511–520 of 575 posts
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How do you know things are grounded?
My tests work, multiple agents confirm the code base. Code reviews pass. I do very little actual reading of code anymore. Now I am more of a results merchant.
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> The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write . That is certainly not true. LLMs can't write code worth a damn still, and you have to babysit them to make sure they aren't doing stupid stuff. A human is still by far the best choice for doing programming, and when trend-chasing companies realize the damage they've done to their businesses they will stop pushi…
"LLMs can't write code worth a damn" I think it depends on the - ahem - context. In my experience they usually can, sometimes they can't. But generally their sense of software architecture remains abysmal, so even if their writing is ok, you need to have architectural authority the very least to guide them.
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> That sounds great until you realize that "hey Claude, read this Jira ticket and implement the feature in this code base" isn't really worth $200K/year. Nor even €50k/year. Two things are true: 1) The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to *write*. 2) My actual job also included non-coding work: Does this attempted solution even work at all? Is this solving the…
Before, if I would start working / thinking on a problem and discover it was harder than anticipated, that often was a signal that the feature may not be worth it. Because implementing it doesn't fit the current model of how things work or similar. Then it was back to the drawing table and find a better way of solving the underlying issue. But an LLM just happily chugs along and does it, no one feels the friction, wh…
The LLM will look through the codebase, think, and tell you what it would do and if there's any design decisions you would have to make, as well as other things you probably need to be aware of. Then you go through the LLM's output and address all those decisions point by point, asking the LLM more questions if something isn't clear or requires more investigation or you're unsure what to do. And end with "address what I wrote, and share any other thoughts or questions or things to clarify you might still have, don't implement yet"
And you do this back and forth until all the design decisions have been addressed and you feel confident of what the code and architecture will look like, and only then say "ok, implement"
That way you get a lot of the benefits of writing it by hand (being forced to think through what the best design would be and how it would integrate with existing code, and increasing understanding of how existing code works) but it's still much faster. The tool I personally use is Cursor in auto mode.
P.S. actually before even that you first ask the LLM "what is the current state of X in the codebase" and then you ask follow-up questions until you have a good understanding of all the details that are relevant to you. And then you can start having the design/implementation conversation in the same chat context, since having the above information in context is useful.
P.P.S. and you can also ask a bridge question like "can XYZ be cleanly added with how things are currently structured?" or "what would it take to add XYZ to the project?"
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Ai can work through all of those questions in number 2 and design around it. I was just doing that yesterday designing a game demo lol
I have also tried developing games with them. I find them very weak at these things. Often they can't figure out how to test the thing, so "does this attempted solution even work at all?" is its guess from reading the source code, and sometimes I've even caught them writing "tests" which are a regex on the source code, not functionality. They know almost nothing about "is this solving the right problem?", they're tra…
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Not everyone has this, but I always felt that a significant percentage of the value I bring is in immediately recognizing what you shouldn’t do . I have a hard enough time explaining why “yet another bespoke application on some unmanaged azure resources” is a bad idea when they have more reasonable alternatives at hand. Now these goofs can (very nearly) press a button and do it anyway, with no comprehension of the co…
This lands. Llms are bad precisely at following what not to do. They work best off of positive constraints. I have a design principles + tech preferences doc I force llms "lint" their approach against. It's not perfect but it helps. I call it a bias field, pushes them toward hopefully the happy and harmonious (with the rest of the system) paths. Obviously this is only partial and imperfect enforcement, but if it's ap…
Where does it land?
And this is because if companies are pushing you to "use AI and go faster", then there's no time to learn from GenAI tools, and companies don't care if you do.
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There is a limit, after a few 10,000's of LOC ai projects start disintegrating and becoming unmanageable.
Really? I just did the LoC measure that isn't "official" but actually closer to how AI would read it: I.e. simply actual lines. Never mind "context window". Just in the actual source folder there are 1.9 million lines. That's not tokens. Lines. Not even LoC as per some official canonicalized definition. Various languages. ~15 year old code base. We work with Claude just fine. For variable measures of "fine" depending…
At the same size I only heard of AI generated projects reimplementing compilers and browser engines which come with amazing tests written by hand, but even these projects generally don't become reliable enough to use.
You'll still need to learn stuff and learn your craft but if you don't become an expert at programming switch statements so much the better or the next garbage web framework.
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Really? I just did the LoC measure that isn't "official" but actually closer to how AI would read it: I.e. simply actual lines. Never mind "context window". Just in the actual source folder there are 1.9 million lines. That's not tokens. Lines. Not even LoC as per some official canonicalized definition. Various languages. ~15 year old code base. We work with Claude just fine. For variable measures of "fine" depending…
Yes, a 15 year old code base written by hand obviously, not by a coding agent from the start. At the same size I only heard of AI generated projects reimplementing compilers and browser engines which come with amazing tests written by hand, but even these projects generally don't become reliable enough to use.
That 1.9m code base, within the same time frame as the 160k was created? 10k I can comfortably throw at a local agent on my system (see recent posts in my history). Whether I can keep it that small is another question I suppose lol!
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Maybe I’m naive, or misguided, or something else, but I decided almost 2 years ago that I’m going to bank on not using LLMs to do my job. I still write code by hand everyday. I don’t use AI to write code or lookup docs, or anything else. I just figure that if we really, truly, indisputably hit the point of AI assisted coding being mandatory, I can learn it and get up to speed in a few weeks (or at worst a couple mont…
I am in exactly the same position; I don't feel left behind per se but I was just fired for my stance. Management is entirely deluded across the industry, and in my case it was even acknowledged and agreed that my output and the quality of work was not in question, simply the fact that I didn't use LLMs to produce it. I reckon everything needs another 6-18 months to ripen, then those of us who haven't bet the farm on…