Ignoring lemmyhate, are programmers really using AI to be more efficient?
from bridgeenjoyer@sh.itjust.works to programming@programming.dev on 15 Aug 2025 16:02
https://sh.itjust.works/post/44161444
from bridgeenjoyer@sh.itjust.works to programming@programming.dev on 15 Aug 2025 16:02
https://sh.itjust.works/post/44161444
I’ve seen a few articles saying that instead of hating AI, the real quiet programmers young and old are loving it and have a renewed sense of purpose coding with llm helpers (this article was also hating on ed zitiron, which makes sense why it would).
Is this total bullshit? I have to admit, even though it makes me ill, I’ve used llms a few times to help me learn simple code syntax quickly (im and absolute noob who’s wanted my whole life to learn code but cant grasp it very well). But yes, a lot of time its wrong.
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From my experience it’s great at doing things that have been done 1000x before (which makes sense given the training data), but when it comes to building something novel it really struggles, especially if there’s 3rd party libraries involved that aren’t commonly used. So you end up spending a lot of time and money hand holding it through things that likely would have been quicker to do yourself.
the 1000x before bit has quite a few sideffects to it as well.
Its also trained on old stuff.
And because its old, you get some very strange side effects and less maintainability.
It’s decent at reviewing its own code, especially if you give it different lenses to look though.
“Analyze this code and look for security vulnerabilities.” “Analyze this code and look for ways to reduce complexity.”
And then… think about the response like it’s a random dude online reviewing your code. Lots of times it raises good issues but sometimes it tries too hard to find little shit that is at best a sidegrade.
The pycharm AI integration completes each line. That’s very useful when you are repeating a well known algorithm and not distracting when you are doing something unusual. So overall, for small things AI is a speed up. I haven’t tried asking chatgpt for bigger coffe chunks, I haven’t had the greatest experience with it up to now and ii don’t want to spend more time debugging than I am already.
Oh man, the Codeium auto complete in PyCharm has been just awful for me. Slow enough that it doesnt come up fast enough that I ever expect it (and rarely comes up when I pause to wait for it) then goes away instantly when I invariably continue typing when it comes up. Then won’t come back if I backspace, erase the word and start retyping it, etc. And competes with the old school pycharm auto complete sometimes which adds another layer of fun.
this
Yes. But I’m not paying for premium like some of my cowokres. I use it to avoid the grunt work, and to avoid things I know I’d have to google.
I used some coworkers account for a while and auto complete is amazing. I it guesses wrong you just keep tipping as usual. If its right, hit tab and saves you like 20 seconds.
On the other hand I have cokowkers that do not check the chatgpt output and the PRs make no sense. Example: instead of making a variable type any (which is forbidden in our codebase) they did
Let a : date|number|string|object|(…) = fetchData()
From my experience it’s really great at bootstrapping new projects for you. It’s good at getting you sample files and if you’re using cursor just building out a sample project.
It’s decent at being an alternative to google/SO for syntax or previously encountered errors. There’s a few things it hallucinates but generally it can save time as long as you don’t trust it blindly.
It struggles when you give it complex tasks or not-straightforward items. Or things that require a lot of domain knowledge. I once wanted to see what css classes were still in use across a handful of react components and it just shat the bed.
The people who champion AI as a human replacement will build a quick proof of concept with it and proclaim “oh shit this is awesome!” And not realize that that’s the easy part of software engineering.
I’ve had good luck having it write simple scripts that I could easily handle myself. For example, I needed a script to chop a directory full of log files up into archives, with some constraints. That sort of thing.
I haven’t tried it on anything more substantial.
This was using Copilot because I haven’t found a good coding model that will run locally on 16GB VRAM.
Programmers are promoted to architects who write high-level specs with a subordinate to do the leg work (AI). I think the hate is because not everyone is good at planning and some people are better at perfecting implementation details, and AI isn’t helpful there.
If by promoted you mean required to do architect’s but not qualified, nor paid for that
Some definitely are. But I think a lot aren’t. Hell, a lot of programmers still don’t even use an IDE.
I don’t know why it would make you ill.
The same reason I wont use autotune or melodyne. Its just gross to me.
I don’t feel like it’s the same - autotune can make me more in tune than I could ever achieve. Current LLMs definitely can’t write better code than me, they can just do it faster.
I use it sparingly and only to automate things I know how to do very well, so reviewing its work become easier.
