Usually the CEO or investor says 30% (or some other made up number) of all code is written by AI and the number will only increase, implying that developers will soon be obsolete.
It's implied that 30% of all code submitted and shipped to production is from AI agents with zero human interaction. But of course this is not the case, it's the same developers as before using tools to more rapidly write code.
And writing code is only one part of a developer's job in building software.
I vibed probably around 40-50% of the code so far, the rest I wrote myself. $30 spent up to this point and the app is 60% done. I’m curious what my total expenses will be at the end of all this.
edit: typos
Lots of changes where describing them in English takes longer than just performing the change. I think the most effective workflow with AI agents will be a sort of active back-and-forth.
Here's a fresh example that I stumbled upon just a few hours ago. I needed to refactor some code that first computes the size of a popup, and then separately, the top left corner.
For brevity, one part used an "if", while the other one had a "switch":
if (orientation == Dock.Left || orientation == Dock.Right)
size = /* horizontal placement */
else
size = /* vertical placement */
var point = orientation switch
{
Dock.Left => ...
Dock.Right => ...
Dock.Top => ...
Dock.Bottom => ...
};
It sort of makes sense: once the model has "completed" an if, and then encounters the need for a similar thing, it will pick an "if" again, because, well, it is completing the previous tokens.
Harmless here, but in many slightly less trivial examples, it would just steamroll over nuance and produce code that appears good, but fails in weird ways.
That said, splitting tasks into smaller parts devoid of such ambiguities works really well. Way easier to say "store size in m_StateStorage and apply on render" than manually editing 5 different points in the code. Especially with stuff like Cerebras, that can chew through complex code at several kilobytes per second, expanding simple thoughts faster than you could physically type them.
Give it to o3 and it could definitely handle that today.
Sweeping generalizations about how LLMs will never be able to do X, Y, or Z coding task will all be proven wrong with time, imo.
Whatever can be statistically predicted
by the human brain
Will one day also be
statistically predicted by melted sand
Or the current strategies to scale across boards instead of chips gets too expensive in terms of cost, capital, and externalities.
Look at how well Deepseek performed with the limited, outdated hardware available to its researchers. And look at what demoscene practitioners have accomplished on much older hardware. Even if physical breakthroughs ceased or slowed down considerably, there is still a ton left on the table in terms of software optimization and theory advancement.
And remember just how young computer science is as a field, compared to other human practices that have been around for hundreds of thousands of years. We have so much to figure out, and as knowledge begets more knowledge, we will continue to figure out more things at an increasing pace, even if it requires increasingly large amounts of energy and human capital to make a discovery.
I am confident that if it is at all possible to reach human-level intelligence at least in specific categories of tasks, we're gonna figure it out. The only real question is whether access to energy and resources becomes a bigger problem in the future, given humanity's currently extraordinarily unsustainable path and the risk of nuclear conflict or sustained supply chain disruption.
When I tried giving top-notch LLMs harder tasks (scan an abstract syntax tree coming from a parser in a particular way, and generate nodes for particular things) they completely blew it. Didn't even compile, let alone logical errors and missed points. But once I broke down the problem to making lists of relevant parsing contexts, and generating one wrapper class at a time, it saved me a whole ton of work. It took me a day to accomplish what would normally take a week.
Maybe they will figure it out eventually, maybe not. The point is, right now the technology has fundamental limitations, and you are better off knowing how to work around them, rather than blindly trusting the black box.
I think it's a combination of
1) wrong level of granularity in prompting
2) lack of engineering experience
3) autistic rigidity regarding a single hallucination throwing the whole experience off
4) subconscious anxiety over the threat to their jerbs
5) unnecessary guilt over going against the tide; anything pro AI gets heavily downvoted on Reddit and is, at best, controversial as hell here
I, for one, have shipped like literally a product per day for the last month and it's amazing. Literally 2,000,000+ impressions, paying users, almost 100 sign ups across the various products. I am fucking flying. Hit the front page of Reddit and HN countless times in the last month.
Idk if I break down the prompts better or what. But this is production grade shit and I don't even remember the last time I wrote more than two consecutive lines of code.
Except, not all work is like that. Fast-forward to product version 2.34 where a particular customer needs a change that could break 5000 other customers because of non-trivial dependencies between different parts of the design, and you will be rewriting the entire thing by humans or having it collapse under its own weight.
But out of 100 products launched on the market, only 1 or 2 will ever reach that stage, and having 100 LLM prototypes followed by 2 thoughtful redesigns is way better than seeing 98 human-made products die.
