LLMs evaporated 90% of the "moments of despair" when you have an error and googling it isn't helping, or googling it made you realize you have to read 30min of documentation.
Coding is a joy now. LLMs shaved off all the rough edges.
I am terrified of allowing these things to complete tasks end-to-end with nothing intervening. Maybe that's why I don't run into many of these issues. I mostly delegate grunt work and manual tedium, not reasoning or design choices to the LLM. I may consult the LLM and ask for criticism, but there is no way I'm going to allow it to quietly make design decisions that I don't know about.
I use LLMs in the following ways:
1. Copy-pasting code into the web chat UI and asking for something (bugfix, add a feature, refactor, explain, review it etc), including entire source code files. A $20/mo Gemini subscription goes a long way (never been rate-limited). I only use the highest model. I often just copy-paste the entire source file between 3 backticks.
2. Cursor Tab. I do have hotkeys to enable and disable it; it's disabled most of the time otherwise it gets annoying.
3. Single-file changes directly from Cursor's AI sidebar. I only do this for simple, predictable stuff because even their auto-routing "Premium" setting is not as good as pasting stuff into Gemini 3.1 Pro.
That means I have only two $20/mo subscriptions: Gemini and Cursor.
I don't use Claude Code, it's really for people who don't know how to code. I don't use Plan Mode; I make and track the plan myself (if at all). I only tell the LLM granular tasks to execute. I don't use `claude.md` or `agents.md` or anything like that. If I don't like a particular output, I reset everything, modify my prompt and try again.
I believe this is the only way to fully leverage LLMs without losing any product quality. If you're trading off quality for "speed" (in quotes because over the long term, a low quality codebase is a massive drag on productivity) then there's no point.
Is that accurate?
What counts as “works” is the important bit, I think.
The LLM will do what you tell it to do. Manage it.
And then condensed an equal quantity of despair out of the ether via confident confabulations.
It's getting hard to keep up with trying to teach new devs what bad code looks like. And I swear sometimes they just copy my PR comments into their AI tool to fix the mistakes without any of the learning.
How have you set yours up that works well for you?
Take the bad result that you're getting, and pretend it's coming from an enthusiastic junior. What would you tell them to make them do this task better? Add that explanation to the agent (or explain that to the LLM and get it to add that to the agent, I have found this to work as well).
When you create a task for the LLM, get it to create a requirements document that lists all the requirements. Feed that into the review agent so it understands what the code agent was trying to do.
The LLM will do what you tell it to do. It doesn't magically understand what you want it to do. You have to tell it what to do.
A year ago I would've told my boss “can't be done” about my work today. I'd tell him to get me the right person to talk to (our partner, not an alien) who could give me some insight into what the hell I'm supposed to be doing to consume their API. Or to at least explain why it is that this can't be done.
Nowadays, I spent a couple of weeks reverse engineering their terrible ideas. Yeah, it worked. But it's a complete waste of my time, and tokens, energy, chips and RAM. And worst of all, it will lead to a terrible design.
That will work, but will eventually colapse under its own weight, as we use our increased power to increase our sloppiness and take it a little further. Because we can manage it. For now.
It writes the code for you. Then it runs the tests. Then it runs the linter. Then it runs the static analysis tool. If any of those fail, then it rewrites the code and runs them all again.
You only look at the code once it has done all of that.
If AI is ignoring the rest of the document and doing whatever it wants then you need to improve your document-writing skills. You can ask it why it did something, that helps discover how to improve. It's a process of refinement and discovery, just like learning how to use any new tool.
I use AI in two ways. In the way you describe, and also in the text editor as AI autocomplete. It works great until it doesn't. It inserts typos all the fucking time.
Also, don't assume you know it all.
I don't miss wasting an hour on a problem in a technology I'm not familiar with, where it's not like a big conceptual thing but something I could clear up in 5 seconds if I just had an expert in the room.
These LLMs can already incorporate our entire cultural corpus yet your "professional experience" is the threshold they won't cross?