Definitely depends on the sub-sector of the industry you’re in. There’s no shortage of stories of people who swear by it, or who are having it forced on them by management.
Me personally’ I’ve never wanted or been pressured to use it, and I work for a company with “AI” in the damn name. To be fair, though, this company was around doing general machine-learning stuff before the current LLM craze exploded. Also, I work with a small team that was only bought by this company a few years ago, and thus far has been allowed to remain practically independent. Also also, the business domain my team works in is finance and accounting, where there’s bot much practical application for ML, and where you REALLY can’t afford to fuck around and find out, with business data.
You can either spend your time generating prompts, tweaking them until you get what you want and then using more prompts to refining the code until you end up with something that does what you want…
or you can just fucking write it yourself. And there’s the bonus of understanding how it works.
AI is probably fine for generating boiler plate code or repetitive simple stuff, but personally I wouldn’t trust it any further than that.
There is a middle ground. I have one prompt I use. I might tweak it a little for different technologies, languages, etc. only so I can fit more standards, documentation and example code in the upload limit.
And I ask it questions rather than asking it to write code. I have it review my code, suggest other ways of doing something, have it explain best practices, ask it to evaluate the maintainability, conformance to corporate standards, etc.
Sometimes it takes me down a rabbit hole when I’m outside my experience (so does Google and stack overflow for what it’s worth), but if you’re executing a task you understand well on your own, it can help you do it faster and/or better.
I’ve been using phind as a technical-focused AI search engine, which is a great addition to my toolset.
I’m mindful of using it vs searching [ref docs etc], not only in the kind of search and answer I’m looking for but also energy consumption impact, but it’s definitely very useful. I’m a senior dev though, and know what to expect and I am able to assess plausibility, and phind provides sources I can inspect too.
As for code assistance, I find it plausible that it can be useful, even if from my personal experience I’m skeptical.
I watched an Microsoft talk from two devs, which was technically sound and plausible in that it was not just marketing but they talked about their experience, including limits of AI, and where they had to and to what degree they had to deal with hallucinations and cleanup. They talked about where they see usefulness in AI. They were both senior, and able to assess plausibility, and do corrections where necessary. What I remember; they used it to bounce ideas back and forth, to do an implementation draft they then go over and complete, etc.
Microsoft can do the investment of AI setup, code sharing to model, AI instructions/meta-description setup investment, etc.
My personal experience was in using copilot for Rust code, for Nushell plugins. I’m not very familiar with Rust, and it was very confusing, and with a lot of hallucinations.
The PR descriptions CodeRabbit did were verbose and not useful for smaller PRs I made. That has been a while ago.
At work we have a voluntary work group exploring AI. The whole generate your whole app kind of thing seems plausible for UI prototypes. But nothing more. And for that it’s probably expensive.
I’m not sure how much the whole thing does or can do for efficiency. Seems situational - in terms of environment, setup, capabilities, and kind of work and approach.
Phind has been decent for me when I’m looking for prior art and/or research papers on something I’m trying to develop/implement. It’s nice to be able to pose a question as I would to a colleague and get references back that I can read for myself.
Sadly, it still hallucinates some stuff, and when it doesn’t it tends to give me references that are tangentially related to my query but don’t actually cover it.
I’m okay with AI-powered autocomplete, or with AI-powered mock project generator. Anything beyond that seems like the management’s misguided attempt at
having more meetingsraising productivity.I’m not using AI, and I rarely use IDE, because ugh, code editor is not fullscreen, and I don’t need a separate panel to navigate project tree and edit makefiles, I can perfectly use the shell for that, and I don’t even need to wiggle the mouse like some graphics designer to debug my code.
I’ve found in-line completions/suggestions useful at times, but multi-line completions always irritating to the point that I disabled them completely. Much more often I want to read surrounding and following code, and not have it be pushed out of view, and rarely was it useful to me.
Of course, that may be largely the project and use case. (And quite limited experience with it.)
It’s good for what it’s good, and bad for what it’s bad.
If you only use it for what is good I would suppose it would be easy to be more productive. Sometimes is faster to ask an LLM than trying to surf through pages of SO “repeated question” to get an answer.
I use mostly for things like that, questions, translation between languages (for instance having some working code in one language that you want to quickly translate to other language), boiler plate of well known algorithms and functions.
For full programming development I’ve no luck to make it work. And trusting it to refactor all your code would be something hilarious.
