21–30 of 66 posts
Literally nobody thought this.
Usefulness depends on intended job to be done. A hair dryer isn't very useful at drying clothes. If someone spent a lifetime mastering woodworking with hand tools, and then was shown a couple very early rudimentary power tools (lacking safety features, crude features, etc) they would rightly conclude they weren't useful. The artisan can do better work faster with less risk of dismemberment without them. Prior to LLMs…
More useful for drying clothes than a blender would be though. Or an LLM for that matter. No real point here I just thought it was an interesting example you used because it's still something that does work if you're lacking better options
> Another example is getting take out vs cooking the meal yourself
Interesting analogy
Buying ingredients and Cooking for yourself is almost guaranteed to be cheaper and healthier for your body in the long run. It's pretty difficult to eat out cheaply and very difficult to eat out healthily.
I dunno, might turn out to be some parallels there. I suspect that much like eating too much takeout, LLM usage makes us less fit.
Yes. Like working in a team. It can be hard to work on a team and to have to understand what your colleagues did, and how to fix or extend it. What if working in teams is a fantasy?
AI is not going to go away and it's not going to stop improving. That would go against the entire history of computing.
We have levels of improvements in the R&D pipeline in every area: hardware, software, model architecture and training. The models will get larger, architecture more sophisticated, computation dramatically more efficient. New materials, paradigms, more efficient nano-devices, scaling up manufacturing for better devices that are already out of the lab, etc. are in progress pointed towards multiple orders of magnitude efficiency gains. And by the way, that is not at all unusual -- we have been making large and small innovations in computing efficiency for decades.
There are still some things lacking in AI -- it still is jagged intelligence. Give it a few years, people will be nostalgic about the time when humans could still point to some victories here and there. That doesn't require any major breakthroughs -- just continuing to increase the size of the models and improving the training.
Practically speaking, is there really a difference between prompting an LLM to create a CMS for a website and installing something like WordPress or Ghost? The setup requires no understanding of the underlying software, but you'll have to build up a mental model from scratch if you want to extend or modify it later.
Of course, it's not going to please anyone that likes the process of programming or knows enough to tell that the output is mediocre at best. But it's very useful for people that don't know how something works and want a 'workable' solution that functionally does what they need. It's basically the next iteration of the WYSIWYG web design tool, the CMS, the site builder, etc. A professional software engineer would be horrified by the code they output, but a non-technical manager type wouldn't notice or care.
Usefulness depends on intended job to be done. A hair dryer isn't very useful at drying clothes. If someone spent a lifetime mastering woodworking with hand tools, and then was shown a couple very early rudimentary power tools (lacking safety features, crude features, etc) they would rightly conclude they weren't useful. The artisan can do better work faster with less risk of dismemberment without them. Prior to LLMs…
The author of the article mainly talks about agentic programming, code generation, and reasoning. Ans very rightly identifies a big problem with agentic programming, in my experience. If developers can't maintain the software without AI, it's doubtful they can steer AI to maintain it either. Maybe this is not true and we can tell AI something like 'reduce the number of lines of code' until the essential software is e…
Doesn't seem too crazy. This is the life of every customer (and most managers) of a software dev. To fathom it, think of yourself as the unicorn customer that can define projects, specs, and methods with competence, and even make architectural calls, with the AI being the dev.
To me, this is the incredibly clear eventuality of our profession, within the next 5-10 years. You writing code will be the same as most devs who try to hand write ASM: you will very very rarely be able to beat the compiler at its own game. And, just as now, there's a very small chance you'll go read what compiler produces.
It's obviously useful - we have to figure out best practices.
It is most certainly not obviously useful. To anyone who actually cares about quality and not just turning out slop, you have to spend so much time reviewing the code the machine generated that you wind up not saving any time. There are lots of people who don't give a damn about quality, but those were terrible developers before LLMs and remain so now.
Take a complex, sophisticated codebase you wrote, that only you have deep understanding of, and throw Codex or Claude at it and ask it questions. How does the Foo work? What happens if the user tries to Frozzle a Bar? Give me a guided tour of how the Baz feature was implemented, top to bottom of the stack.
Personally, I was amazed at how accurate the results were, on a codebase where I knew the answers already. Try to pretend that's not a useful tool to use when you have to fix bugs or add features to a large codebase you aren't already familiar with.
If you don't have a useful mental model of the architecture that's a communication / review / documentation problem, not a problem with the entire idea of codegen.
Personally, I've found that understanding what the AI generates well enough to make substantial changes to it hasn't been too difficult, though it'll obviously depend on how complex the project you're working on is. Effectively speaking, it's like using any sort of off-the-shelf solution and then modifying that solution; you don't need to understand it to use it for your project, but you'll need to do so to make sign…
There is some difference; when you install WordPress or Ghost, your problems are shared. There's a community that goes along with the software and a "shared understanding in the world" of how it works and where the rough edges and limitations are. A lot of the time, LLMs will actually be better at modifying WordPress or Ghost to do what you want than they will be at fixing the CMS you built last Tuesday.
When you custom build, you can get an exact fit for your needs but your misery is yours alone.
Usefulness depends on intended job to be done. A hair dryer isn't very useful at drying clothes. If someone spent a lifetime mastering woodworking with hand tools, and then was shown a couple very early rudimentary power tools (lacking safety features, crude features, etc) they would rightly conclude they weren't useful. The artisan can do better work faster with less risk of dismemberment without them. Prior to LLMs…
> A hair dryer isn't very useful at drying clothes. More useful for drying clothes than a blender would be though. Or an LLM for that matter. No real point here I just thought it was an interesting example you used because it's still something that does work if you're lacking better options > Another example is getting take out vs cooking the meal yourself Interesting analogy Buying ingredients and Cooking for yourse…
So far it seems that we can't all be good/capable at everything.
Maybe you write all your own code and cook your own meals, but do you do your own taxes? would you defend yourself in court? would you fix your car yourself?
Some people may answer yes to all the above, but most people outsource most things. Many people can't afford to outsource to other people, but can afford to outsource to LLMs.
Again it comes down to the job to be done:
- does the person want to develop or practice a skill, or do they just want it done? What is their budget, and how important is it to them to minimize certain risks?