341–350 of 424 posts
I think the post, while extensive, missed one important issue. The fact then when we read others' code, we don't remember/integrate it into our thinking as well as we do when we're the authors. So mentoring "AI Juniors" provides less growth then doing the job, esp. if it is mostly corrective actions.
I’m personally going to keep myself sharp, only using LLMs very very sparingly.
For now, my mind is still made up. I leave the door open to be shown any serious piece of software that is built primarily through agentic workflows. Having tried to use these tools over the past month to build a critical piece of infrastructure for my company, I agree with OP. I spent so much time wrangling back unnecessary garbage that the LLM found was important, that I wondered if just writing it in one shot woul…
> Son, your system is not durable, your tests are misleading, and you can't reason about what's happening You just described the majority of pre-LLM enterprise software.
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In my experience this approach is kicking the can down the road. Tech debt isn't paid down, it's being added to, and at some point in the future it will need to be collected. When the agent can't kick the can any more who is going to be held responsible? If it is going to be me then I'd prefer to have spent the hours understanding the code.
> who is going to be held responsible? This is actually a pretty huge question about AI in general When AI is running autonomously, where is the accountability when it goes off the rails? I'm against AI for a number of reasons, but this is one of the biggest. A computer cannot be held accountable therefore a computer must never make executive decisions
It's a fine post, but two canards in here: First, skilled engineers using LLMs to code also think and discuss and stare off into space before the source code starts getting laid down. In fact: I do a lot, lot more thinking and balancing different designs and getting a macro sense of where I'm going, because that's usually what it takes to get an LLM agent to build something decent. But now that pondering and planning…
>First, skilled engineers using LLMs to code also think and discuss and stare off into space before the source code starts getting laid down Yes, and the thinking time is a significant part of overall software delivery, which is why accelerating the coding part doesn't dramatically change overall productivity or labor requirements.
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I see a lot of comments like this and it reflects strongly negatively on the engineers who write it imho. As in I've been a staff level engineer at both Meta and Google and a lead at various startups in my time. I post open source projects here on HN from time to time that are appreciated. I know my shit. If someone tells me that LLMs aren't useful i think to myself "wow this person is so unable to learn new tools th…
this comment states that you have posted a plurality of projects, but there is only one which received two votes (hardly well-received); the unawareness of your own posts (or lack thereof) implies this may not have been written by a human.
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The difficulty is we skeptics have read claims like yours tens of times, and our response is always, "please share a repo built this way and an example of your prompts," and I at least have never seen anyone do so. I'd love for what you say to be possible. Comments like yours often cause me to take another crack at agentic workflows. I'm disappointed every time.
Most AI evangelist commenters here end up with the same arguments every time. You are just not a true scotsman. And when they link something is one of those repos full of sloop and no code. I am becoming paranoid and wonder how many people here can even code.
I want them to be right and me wrong! Please someone show I am wrong.
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> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding. A software engineer's primary job isn't producing code, but producing a functional software system. Most…
I can imagine an industry where we describe business rules to apply to data in natural language, and the AI simply provides an executable without source at all. The role of the programmer would then be to test if the rules are being applied correctly. If not, there are no bugs to fix, you simply clarify the business rules and ask for a new program. I like to imagine what it must be like for a non technical business o…
The programming building blocks popular today (lines of code in sub-routines and modules) do not support such a jump?
I agree with this. My bosses boss thinks that AI is going to end up doing 95% of our work for us. From my experience (so far) AI coding follows the 80/20 rule, it can get you 80% of what you want for 20% of the time/effort. And the ratio might be more like, it'll get you 80% of what you want IMMEDIATELY, but it can't get you the last 20%, it needs a human to get it over the finish line. It's super impressive in my op…
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> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding. A software engineer's primary job isn't producing code, but producing a functional software system. Most…
> And you will never know code as well as a reader and you would have as the author for anything larger than a very small project. This feels very true - but also consider how much code exists for which many of the current maintainers were not involved in the original writing. There are many anecdotal rules out there about how much time is spent reading code vs writing. If you consider the industry as a whole, it see…
Very unskilled programmers can use generative AI to create complex code that is hard to understand.
Unskilled programmers on their own write simpler code with more obvious easy to solve mistakes.
On the other hand, all companies I have worked for tried to avoid have unmaintained code (with different levels of success). AI tech debt seems to be added on purpose pushed by upper management.
There is a technical and cultural difference.