AI stands for Artificial Intelligence. There are no inherent limits around what AI can and can't do or comprehend. What you are specifically critiquing is the capability of today's popular models, specifically transformer models, and accompanying tooling. This is a rapidly evolving landscape, and your assertions might no longer be relevant in a month, much less a year or five years. In fact, your criticism might not even be relevant between current models. It's one thing to speak about idiosyncrasies between models, but any broad conclusions drawn outside of a comprehensive multi-model review with strict procedure and controls is to be taken with a massive grain of salt, and one should be careful to avoid authoritative language about capabilities.
It would be useful to be precise in what you are critiquing, so that the critique actually has merit and applicability. Even saying "LLM" is a misnomer, as modern transformer models are multi-modal and trained on much more than just textual language.
Lots of us are interested in technology that's actually available, and we can all read date stamps on comments.
Why not look at Bob who takes like 2 weeks to write tickets on what they actually want in a feature? Or Alice who's really slow getting Figma designs done and validated? How nice would having a "someone's bothered a developer" metric be and having the business seek to get that to zero and talk very loudly about it as they have about developers?
I think an over-reliance, or perhaps any reliance, on AI tools will turn good programmers into slop factories, as they consistently skip over a vital part of creating high-quality software.
You could argue that the prompt == code, but then you are adding an intermediary step between you and the code, and something will always be lost in translation.
I'd say just write the code.
With AI, instead of starting with zero and building up, you can start with a result and iterate on it straight away. This process really shines when you have a good idea of what you want to do, and how you want it implemented. In these cases, it is really easy to review the code, because you knew what you wanted it to look like. And so, it lets me implement some basic features in 15 minutes instead of an hour. This is awesome.
For more complex ideas, AI can also be a great idea sparring partner. Claude Code can take a paragraph or two from me, and then generate a 200-800 line planning document fleshing out all the details. That document: 1) helps me to quickly spot roadblocks using my own knowledge, and 2) helps me iterate quickly in the design space. This lets me spend more time thinking about the design of the system. And Claude 4 Opus is near-perfect at taking one of these big planning specifications and implementing it, because the feature is so well specified.
So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.
I wonder if the independent studies that show Copilot increasing the rate of errors in software have anything to do with this less bold attitude. Most people selling AI are predicting the obsolescence of human authors.
Even if code is the right medium for specifying a program, transformers act as an automated interface between that medium and natural language. Modern high-end transformers have no problem producing code, while benefiting from a wealth of knowledge that far surpasses any individual.
> Most people selling AI are predicting the obsolescence of human authors.
It's entirely possible that we do become obsolete for a wide variety of programming domains. That's simply a reality, just as weavers saw massive layoffs in the wake of the automated loom, or scribes lost work after the printing press, or human calculators became pointless after high-precision calculators became commonplace.
This replacement might not happen tomorrow, or next year, or even in the next decade, but it's clear that we are able to build capable models. What remains to be done is R&D around things like hallucinations, accuracy, affordability, etc. as well as tooling and infrastructure built around this new paradigm. But the cat's out of the bag, and we are not returning to a paradigm that doesn't involve intelligent automation in our daily work; programming is literally about automating things and transformers are a massive forward step.
That doesn't really mean anything, though; You can still be as involved in your programming work as you'd like. Whether you can find paid, professional work depends on your domain, skill level and compensation preferences. But you can always program for fun or personal projects, and decide how much or how little automation you use. But I will recommend that you take these tools seriously, and that you aren't too dismissive, or you could find yourself left behind in a rapidly evolving landscape, similarly to the advent of personal computing and the internet.
Perhaps LLM can be modified to step outside the circle, but as of today, it would be akin to monkeys typing.
Why wouldn't your boss ask ChatGPT directly?
> Source: The Times of India
what in the recycled content is this trash?
1. There was no error on line 91, it did some inconsequential formatting on that line 2. More importantly, it just ignored the very specific line I told it to go to. It's like I was playing telephone with the LLM which felt so strange with text-based communication.
This was me trying to get better at using the LLM while coding and seeing if I could "one-shot" some very simple things. Of course me doing this _very_ tiny fix myself would have been faster. Just felt weird and reinforces this idea that the LLM isn't actually thinking at all.
And now you've learned that LLMs can't count lines. Next time, try asking it to "fix the error in function XYZ" or copy/paste the line in question, and see if you get better results.
> reinforces this idea that the LLM isn't actually thinking at all.
Of course it's not thinking, how could it? It's just a (rather big) equation.
You need to give LLMs context. Line number isn't good context.
CoffeeOnWrite•1h ago
(Wow I sound triggered! sigh)
upghost•1h ago
vram22•1h ago
A man is a human.
layer8•1h ago
“Manual” comes from Latin manus, meaning “hand”: https://en.wiktionary.org/wiki/manus. It literally means “by hand”.
anamexis•1h ago
GuinansEyebrows•1h ago
this is okay! it's a sign of your humanity :)
layer8•58m ago
dalyons•24m ago