I use it now and again but not integrated into an ide and not to write large bits of code.
My uses are like so
Rewrite this rant to shut the PO/PM up. Explain why this is a waste of time
Convert this excel row into a custom model.
Given these tables give me the sql to do xyz.
Sometimes for troubleshooting an environment issue
Do I need it , no. But if it saves me some time on bullshit tasks then thats more time for me
My brother in programming,
please don't use AI for data format conversion when deterministic energy efficient means are readily available.
It was just an example to illustrate the point. I use specific convertors for actual format conversions. Actual uses have been map it to a custom data model .
You are right though , right tool for the job and all that.
🙄
I’d never trust it to make the change but I recently asked for a Python script to do a change I needed and it did it perfectly first try (verified the diff).
Also I don’t know Python at all.
— a fellow old man
Sorry, I agree with other replier, but why would you use AI to convert from and to XML... when another more objective, reliable, and deterministic tool to convert exists for a long time. You know well how often LLM makes up stuff...
Clarified my point in the reply above .
I’m pretty sure every time you use AI for programming your brain atrophies a little, even if you’re just looking something up. There’s value in the struggle.
So they can definitely speed you up, but be careful how you use it. There’s no value in a programmer who can only blindly recite LLM output.
There’s a balance to be struck in there somewhere, and I’m still figuring it out.
I assume you were joking but some studies have come out recently that found this is exactly what happens and for more than just programming. (sorry it was a while ago so I dont have links)
Doesn’t sound like they’re joking to me.
There are similar studies on the effects of watching a Youtube video instead of reading a manual.
This is literally the exact same argument made against using books and developing writing.
Not a programmer, but I used it at my last job to get over humps where I was stuck on PowerShell scripts. AI can show you a path you didn’t know or hadn’t thought about. The developers seemed to be using it the same way. Great tool if you don’t completely lean on it and you know enough to judge the output.
Thats the key, use it to learn, not to do your thinking
I find it excels at one-off scripts. They are simple enough that every parameter and line of code fits in a small bit of memory. They are really bad at complex tasks, but they can help if you use it judiciously.
I used ChatGPT to write some fairly straight forward bash scripts last week and it was mostly awful. I ended up massaging it enough to do what I needed, but I would have been better off just writing it myself and maybe asking it a couple syntax questions (although the regex I needed was one of 8 things it stumbled over)
I’m generally much better at writing regex than ChatGPT. Though I will say, I needed the regex for ISO 3339 date format just yesterday for validation and copilot/Claude provided a more specific version than Google search. I still have to go back and double check the corporate standard as I suspect we only allow offsets from UTC and all implementations I looked at are too permissive.
I’ve had middling experience with bash. The scripts I wrote are generally petty basic. Set a few variables based on the current project and then execute some gcloud or Tekton commands. And I don’t write them often so it finds and fixes things I often forget like not being allowed to have spaces around
=.I think the more externalities that need to be considered to come up with a correct answer, the less reliable ChatGPT is because there are a lot of externalities it doesn’t really know to consider. Bash has a huge number of externalities that might affect the correct way of doing something.
I should experiment with more functional languages. “Pure functions” are really good at minimizing externalities. Worth investigating.
I used supermaven (copilot competitor) for awhile and it was sorta ok sometimes, but I turned it off when I realized I’d forgotten how to write a switch case. Autocomplete doesn’t know your intent, so it introduces a lot of noise that I prefer to do without.
I’ve been trying out Claude code for a couple months and I think I like it ok for some tasks. If you use it to do your typing rather than your thinking, then it’s pretty decent. Give it small tasks with detailed instructions and you generally get good results. The problem is that it’s most tempting to use when you don’t have the problem figured out and you’re hoping it will, but thats when it gives you overconvoluted garbage. About half the time this garbage is more useful than starting from scratch.
It’s good at sorting out boilerplate and following explicit patterns that you’ve already created. It’s not good at inventing and implementing those patterns in the first place.
My favorite use is actually just to help me name stuff. Give it a short description of what the thing does and get a list of decent names. Refine if they’re all missing something.
Also useful for finding things quickly in generated documentation, by attaching the documentation as context. And I use it when trying to remember some of the more obscure syntax stuff.
As for coding assistants, they can help quickly fill in boilerplate or maybe autocomplete a line or two. I don’t use it for generating whole functions or anything larger.
So I get some nice marginal benefits out of it. I definitely like it. It’s got a ways to go before it replaces the programming part of my job, though.
