productivity boost - you must take your time for these detailed prompts and then verify the output, which is less of the case when you write the thing from scratch.
It certainly speed up some things, slows down others; for learning - a great resource! For generating code I'm on the fence, still experimenting; for now, I write some code manually, some with Claude, working on the hybrid setup. My intuition tells my that a flexible use of this tool will prove to be the most optimal - writing some code manually, some with LLms, depending on both the task and the programmer knowledge, experience and skills.
I think that the bottom line is that the bottlenecks are - the specific model you use & your skills, experience and reasoning capacity (intelligence); and you control only the latter, so focus on that!
"Perhaps there are frontiers of digital addiction we have yet to reach. Maybe one day we’ll all have Neuralinks that beam Instagram Reels directly into our primary visual cortex, and then reading will really be toast."
Even then, smart people will care about dissecting ideas, explore new concepts and broaden their understanding - and for most of it, Text Is King.
Hunger and drive can definitely lead to unexpected initially results; they cannot replace relevant experience, but if somebody has at least done something similar, it's often worth making a bet on them!
There also is an interesting paradox in experience and motivation; often the most experienced and best people on paper are unfortunately the least motivated, least hungry - burn out and boredom do their part.
What makes you think that they will just keep improving? It's not obvious at all, we might soon hit a ceiling, if we haven not already - time will tell.
There are lots of technologies that have been 99% done for decades; it might be the same here.
I don't know whether I would go that extreme, but I also often find myself faster writing code manually; for some tasks though and contextually, AI-assisted coding is pretty useful, but you still must be in the driving seat, at all times.
Are guardrails, CI/CD, to make code at least compile-able and require minimal quality standards also possible to change via PR or managed somewhere else? With this possibility, it might went into oblivion indeed!
Ahhh, the problem with incentives; maybe it's more the case that, had the Linux Foundation behaved in a more aligned with the pure open-source ethos way, Linux would not be so widely used and working so well? They problem of incentives in open-source is real - who, why and how should support your development? Especially for things like OSes, which require constant work.
I don't know whether we have figured out the best models here just yet; the results are mixed
Exactly this; for some tasks, it can speed up you dramatically, 5 - 10 x; with others, it actually makes you slower.
And yes, very often writing a prompt + verifying results and possible modifying them and/or following-up takes longer than just writing code from scratch, manually ;)
"The counter-scenario: as AI handles the routine 80%, humans focus on the hardest 20%. Architecture, tricky integrations, creative design, edge cases: the problems machines alone can’t solve. Rather than making deep knowledge obsolete, AI’s ubiquity makes human expertise more important than ever. This is the “high-leverage engineer” who uses AI as a force multiplier but must deeply understand the system to wield it effectively"
I would argue that:
- you cannot develop these skills without doing lots of development with minimal to no AI assistance
- this skills will atrophy, once you use AI too much and too often
I personally err on the side of using AI/LLMS rather too little than too much, to retain and further develop my core skills - time will tell which cohort made the right decision :)
No less impressive than the SQLite project itself; especially 100% branch coverage! That's really hard to pull off and especially to maintain as the development continues.
Good points; I wonder have they measure whether charities that get this money are effective though - it is a really hard problem; not only to help for the help sake, but to help effectively, achieving the intended end results
I also like and use lazy loading a lot :) But I guess the general question worth asking is: who drives the need for new features and functionalities in the browsers?
Yes! One could argue that we might end up with programmers (experts) going through a training of creating software manually first, before becoming operators of AI, and then also spending regularly some of their working time (10 - 20%?) on keeping these skills sharp - by working on purely education projects, in the old school way; but it begs the question:
Does it then really speeds us up and generally makes things better?