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Engineering management after the cost of code collapsed

karimjedda.com

111–120 of 217 posts

Re: Engineering management after the cost of code collapsed

#111
post #107

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

I think this is outdated. If you follow spec-driven development, get the model to do all the planning work upfront, review and iterate the plan, write clear markdown file documentation on the abstractions and patterns you want to follow, then you have every opportunity to tell the model how you want it to write the code. If you use Opus or Fable 5 it will then write the code better and faster than you will.

Depends on how long that planning/review cycle takes.

Re: Engineering management after the cost of code collapsed

#112

Engineers do not need management. Investors do. As I understand it, the purpose of management is to match financial resources with material+human resources to perform feasible tasks. There is nothing here I see that can't be done by an experienced token generator. If anything, automating management seems easier than automating engineering. As for leadership, it can be done by the investors.

You can automate work but never owning the consequences. AI tasks emerge from human contexts, they perform work in the context and finally the outcomes collect in the context - gains, losses, risks, costs. So AI is great but it needs our skin for the start, middle and end of a task.

Re: Engineering management after the cost of code collapsed

#113
post #107

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

I think this is outdated. If you follow spec-driven development, get the model to do all the planning work upfront, review and iterate the plan, write clear markdown file documentation on the abstractions and patterns you want to follow, then you have every opportunity to tell the model how you want it to write the code. If you use Opus or Fable 5 it will then write the code better and faster than you will.

IMO "get the model to do all the planning work upfront, review and iterate the plan" is backwards. It works better if you do an initial plan yourself and then have the AI review it (and iterate as necessary). Makes you think about the design for a bit so you can have a semblance of a mental model.

Re: Engineering management after the cost of code collapsed

#114

Pangram reports this post was 100% AI generated.

Pangram's marketing always reminds me of Anchorman's Sex Panther cologne: "They've done studies, you know. Sixty percent of the time, it works every time." Pangram's "100% AI generated" claims are right 65% of the time. https://link.springer.com/article/10.1007/s40979-026-00226-w

Did you read the paper you are linking to? It records a 0% false positive rate for evaluation of human-authored controls, and fewer than 5% of the hybrid and humanized papers had their AI levels overestimated by pangram. It is not completely clear from the data, but it seems that depending on whether the n=2 overestimates were “100% ai generated” assessments, the study you link says pangram’s 100% AI assessments were right anywhere from 95-100%

Re: Engineering management after the cost of code collapsed

#115

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

I hate saying this but unironically opus 5/gpt 5.6 are writing maintainable code on the order of 500-1000 lines

Re: Engineering management after the cost of code collapsed

#116
post #107

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

I think this is outdated. If you follow spec-driven development, get the model to do all the planning work upfront, review and iterate the plan, write clear markdown file documentation on the abstractions and patterns you want to follow, then you have every opportunity to tell the model how you want it to write the code. If you use Opus or Fable 5 it will then write the code better and faster than you will.

Sure. But try it on a project on non-trivial size and then tell us the cost of it. LLMS are billed per token.

Re: Engineering management after the cost of code collapsed

#117

Engineers do not need management. Investors do. As I understand it, the purpose of management is to match financial resources with material+human resources to perform feasible tasks. There is nothing here I see that can't be done by an experienced token generator. If anything, automating management seems easier than automating engineering. As for leadership, it can be done by the investors.

You can automate work but never owning the consequences. AI tasks emerge from human contexts, they perform work in the context and finally the outcomes collect in the context - gains, losses, risks, costs. So AI is great but it needs our skin for the start, middle and end of a task.

It's not as if management owns any consequences. At best they adapt, but so can AI. Management tasks are not like engineering tasks.

Re: Engineering management after the cost of code collapsed

#118

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

How do you handle the fact that an LLM can't seem to help itself from disgorging page after page of words no matter what it's asked to do? I've never seen an LLM say "this looks good as-is; I would not spend any more time on it; what's next?" it will always seem to suggest using another pattern or additional abstractions or other yak shaving. But to be fair human code reviews have the same problem. It's like reviewer…

What tools are you using? I've found that Codex will happily tell me there's nothing more to do after a few rounds of /review and fix (or sometimes, it picks up on low value points that are technically correct but not worth changing).

Re: Engineering management after the cost of code collapsed

#119

Earlier quoted context omitted.

Pangram's marketing always reminds me of Anchorman's Sex Panther cologne: "They've done studies, you know. Sixty percent of the time, it works every time." Pangram's "100% AI generated" claims are right 65% of the time. https://link.springer.com/article/10.1007/s40979-026-00226-w

Did you read the paper you are linking to? It records a 0% false positive rate for evaluation of human-authored controls, and fewer than 5% of the hybrid and humanized papers had their AI levels overestimated by pangram. It is not completely clear from the data, but it seems that depending on whether the n=2 overestimates were “100% ai generated” assessments, the study you link says pangram’s 100% AI assessments were…

[deleted]

Re: Engineering management after the cost of code collapsed

#120

I think most of this is correct, in spite of potentially being built on a bad assumption. The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that. Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally r…

> The other thing that the "LLMs write code camp" misunderstands is that writing was never the bottleneck. Understanding was. And understanding the code is still the bottleneck. But understanding is truly gained during the writing loop. The understanding you gain from pure reading or code review is marginal compared to the understanding you gain while writing. This was my stance a couple of years ago, but now I've gi…

> Quite a lot of changing the code is just useless busywork: re-wiring old functions, moving imports around, searching for locations that benefit from extracting a common piece of code.

If other people are dissatisfied with LLM output quality while it seems to work fine for you, you might want to consider that the quality of code you produce is closer to the quality of code the LLM produces than what those other people are producing.

What you posted there, for example, about most of changing code being busy work is a pretty big red flag for a codebase. One of those "large structure" things that you're supposed to be paying attention to is the architecture of the code. There's always the chance that some change you need to do goes against the grain of the solution you architected, and you need to make changes all across your codebase to fit it in, but in general the point of modularity and good architecture is that when you make a change you just have to make that one change, ideally just changing the logic of the one responsible function with only minor changes required anywhere else in the codebase. If you're consistently having to hunt throughout the code for related functions that you need to rewire that's a sign that your architecture does not fit with the direction your codebase is evolving, or alternatively that you don't have much of an architecture to begin with and your code is highly interconnected.

Actually one habit you mention at the end of that quote can worsen this issue: "searching for locations that benefit from extracting a common piece of code". Tautologically this is a good thing as you define it as only working on locations that will benefit, but given the frequent need for rewiring of functions I would hazard to guess that you've "deduplicated" code a bit overzealously. Just because two functions share some common code does not necessarily mean it is appropriate to pull that out into a function. Deduplicating is good if conceptually the code is a single thing that you would always want to keep in sync, as it means that when you need to make a change to it you don't have to hunt down all the places it's used. On the other hand, if you find yourself frequently needing to delve in to these functions to rework them because you need to make a change to how it's used by just one caller, your "deduplication" has added to your workload, and probably created some overcomplicated code in the function that is in reality handling multiple distinct needs.

I hope this doesn't come across as too condescending, and if I've just wasted your time explaining principles you already understand I apologize. I don't know you or the code you're working on so I can't exactly confidently judge your work solely on a few paragraphs. It's just that your mention of how your experience of coding has been different from what others have described, and specifically that, for you, writing has been the bottleneck rather than understanding, combined with the specific issues you describe facing, imply to me that you may not realize that the approach you are taking to producing code yourself may be significantly different from how other Software Engineers are producing code, and that may account for some of the differences you note in your personal experiences programming.

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