I was super skeptical about a year ago. Copilot was making nice predictions, that was it. This agent stuff is truly impressive.
This isn't a magic code genie, it's a very complicated and very powerful new tool that you need to practice using over time in order to get good results from.
you: HAVE YOU PUT MORE TOKENS IN???? ARE YOU PUTTING THEM IN THE EXPENSIVE MACHINES???
super compelling argument /s
if you want to provide working examples of "prompt engineering" or "context engineering" please do but "just keep paying until the behavior is impressive" isn't winning me as a customer
it's like putting out a demo program that absolutely sucks and promising that if I pay, it'll get good. why put out the shit demo and give me this impression, then, if it sucks?
Then it ran out of money again, and I gave it even more money.
I'm in the low 4 figures a year now, and it's worth it. For a day's pay each year, I've got a junior dev who is super fast, makes good suggestions, and makes working code.
For anyone trying to back of the napkin at $1000 as 4-figures per year, averaged as a day salary, the baseline salary where this makes sense is about ~$260,000/yr? Is that about right lordnacho?
And that's not saying AI tools are the real deal, either. It can be a lot less than a fully self driving dev and still be worth a significant fraction of an entry level dev.
> it's a very complicated and very powerful new tool that you need to practice using over time in order to get good results from.
In essence, you have to do the "engineering" part of the app and they can write the code pretty fast for you. They can help you in the engineering part, but you still need to be able to weigh in whatever crap they recommend and adjust accordingly.
Worth it to me as I can fix all the above after the fact.
Just annoying haha
One could even imagine going a step further and having a confidence level associated with different parts of the code, that would help the LLM concentrate changes on the areas that you're less sure about.
You can also literally do exactly what you said with "going a step further".
Open Claude Code, run `/init`. Download Superwhisper, open a new file at project root called BRAIN_DUMP.md, put your cursor in the file, activate Superwhisper, talk in stream of consciousness-style about all the parts of the code and your own confidence level, with any details you want to include. Go to your LLM chat, tell it to "Read file @BRAIN_DUMP.md" and organize all the contents into your own new file CODE_CONFIDENCE.md. Tell it to list the parts of the code base and give it's best assessment of the developer's confidence in that part of the code, given the details and tone in the brain dump for each part. Delete the brain dump file if you want. Now you literally have what you asked for, an "index" of sorts for your LLM that tells it the parts of the codebase and developer confidence/stability/etc. Now you can just refer to that file in your project prompting.
Please, everyone, for the love of god, just start prompting. Instead of posting on hacker news or reddit about your skepticism, literally talk to the LLM about it and ask it questions, it can help you work through almost any of this stuff people rant about.
Despite explicit instructions in all sorts of rules and .md’s, the models still make changes where they should not. When caught they innocently say ”you’re right I shouldn’t have done that as it directly goes against your rule of <x>”.
Just to be clear, are you suggesting that currently, with your existing setup, the AI’s always follow your instructions in your rules and prompts? If so, I want your rules please. If not, I don’t understand why you would diss a solution which aims to hardcode away some of the llm prompt interpretation problems that exist
Sure I have to be sure what I'm committing and running is good, especially in critical domains. The cheap cost of iteration before actual commit IMO is the one reason why LLM's are disruptive in software and other "generative" domains in the digital world. Conversely real-time requirements, software that needs to be relied on (e.g. a life support system?), things that post opinions in my name online etc will probably, even if written by a LLM, will need someone accountable and verifying the output.
Again as per many other posts "I want to be wrong" given I'm a senior in my career and would find it hard to change now given age. I don't like how our career is concentrating to the big AI labs/companies rather than our own intelligence/creativity. But rationally its hard to see how software continues to be the same career going forward and if I don't adapt I might die. I will most likely going forward, similar to what I do with my current team, just define and verify.
But! There's still room for expertise. And this is where I disagree about swimming with the tide. There will be those who are uninterested in using the AI. They will struggle. They will hone their craft. They will have muscle memory for the tasks everyone else forgot how to do. And they will be able to perform work that the AI users cannot.
The future needs both types.
