Programming with ChatGPT

The weakest use of AI is definitely treating it like a lossy database of everything it's learned.

This is the best use case for voice mode I've found. I also use voice mode to take notes/brainstorm, then have it put them into a CSV or whatever format. The prompt I use has something like "ask any questions if things aren't clear" and the rugby ducky aspect emerges.

do you collate relevant calling functions to somehow give chatgpt full context?

maybe i am missing something.

I have used Claude3.5 for short FE/BE segments and its amazing. I am gonna try cursor next...

In my experience, LLMs are useful primarily as rubber ducks on complex problems and rarely useful as code generation for such.

Instead, I spend most of my time between the interesting work doing rote work which is preventing me from getting to the essential complexity, which is where LLM code gen does better. How do I generate a heat map in Python with a different color scheme? How do I parse some logs to understand our locking behavior? What flags do I pass to tshark to get my desired output?

So, I spend less time coding the above and more time coding how we should redo our data layout for more reuse.

Exactly how I feel about it. This is why I don't find "coding with LLMs" as fun as many others seem to find it. Code reviews are draining because you start with a bunch of unknown unknowns or unproven pitfalls this code might have fallen into. And then you eliminate them on by one as you run the algorithm in your mind. It is a lot more draining than coding is because an experienced coder can take mindful steps and build something solid on a base that he trusts won't collapse. Code Review doesn't get any less draining as you get more experienced, unless you start outsourcing responsibility.

Even if you rely on these tools heavily, you can't help but learn from them because you must still examine the their code, even if briefly. It will be a different type of learning than what you'd learn from, say, cracking open a language manual (yes, we used to do this back in the pre-Web days), but you will learn nonetheless.

I suggest you try all the tools available to you and the decide when it's appropriate for you to use each.

You can never learn it all.

You remember a fraction of things you merely touch. More if you narrow your focus.

If you have the passion for programming it will manifest itself by itself over time and with training.

Use all the tools available for learning and doing. Question it just as much. Explore the mind and the different axis of the field. Read the books but think for yourself.

Programming without money involved is creative and fun. Make sure you remember this. It's not a science or backed by natural laws. Don't worry about being wrong, we all are without guidance, but seek the truth within yourself for what you believe is the best path for becoming a good programmer.

None of us can see the future so it's all just bloviating, but let me give you my two cents as someone who's been programming since I was a kid and went the long way around starting with assembler: it probably mostly depends on what kind of programmer you want to be.

If you're in CS for the career and you have other things you want to do that have nothing to do with technology after the 9-5, then it's probably not going to be a problem. There's a lot more to software engineering than the code and getting good at those areas - like solving non technical coworkers problems with software - is just as important as writing the code. Most code is also not high-risk and doesn't need to be perfect, just maintainable. Learning the skill of translating requirements to LLM conversations now may well pay dividends in the coming decades because it's pretty obvious that LLMs are here to stay.

If you really like programming, want to be an architect later in your career, or want to be the technical cofounder in a successful tech heavy startup or something, then you'll want to minimize your use of those tools to rubber ducking and minor questions. There's a lot of value in developing "grit", that may very well be hampered by use of LLMs. You need to absorb a lot of foundational knowledge so that you can make intuitive decisions about what the LLM is writing. You might be able to use an LLM as the primary guide to developing that knowledge, but it's risky.

To be honest, when I was younger I figured all the people who started learning in college and didn't know how the low level basics of CPUs or memory worked would be at a disadvantage, but that has proven to be dead wrong. The majority of people did just fine using Python or Javascript without any of that knowledge or experience and I figure it will play out the same with LLMs.

LLMs can accelerate your learning. I've been programming for 25+ years and the rate at which I'm learning new skills and tools has gone up a very material amount now that I can bounce things through ChatGPT and Claude.

Should you worry that if you don't struggle against a weird compiler message for 3 hours (which ChatGPT would have told you how to fix in 30 seconds) you won't be gaining essential crank-on-a-frustrating-problem-for-three-hours experience?

I'm personally not convinced that the frustration is worth it. I'd rather have spent those three hours learning a bunch of other stuff.

But maybe the only reason I'm a competent programmer today is that I worked through that pain earlier in my career?

Since you're clearly thoughtful and conscientious, I suggest trying both. Some days, go all-in on LLMs. Other days limit yourself and work through challenges without them. Experiment like that for a month or so to see which learning style appears to be working best for you.

as long as you know how to ask the right questions, formulate the next steps of what you want and what context the AI might need, you are good to go

(This is the kind of test I'd expect it to fail at because of its knowledge cut-off - vulnerability scans are mostly looking for things like out-of-date packages, so any tool with a knowledge cut-off a few months in the past is very likely to include package versions that would no longer pass a scanner)

This is also doable with one line of bash via imagemagick in any terminal that supports the Kitty or iTerm2 graphics protocols, which is most mainstream ones (iTerm2 itself, Kitty, Konsole, etc.)

I don't say this to bash your techniques but to point out that there are other methods which permit quick prototyping and easy iteration, and that LLMs are, at least in my experience, not a paradigm-shifting improvement in this vein.

I do agree. But I think people is focusing on speed as if it was the only important thing to measure. It may be for some people-in-suites, but what about professionals?

A friend of mine who had minor knowledge about internet (used to do wbesites in HTML with iframes back with Dreamweaver) was able to deploy a PHP app using GPT. Great! It saved him a lot of time. Now I asked him if he understood or was able to do a change and he told me that he mostly did copy and paste and asked GPT to change things for him.

Not saying GPT is going to dissappear and no one is able to maintain this, because that sounds silly, but even with GPT at your side, it's improtant to understand what's happening in case you need to do a change.

Two notes, before anyone jump to this: I am not talking about simonw particular case, he seems a developer who knows his way around Python and could do all of this by himself, it's just saving his time.

Secondly, probably in the future a LLM will be much better at coding and will be able to fix or improve your code even if you don't understand it and will be able to do an startup from zero, scale it, deploy servers, etc. Who knows?

I just finished my initial prototype for a Raspberry Pi Zero W based audio recorder. These are all the things it does: - It has a single button connected to GPIO that starts/stops recording - An LED indicator connected to GPIO - Queries the available recording devices, choosing the external USB audio interface if available and setting the appropriate bit-depth - Launches ALSA's `arecord` in a subprocess to handle the actual recording (<5% CPU on a Pi Zero W when recording 48k @ 32bit) - The script is setup as a system service - Nginx is running to serve up the WAV files using a web UI - There is a simple service to start/stop recording and get status via an API the web UI can use

It is all in Python, which I don't really write, but is the default for these kind of Pi projects. 100% of the code, service definitions, Nginx config, README.md, etc were written by LLMs, mostly Claude.