But if you had instead spent the "weeks to learn your way around the codebase", that would have given dividends forever. I'm a bit afraid that by oneshoting features like these, many will never get to the required level to do bigger changes that relies on a bigger understanding.

Of course, LLMs might get there eventually. But until then I think it will create a bigger divide between seniors and juniors than it traditionally has been.

> If you cannot afford the hundreds of dollars a month Claude costs

Employers will buy AI tools for their employees, this isn't a problem.

If you're saying that you need to buy and learn these tools yourself in order to get a job, I strongly disagree. Prompting is not exactly rocket science, and with every generation of models it gets easier. Soon you'll be able to pick it up in a few hours. It's not a differentiator.

> make an impact.

To me, a junior devs biggest job is learning and not delivering value. Is a pitfall I'm seeing in my own team where he is so focused on delivering value that he's not gaining an understanding.

You're sabotaging yourself though. You are avoiding learning.

What's the point of shipping a Chrome feature before graduating? Just to put in your CV that you've committed in some repo? In the past this would be signal of competence, but now you're working towards a future where doing this thing is not competence signaling anymore.

Some companies do not like you upload their code to 3rd parties

I'm curious what tooling you are using to accomplish this?

Two problems:

1) Writing a high-performance memory allocator for a game engine in Rust: https://github.com/golddranks/bang/tree/main/libs/arena/src (Still work in progress, so it's in a bit messy state.) Didn't seem to understand the design I had in mind, and/or the requirements and goes on tangents and starts changing the design. In the end, coded the main code myself and used LLM for writing tests with some success. Had to remove tons of inane comments that didn't provide any explanatory value.

2) Trying to fix a Django ORM expression that generates unoptimal and incorrect SQL. Constantly changes opinion whether something is even possible or supported by Django, apologizes when pointing out mistakes / bugs / hallucinations, but then proceeds to not internalize the implications of the said mistakes.

I used the Zed editor with its recently published agentic features. I tried to prompt it with a chat style discussion, but it often did bigger edits I would have liked, and failed to share a high-level plan in advance, something I often requested.

My biggest frustrations were not coding problems per se, but just general inability to follow instructions and see implications, and lacking the awareness to step back and ask for confirmations or better directions if there are "hold on, something's not right" kind of moments. Also, generally following through with "thanks for pointing that out, you are absolutely right!" even if you are NOT right. That yes-man style seriously erodes trust in the output.

I feel like the opposite is true but maybe the issue is that we both live in separate bubbles. Often times I see people on X and elsewhere making wild claims about the capabilities of AI and rarely do they link to the actual output.

That said, I agree that AI has been amazing for fairly closed ended problems like writing a basic script or even writing scaffolding for tests (it's about 90% effective at producing tests I'd consider good assuming you give it enough context).

Greenfield projects have been more of a miss than a hit for me. It starts out well but if you don't do a good job of directing architecture it can go off the rails pretty quickly. In a lot of cases I find it faster to write the code myself.

Aside from the fact that you seem to be demanding a lot from someone who's informally sharing their experience online, I think the effectiveness really depends on what you're writing code for. With straightforward use cases that have ample documented examples, you can generally expect decent or even excellent results. However, the more novel the task or the more esoteric the software library, the likelier you are to encounter issues and feel dissatisfied with the outcomes. Additionally, some people are simply pickier about code quality and won't accept suboptimal results. Where I work, I regularly encounter wildly enthusiastic opinions about GenAI that lack any supporting evidence. Dissenting from the mainstream belief that AI is transforming every industry is treated as heresy, so such skepticism is best kept close to the chest—or better yet, completely to oneself.

Yeah, that's obvious. It's even worse for blog posts. Pro-LLM posts usually come with the whole working toy apps and the prompts that were used to generate them. Anti-LLM posts are usually some logical puzzles with twists.

Anyway that's the Internet for you. People will say LLM has been plateaued since 2022 with a straight face.

Maybe you are not reading what we are writing :) Here is an article of mine https://omarabid.com/gpt3-now

> But for certain use cases (e.g. simple script, written from scratch), it's absolutely fantastic.

I agree with that. I've found it to be very useful for "yarn run xxx" scripts. Can automate lots of tasks that I wouldn't bother with previously because the cost of coding the automation vs. doing them manually was off.

i think these developments produce job/economic anxiety and so a certain percentage of people react this way, even higher percents on reddit where there is a lot of job anxiety

Reminds me of the early days of endless “ChatGPT can’t do X” comments where they were invariably using 3.5 Turbo instead of 4, which was available to paying users only.

Humans are much lazier than AIs was my takeaway lesson from that.

I sincerely hope you'll manage to find a job!

What I meant was purely from the capabilities perspective. There's no way a current AI model would outperform an average junior dev in job performance over... let's say, a year to be charitable. Even if they'd outperform junior devs during the first week, no way for a longer period.

However, that doesn't mean that the business people won't try to pre-empt potential savings. Some think that AI is already good enough, and others don't, but they count it to be good enough in the future. Whether that happens remains to be seen, but the effects are already here.

If I may ask: what university was this? Asking as I am the CTO of a YC startup and we are hiring junior engineers in Berlin!