If LLMs get good enough, one might be tempted to ask so what if most humans can't understand the output? Human civilization has by and large been a constant exercise in us collectively accomplishing more and more while individually comprehending less and less.
Our ancestors likely understood more about hunting live game or murdering each other than we do. Most of us do not consider that a great loss. Most of us living in the modern world depend on things we don't fully comprehend. I'm just not sure how this would lead to being reassured re the human as SWE.
The article you shared has little to do with this. Questions of how to divide up gains technology creates are a separate question from that of the technology itself. Tbh I found what you shared so boring I could barely finish it. I already in this thread made an exhortation to support politicans who commit to erasing inequality. The idea that LLMs can only exist with inequality is nonsensical. The only thing grim about what you shared is the lack of political imagination. It's boring.
Software specialization might look very different in 10 years but I doubt that technically specialized humans will be completely removed from their professions. We might not be carrying bows and arrows anymore but we will be carrying the equivalent of a rope and a Stetson.
I appreciate your points. I agree with you that not all "technically specialized humans will be completely removed" but let's not pretend the comparison is going from a caveman with a spear to a cowboy with a lasso. If you concede it is likely to be very different at some point calling it SWE is no longer useful.
I think SWEs would be better off realizing they have enjoyed a relatively extreme level of privilege, and rather than trying to hold onto it, use what time they still have to advocate for a more egalitarian society, even if that means giving up some of their gains. Otherwise speaking of farming, the mass layoffs to come when software has been disrupting blue collar jobs for decades will really be a chickens coming home to roost moment.
Considering that only 1% of the US workforce was a software engineer I expect similar workforce optimization to occur in software engineering specializations over the next 12,000 years. /s But seriously, it's never going to zero.
No need for specialized commercial software, if everyone can just explain to the computer what they want in English.
Regarding such formal reasoning we have already seen marked improvement in the last year or two alone. The question is how this weighs on your prediction re their capabilities in the next two, five, ten, etc years.
The present notions of harnesses, structured output or looping in the LLM to some external state or sandbox be it debugger output or embedding into a runtime already show early promising results along these lines. I see no reason to believe these gains will not continue over the next five years.
If you have some theories in the converse in that regard I am all ears.
If you think the potential of LLMs is overblown feel free to short the market. I don't pretend to know the future. But if I may, I don't think you are framing the debate in the correct terms. Evidence is an important facet of human affairs. So is risk. Best of luck with your predictions.
I agree with you both - undoubtedly there are still massive gains to be made with the frontier models we have today with tooling and iteration, yet I do not believe there's sufficient evidence to claim we are rolling towards AG/SI on an exponential curve, without some additional breakthroughs given the jagged edges and data used to train models being fundamentally linear
You just need AI to be just good enough to win the tradeoff over a human employee. Just take your average office. Then ask yourself if the bar is really that high. AGI strikes me as an extremely nebulous concept. Better to just list everyone at your office and bucket them with a guess of how soon you think AI will replace them. Or weaken their market power. This is what every corporate boss in America is already doing. I'm merely suggesting rather than hope a graph curves in our individual favor we try to act more collectively as a species. Of course, I don't hold my breath.
I also don't find myself compelled by the notion that the danger to humanity is "AGI". The true danger is as it always has been - each other.
How many years away do you think we are from a “concierge” AI that can do the menial tasks handled by most personal assistants / program managers? Booking flights and hotels and coordinating employee availability?
“Unlimited progress” is not a statement on the rate of progress, it’s a statement on the limits of progress. It’s a much weaker claim than you’re framing it as. Your claim very much is that we have not yet reached the limits of LLMs potential. My claim, conversely, is that we’re already reaching diminishing returns, which are being masked by a massive influx of compute and energy. My short: LLMs are not the path to AGI.
Most likely because you haven't constrained their behavior in your prompt. You're making the assumption that they "understand" that using best practices is what you want. You have to tell them that, and tell them which practices they should use.
If incorrect LLM output is a prompt issue then demand for experienced developers will remain, and demand may actually increase as time passes.