61–70 of 76 posts
And you immediately give up your IP for someone else to use. The 4th option, if you have something in your mind worth building, is to just build the thing, without an LLM.
almost every inference operator either has ZDR or an opt out from training
unless you think they're just lying and training on business users data
And yes, there is also another sane and rewarding option: write everything just by youserlf without any assistance. Let's not forget about that one, shall we?
It's kind of like folks wielding gen ai and calling themselves artists. Questionable output, generally shunned by artisans. But possibly good enough for some.
Artists are biased when evaluating AI art (same with programmers evaluating AI code).
That said, I'd really like to see data to compare which approach works the best.
Earlier quoted context omitted.
There are still companies who refuse to believe this and still put Senior+ devs through hell during an interview process with junior level algorithm memorization. In all aspects there will be dinosaurs and deniers and there will be embracers.
How can you assess the output of coding agents if you don't know about algorithms and other fundamentals of software engineering?
One aspect AI is weak in is controlling complexity. If you tell it to implement something it will go ahead and implement it, without considering how much complexity it adds to the system or weighing alternatives. An experienced engineer on the other hand may decide the feature is too minor relative to the complexity it adds, and may decide to not do the feature. Or he may make some clever compromises to get most of t…
I feel this mostly is a side effect from lack of domain knowledge. Most of the time this has happened to me, it's because I myself did not cleanly know how a problem should be solved to begin with. If you have a clear picture of what you want, approximately what syntax goes where and why, thats really when LLMs shine in my experience.
One aspect AI is weak in is controlling complexity. If you tell it to implement something it will go ahead and implement it, without considering how much complexity it adds to the system or weighing alternatives. An experienced engineer on the other hand may decide the feature is too minor relative to the complexity it adds, and may decide to not do the feature. Or he may make some clever compromises to get most of t…
Have you found any solutions to this? It would be a big unlock to give it this kind of judgement
Earlier quoted context omitted.
I have agents that have been running nonstop for several days working through tasks. At some point I have to go to sleep, and wake up to more progress. Even with preplanning and post hoc analysis thrown in, I am seeing way more than 2x return on my investment. Where are your numbers coming from?
> wake up to more progress I wonder how that progress is being measured. Lines of code or counts of PRs? Sure... but I thought the matter of measuring productivity by lines of code was already well-understood as being misguided. I'm having trouble reconciling all that supposed productivity with the real world where software isn't getting better, delivered faster, or becoming cheaper - unlike virtually all breakthroug…
It sucks, but you don't usually have the time to pour over code when you manage multiple engineers either, so you have to learn how to do thorough but targeted reviews, minimize distraction, maximize efficiency, etc. A lot of these skills transfer over to managing agents.
We've only had truly decent agents capable of running long-horizon tasks for less than a year, I think it's worth calibrating around that: it's too soon to expect the entire industry to visibly shift.
That said, every senior engineer I know has gone all-in on agentic development, and juniors I mentor are getting a lot done as well.
With juniors it's important to make them understand that these models can't be blindly trusted and the output needs to constantly be critically evaluated.
But engineers who know exactly what they are doing have really been able to make some awesome things this year. I'm also working on a few really cool things, more than before, more ambitious as well, without sacrificing quality or craftsmanship.
I can also seem where some trends are headed. The breadth of software available to both harm and help you is going to explode, and computing is going to look a lot different soon. I'm already building targeted health apps for myself, bespoke personal apps and tooling, development tools, I'm working on games, libraries, various kinds of research, you name it. It feels like an intellectual Renaissance, and within a decade I expect things to look a lot different even if models stopped improving today.
You do have to work differently with these models. Your code evolves in a different way, and testing habits have to adapt. Clients are going to accept less stable but more ambitious demos. Prototyping and research have suddenly become very cheap. We're going to see the effects of the spread through STEM and the arts.
Earlier quoted context omitted.
bubblesort has entered the chat
Better example, leetcode interviews became so popular because people were memorizing FizzBuzz and they needed an alternative (but said interviewers didn't fully understand the purpose of FizzBuzz and though "harder means better, right?").
This thinking was popularized by coding competions which existed before leetcode and inspired leetcode. Schools themselves produced people who believed these puzzles are what makes you superior developer - one of us, special and choosen.
And yes, there is also another sane and rewarding option: write everything just by youserlf without any assistance. Let's not forget about that one, shall we?
sane as in your boss will let you do that?
But if the person that is above me forces me to use the latest craze tool to do *my* job then that is no longer a place for me to stay.