Reverse dictionary lookup, more or less.
Now, that is something LLMs should be actually good at, unlike practically any other thing they’re being sold as being good at.
In the grand scheme of things, I think AI code generators make people less efficient. Some studies have come out that indicate this. I’ve tried to use various AI tools, as I do like fields of AI/ML in general, but they would end up hampering my work in various ways.
I’m somewhat new to the field ~1.5 years, so my opinion doesn’t hold too much weight.
But in the embedded field I’ve found AI to not be as helpful as it seems to be for many others. The one BIG thing is has helped me with is I can give it a data sheet and it’ll spit out all the register fields that I need, or help me quickly find information that I’m too lazy to Ctrl-f, which saves a couple minutes.
It has not proven it’s worth when it comes to the firmware itself. I’ve tried to get it to instantiate some peripheral instances and they never ended up working, no matter how I phrased the prompt or what context i’ve given it.
I use it mainly to tweak things I can’t be bothered to dig into, like Jekyll or Wordpress templates. A few times I let it run and do a major refactor of some async back-end code. It botched the whole thing. Fortunately, easy to rewind everything from remote git repo.
Last week I started a brand new project, thought I’d have it write the boilerplate starter code. Described in detail what I was looking for. It sat there for ten minutes saying ‘Thinking’ and nothing happened. Killed it and created it myself. This was with Cursor using Claude. I’ve noticed it’s gotten worse lately, maybe because of the increased costs.
I don’t see how it could be more effecient to have AI generate something that you then have to review and make sure actually works over just writing the code yourself, unless you don’t know enough to code it yourself and just accept the AI generated code as-is without further review.
The junior developer can (hopefully) learn and improve.
LLMs are also improving though.
They’ll never be able to learn, though.
A LLM is merely a statistical model of its training material. Very well indexed but extremely lossy compression.
It will always be outdated. It can never become familiar with your codebase and coding practices. And it’ll always be extremely unreliable, because it’s just a text generator without any semblance of comprehension about what the texts it generates actually mean.
All it’ll ever be able to do is reproduce the standards as they were when its training model was captured.
If we are to compare it to a junior developer, it’d be someone who suffered a traumatic brain injury just after leaving college, which prevents them from ever learning anything new, makes them unaware that they can’t learn, and incapable of realising when they don’t know something, makes them unable to reason or comprehend what they are saying, and causes them to suffer from verbal diarrhoea and excessive sycophancy.
Now, such a tragically brain damaged individual might look like the ideal worker to the average CEO, but I definitely wouldn’t want them anywhere near my code.
if the only point of hiring junior devs were to skill them up so they’d be useful in the future, nobody would hire junior devs
LLMs aren’t the brain: they’re exactly what they are… a fancy auto complete…
type a function header, let if fill the body… as long as you’re descriptive enough and the function is simple enough to understand (as all well structured code should be) it usually gets it pretty right: it’s somewhat of a substitute for libraries, but not for your own structure
let it generate unit tests: doesn’t matter if it gets it wrong because the test will fail; it’ll write a pretty solid test suite using edge cases you may have forgotten
fill lines of data based on other data structures: it can transform text quicker than you can write regex and i’ve never had it fail at this
let it name functions based on a description… you can’t think of the words, but an LLM has a very wide vocabulary and - whilst not knowledge - does have a pretty good handle on synonyms and summary etc
there’s load of things LLMs are good for, but unless you’re just learning something new and you know your code will be garbage anyway, none of those things replace your brain: just repetitive crap you probably hate to start with because you could explain it to a non-programmer and they could carry out the tasks
I never said that, and a single review already will make a junior dev better off the bat
I agree, but then you say…
…which says the other thing. Implementing a function isn’t for a “fancy autocomplete”, it’s for a brain to do. Unless all you do is reinventing the wheel, then yeah, it can generate a decent wheel for you.
Fuck no. If it gets the test wrong, it won’t necessarily fail. It might very well pass even when it should fail, and that’s something you won’t know unless you review every single line it spits out. That’s one of the worst areas to use an LLM.
I’m not sure what you mean by that.
I agree with that, naming or even documenting is a good way to use an LLM. With supervision of course, but an imprecise name or documentation is not critical.
Not speaking for them, but I use LLMs for this. You have lines of repetitive code, and you realize you need to swap the order of things within each line. You could brute force it, or you could write a regex search/replace. Instead, you tell the LLM to do it and it saves a lot of time.
