> AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities.
Integration, testing, deployment and production validation activities are also, depending on you setup, available for AI to work on. It depends on the type of stack that you have and how your hosting is set up but with an AI-friendly set up there's a lot of time to be saved here too.
This is largely true to only the extent that you you have a machine-verifiable measure of quality. If not, you’re putting more pressure on the humans who can actually reason and understand business goals. This cascades: tests are what make LLM code generation work but if you have an LLM write the tests you are then pushing further down the road the point where you learn that the first LLM was going in the wrong direction.
well, that is a problem with automation in general i think; if you go too far instead of a simple process a complicated processes get encoded in the automation and if it hits an edge case or breaks for whatever reason the babysitting kills productivity (this is just my experience anyways)
also, as far as quality goes, once something is automated that potential breakage puts a cap on how much improvement/quality you can achieve later... i'd guess llms are a bit more malleable there, but idk, i still see this issue even with skills and such...
This is the weirdest combination of words I have seen this week. If I own a trampoline, why would my insurance company care? And if they care, why doesn't the market fix this, parents with trampolines would certainly choose other companies.
They care because trampolines have a very high rate of injury. If you throw a birthday party at your house and one of your neighbors kids breaks their arm or worse, neighbor might decide to sue you. That then puts your insurance company on the hook. Even if your policy forbids trampolines you can often plausibly argue you weren't aware, and they end up having to pay out. Insurance companies refuse to cover things all the time and trampolines are apparently a big problem for them (just talked to our insurance broker a few weeks ago about this as my family is pushing to get one and I'm having to push back.)
> ...badly architected, often redundant, feature-bloated code which always looks good, even upon earnest review, but actually sucks and becomes extremely difficult to maintain in ways which are not obvious in code review.
I can only judge from my own experience but with or without LLMs, these are the codebases that I have worked with during most of my career. To me, much of the question is whether LLMs produce worse code than the me and my colleagues have done in the past and I don't think that's the case. It is however very common that people hold LLMs to a higher standard than human colleagues and then it's not a useful comparison.
LLMs can produce bad code in ways no human ever would. I remember working on headscratcher problem of walking a tree while gradually building up state, and since LLMs are supposed to be good at algorithms, I asked Claude.
It came up with a correct LC-hard tier solution that involved dynamic programming, and was essentially an unreadable dense mess that was impossible to reason through as a human.
It worked, but it was so bad, that I sat down and realized after a bit that with maintaining a small cache, and being very particular about how the nodes are traversed, I reduced the solution to like a 10 line modified DFS, that I could understand.
I’ e written bad code and I’ve reviewed bad code by other humans. In both of those cases the code gets re-written. In the first the reviewer tells me the code is hard to understand, can it please refactor. In the 2nd I tell the person I’m reviewing the same thing, maybe with some suggestions.
I do the same with the LLM. I tell it that solution is convoluted and hard to understand, if I have a concrete suggestion I suggest one, otherwise I ask it for ideas. We get there just like I do with humans
In my experience, even if the code is not worse, there is so much more of it that people quickly stop understanding what is going on. Now you could argue „the LLM knows“, but that comes at an insane token cost, even if it works well.
This is in the interest of big AI companies: if they quasi-monopolize the skills entire sectors of the economy need in order to function, that will be great (for them).
Everyone keeps comparing this to compilers, but I don’t need a multiple-hundred dollar subscription to use LLVM. And people didn’t stop understanding how computers work either, just because they used C. And yeah, maybe local LLMs will become the norm, and I hope so. But market forces (hardware prices) certainly are working against that right now.
thanks appreciate it! you can boost sources and categories (youtube and substack have a boost by default since they're published less frequently than reddit posts), create priority views, filters etc.
planning to set up 'topics' which can be a feed you can subscribe to that combine different source types, that are tagged and associated to the topic based on previous content.
I agree, I have said it before, ChatGPT is like Photoshop at this point, or Google. Even if you are using Bing you are googling it. Even if you are using MS Paint to edit an image it was photoshopped.
> Identifying that habit as part of who you are is key to it being sticky.
This has always been key to me. I've succeeded to identify myself as a runner, as someone who speaks French, as someone who reads books. But my identification as someone who has meaningful programming side-project, who has a garden and so on is to weak to succeed.