What does the next generation do when we’ve automated away that work? How do they learn to recognise what good looks like, and when their LLM has got stuck on a dead end and is just spewing out nonsense?
As has been the case for all those jobs changed by programmers, the people who keep an open mind and are willing to learn new ways of working will be fine or even thrive. The people rusted to their seat, who are barely adding value as is, will be forced to choose between changing or struggling.
Those kinds of masses of people don't pivot on a dime.
I have heard the take that "writing code is not what makes you an engineer, solving problems and providing value is what makes you an engineer" and while that's cool and all and super important for advancing in your career and delivering results, I very much also like writing code. So there's that.
That's not to say there aren't vocations, or people in software who feel the way you do, but it's a tiny minority.
I've been experimenting with a toolchain in which I speak to text to agents, navigate the files with vim and autocomplete, and have Grok think through some math for me. It's pretty fun. I wonder if that will change to tuning agents to write code that go through that process in a semi-supervised manner will be fun? I don't know, but I'm open to the idea that as we progress I will find toolchains that bring me into flow as I build.
But there is also the area of boilerplate, where non-LLM-AI-based IDEs for a few decades already help a lot with templates and "smart" completion. Current AI systems widen that area.
The trouble with AI is when you are reaching the boundary of its capabilities. The trivial stuff it does well. For the complex stuff it fails spectacularly. In the in between you got to review carefully, which easily becomes less fun than simply writing by oneself.
The thing for me is that AI writing the boilerplate feels like the brute force solution, compared to investing in better language and tooling design that may obviate the need for such boilerplate in the first place.
The energy cost is absurdly high for the result, but in current economics, where it's paid by investors not users, it's hidden. Will be interesting to see when AI companies got to the level where they have to make profits and how much optimisation there is to come ...
I think this is a really interesting question and an insight into part of the divide.
Places like HN get a lot of attention from two distinct crowds: people who like computers and related tech and people who like to build. And the latter is split into "people who like to build software to help others get stuff done" and "people who like to build software for themselves" too. Even in the professional-developer-world that's a lot of the split between those with "cool" side projects and those with either only-day-job software or "boring" day-job-related side projects.
I used to be in the first group, liking computer tech for its own sake. The longer I work in the profession of "using computer tools to build things for people" the less I like the computer industry, because of how much the marketing/press/hype/fandom elements go overboard. Building-for-money often exposes, very directly, the difference between "cool tools" and "useful and reliable tools" - all the bugs I have to work around, all the popular much-hyped projects that run into the wall in various places when thrown into production, all the times simple and boring beats cool when it comes to winning customers. So I understand when it makes others jaded about the hype too. Especially if you don't have the intrinsic "cool software is what I want to tinker with" drive.
So the split in reactions to articles like this falls on those lines, I think.
If you like cool computer stuff, it's a cool article, with someone doing something neat.
If you are a dev enthusiast who likes side projects and such (regardless of if it's your day job too or not), it's a cool article, with someone doing something neat.
If you are in the "I want to build stuff that helps other people get shit done" crowd then it's probably still cool - who doesn't like POCs and greenfield work? - but it also seems scary for your day to day work, if it promises a flood of "adequate", not-well-tested software that you're going to be expected to use and work with and integrate for less-technical people who don't understand what goes into reliable software quality. And that's not most people's favorite part of the job.)
(Then there's a third crowd which is the "people who like making money" crowd, which loves LLMs because they look like "future lower costs of labor." But that's generally not what the split reaction to this particular sort of article is about, but is part of another common split between the "yay this will let me make more profit" and "oh no this will make people stop paying me" crowds in the biz-oriented articles.)
The truth is something like: for this to work, there is huge requirements in tooling/infrastructure/security/simulation/refinement/optimization/cost-saving that just could never be figured out by the big companies. So they are just like... well lets trick as many investors and plebs to try to use this as possible, maybe one of them will come up with some breakthrough we can steal
Because of section 174, now hopefully repealed. Money makes the world go round, and the money people talk to the people with firing authority.
There's a huge disconnect I notice where experienced software engineers rage about how shitty things are nowadays while diving directly into using AI garbage, where they cannot explain what their code is doing if their lives depended on it.