Swapping the order of things is just one example. It can change capitalization, insert values, or generate endless amounts of mock data.
Ah! That does seem useful indeed! Even just generating a bunch a dummy data.
I was tasked once with writing a front-end for an API that didn’t exist yet, but I had a model. I could have written a loop that generated “Person Man 1”, “Person Man 2”, etc. with all of the associated fields, but instead I gave the LLM my class definition and it spat out 50 people with unique names, phone numbers, emails, and everything. It made it easy to test the paging and especially the filtering. It also took like 30 seconds to ask for and receive.
I originally asked it to make punny names based on celebrities, and it said “I can’t do that.” ☹️
yup! absolutely this too - i provided a different example in my reply, but honestly this is exactly the thing i use it for most… type a couple of lines, it gets the idea of what you’re trying to copy, and then it’s just hitting accept until it’s done… it’s pretty close to 100% accurate, and even if it’s not… fixing it ain’t exactly hard!
pretty much every line of code we write in modern software isn’t unique… we use so many orders of magnitude more lines of other people’s code than our own, we’re really just plumbing pipes together
most functions we write that aren’t business logic specific to the problem domain of our software (and even sometimes then) has been written before… the novel part isn’t in the function body: the low level instructions… the novel part is how those instructions are structured… that may as well be pseudocode, and that pseudocode may as well take the form of function headers
write tests, tests fail, write code, tests slowly start to pass until you’re done… this is how we’ve always done TDD because it ensures the tests fail when they should. this is a good idea with or without LLMs because humans fuck up unit tests all the time
for example, you have an external API of some kind with an enum expressed via JSON as a string and you want to implement that API including a proper Enum object… an LLM can more easily generate that code than i can, and the longer the list of values the more cumbersome the task gets
especially effective for generating API wrappers because they basically amount to function some_name -> api client -> call /api/someName
this is basically a data transformation problem: translate from some structure to a well-defined chunk of code that matches the semantics of your language of choice
this is annoying for a human, and an LLM can smash out a whole type safe library in seconds based on little more than plain english docs
it might not be 100% right, but the price for failure is an error that you’ll see and can fix before the code hits production
and of course it’s better to generate all this using swagger specs, but they’re not always available and tend not to follow language conventions quite so well
for a concrete example, i wanted to interact with blackmagic pocket cinema cameras via bluetooth in swift on ios: something they don’t provide an SDK for… they do, however document their bluetooth protocols
…blackmagicdesign.com/…/BlackmagicPocketCinemaCam…
(page 157 if you’re interested)
it’s incredibly cumbersome, and basically involves packing binary data into a packet that represents a different protocol called SDI… this would have been horrible to try and work out on my own, but with the general idea of how the protocol worked, i structured the functions, wrote some test case using the examples they provided, handed chatgpt the pdf and used it to help me with the bitbanging nonsense and translating their commands and positionally placed binaries into actual function calls
could i have done it? sure, but why would i? chat gpt did in 10 seconds what probably would have taken me at least a few hours of copying data from 7 pages of a table in a pdf - a task i dont enjoy doing, in a language i don’t know very well
You can type at 300 words per minute with zero mistakes. Youre able to do than on systems youve never worked on before in languages youve never seen. #Doubt
My AI Skeptic Friends Are All Nuts - fly.io/blog/youre-all-nuts/
Not sure why you're sharing this. This is one of the worst blog post I've read this year. The amount of name calling is unnecessary, childish. It's just not good.
Not total bullshit, but it’s not great for all use cases:
For coding tasks the output looks good on the surface but often I end up changing stuff, meaning it would have been faster up do myself.
For coding I know little about (currently writing some GitHub actions), it’s great at explaining alternatives, pros and cons, to give me a rudimentary understanding of stuff
I’ve also used it to transcribe tutorial screencasts, and then afterwards having a secondary LLM use the transcription to generate documentation (include in prompt: "when relevant, generate examples, use markdown tables, generate plantuml etc)
I’m not against AI use in software development… But you need to understand what the tools you use actually do.
An LLM is not a dev. It doesn’t have the capability to think on a problem and come up with a solution. If you use an LLM as a dev, you are an idiot pressing buttons on a black box you understand nothing about.
An LLM is a predictive tool. So use it as a predictive tool.
The one use of AI, at the moment, that I actually like and actually improves my workflow is JetBrains’ full line completion AI. It very often accurately predicts what I want to write when it’s boilerplate-ish, and shuts up when I write something original.
Yes they do have the abikity to think and reason just like you (generally mush faster and slightly better)
medium.com/…/how-gpt-5-compares-to-claude-opus-4-…
96% on the AIME with zero tools. Only reading the question and reasoning through the answer
www.datacamp.com/blog/gpt-5
This is not true. They do not think or reason. They have code that appears to reason, but it definitely is not.
Once it gets off track it doesn’t consider that it is obviously wrong.
A simple math problem can fail and it is really obvious to a human for example.
No, they can’t think and reason. However, they can replicate and integrate the thinking and reasoning of many people who have written about similar problems. And yes, they can do it must faster than we could read a hundred search result pages. And yes, their output looks slightly better than many of us in many cases, because they are often dispensing best practices by duplicating the writings of experts. (In the best cases, that is.)
arxiv.org/pdf/2508.01191
arstechnica.com/…/researchers-find-llms-are-bad-a…
Absolutely not. This comment shows you have absolutely zero idea how an LLM works.
I wouldn’t know about professionally as I don’t work in the industry, but anecdotally a lot of young people I see use LLMs for everything. Meanwhile in the FOSS community online I see very little of AI/LLMs. I think it’s a cultural thing that will vary depending on what circle of people you’re looking at.
I do and it’s great for small tasks. Wouldn’t trust it on an existing code base or more than a hundred lines of code.
I always review what it does and often cherry pick stuff
The only thing I vibe code are small websites / front ends because fuck HTML,CSS,JS
Its an absolute gamechanger, IMO - the research phase of any task is reduced to effectively nothing, and I get massive amounts of work done when I walk away from my desk, because I plan for and keep lists of longer tasks to accomplish during those times.
You need to review every line of code it writes, but that’s no different than it ever was when working with junior devs 🤷♂️ but now I get the code in minutes instead of weeks and the agents actually react to my comments.
We’re using this with a massive monorepo containing hundreds of thousands of lines of code, and in tiny tool repos that serve exactly one purpose. If our code quality checks and standards werent as strict as they have been for the past decade, I think it wouldn’t work well with the monorepo.
The important part is that my company is paying for it - I have no clue what these tools cost. I am definitely more productive, there is absolutely no debate there IMO. Is the extra productivity worth the extra cost? I have literally no idea.
I use it to vet ideas, concepts, approaches, and paradigms. It’s amazing for rubber ducking. I don’t use it for wholesale code gen though.
And as a documentation companion it’s pretty rad. Not always right but generally gets things in the correct direction.
I like using it. Mostly for quick ideation, and also for getting rid of some of the tedious shit I do.
Sometimes it suggests a module or library I have never heard of, then I go and look it up to make sure it is real, not malicious and well documented.
I also like using my self hosted AI to document my code base in a readme following a template I provide. It gets it pretty good and usually is like 60-80% accurate and to the form I like. I just edit up the remaining and correct mistakes. Saves me a ton of time.
I think the best way to use AI is to use it like a tool. Don’t have it write code for you, but use it to enhance your own ability.
I’m enjoying it, mostly. It’s definitely great at some tasks and terrible at orhers. You get a feel for what those are after a while:
Throwaway projects - proof of concepts, one-off static websites, that kind of thing: absolutely ideal. Weeks of dev becomes hours, and you barely need to bother reviewing it if it works.
Research (find a tool for doing XYZ) where you barely know the right search terms: ideal. The research mode on claude.ai is especially amazing at this.
Anything where the language is unfamiliar. AI bootstraps past most of the learning curve. Doesn’t help you learn much, but sometimes you don’t care about learning the codebase layout and you just need to fix something.
Any medium sized project with a detailed up front description.
What it’s not good for:
I use llms from both ends. It helps me plan an think through complex code architecture and helps me do the little stuff i do too infrequent to remember. Putting it all together is usually all me.
I’m not a software dev but rather a mathematical researcher. I see zero use for myself or designing any advanced or critical systems. LLM coding is like relying on stack overflow, if you want to solve a novel or sophisticated problem relying on them is the wrong approach.
I’m a full stack web dev. I use it for HTML and CSS (sometimes) else it’s a big waste of time trying to get working modern PHP and JS out of it. Least that’s been my experience
Yes.
Use Claude Sonnet 4