I have never learned so quickly in my entire life than to post a forum thread in its entirety into a extended think LLM and then be allowed to ask free form questions for 2 hours straight if I want to. Having my questions answered NOW is so important for me to learn. Back in the day by the time I found the answer online I forgot the question

Some people talk like skill atrophy is inevitable when you use LLMs, which strikes me as pretty absurd given that you are talking about a tool that will answer an infinite number of questions with infinite patience.

I usually learn way more by having Claude do a task and then quizzing it about what it did than by figuring out how to do it myself. When I have to figure out how to do the thing, it takes much more time, so when I'm done I have to move on immediately. When Claude does the task in ten minutes I now have several hours I can dedicate entirely to understanding.

I used to speak Russian like I was born in Russia. I stopped talking Russian … every day I am curious ans responsible but I can hardly say 10 words in Russian today. if you don’t use it (not just be curious and responsible) you will lose it - period.

That’s always been the case, AI or not.

It's a new problem in the sense that now executive management at many (if not most) software companies is pushing for all employees to work this way as much as possible. Those same people probably don't know what stack overflow even is.

In my experience, no - I think the ability to build more complete features with less/little/no effort, rather than isolated functions, is (more) appealing to (more) developers.

It's difficult to copy & paste an entire app from Stack Overflow

I don't think so. I'll spend a ton of time and effort thinking through, revising, and planning out the approach, but I let the agent take the wheel when it comes to transpiling that to code. I don't actually care about the code so long as it's secure and works.

I spent years cultivating expertise in C++ and .NET. And I found that time both valuable and enjoyable. But that's because it was a path to solve problems for my team, give guidance, and do so with both breadth and depth.

Now I focus on problems at a higher level of abstraction. I am certain there's still value in understanding ownership semantics and using reflection effectively, but they're broadly less relevant concerns.

Copied and pasted without noting the license that stack overflow has on code published there, no doubt

Hey. I resemble that remark sometimes!! quit being a hater (sarcasm) :P

They're so much louder now, though.

People who got into the job who don’t really like programming

These people have always existed. Hell, they are here, too. Now they have a new thing to delegate responsibility to.

And no, I don't understand them at all. Taking responsibility for something, improving it, and stewarding it into production is a fantastic feeling, and much better than reading the comment section. :)

I strongly agree on the refactor, but for debugging I have another perspective: I think debugging is changing for the better, so it looks different.

Sure, you don't know the code by heart, but people debugging code translated to assembly already do that.

The big difference is being able to unleash scripts that invalidate enormous amount of hypothesis very fast and that can analyze the data.

Used to do that by hand it took hours, so it would be a last resort approach. Now that's very cheap, so validating many hypothesis is way cheaper!

I feel like my "debugging ability" in terms of value delivered has gone way up. For skill, it's changing. I cannot tell, but the value i am delivering for debugging sessions has gone way up

As someone who's switched from mobile to web dev professionally for the last 6 months now. If you care about code quality, you'll develop that neural connection after some time.

But if you don't and there's no PR process (side projects), the motivation to form that connection is quite low.

That is not the entire selling point - so you are very wrong.

You very much decide how you employ LLMs.

Nobody are keeping a gun to your head to use them. In a certain way.

Sonif you use them in a way that increase you inherent risk, then you are incredibly wrong.

I see it completely the opposite way, you use an LLM and correct all its mistakes and it allows you to deliver a rough solution very quickly and then refine it in combination with the AI but it still gets completely lost and stuck on basic things. It’s a very useful companion that you can’t trust, but it’s made me 4-5x more productive and certainly less frustrated by the legacy codebase I work on.

Yeah I whole hardheartedly disagree with this. Because I understand the basics of coding I can understand where the model gets stuck and prompt it in other directions.

If you don't know whats going on through the whole process, good luck with the end product.

They purportedly solve the problem of needing skill to get things done. IME, this is usually repeated by VC backed LLM companies or people who haven’t knowingly had to deal with other people’s bad results.

This all bumps up against the fact that most people default to “you use the tool wrong” and/or “you should only use it to do things where you already have firm grasp or at least foundational knowledge.”

It also bumps against the fact that the average person is using LLM’s as a replacement for standard google search.

This is such a weird statement in so many levels.

The latent assumption here is that learning is zero sum.

That you can take a 30 year old from 1856 bring them into present day and they will learn whatever subject as fast as a present day 20 year old.

That teachers doesn't matter.

That engagement doesn't matter.

Learning is not zero sum. Some cultural background makes learning easier, some mentoring makes is easier, and some techniques increases engagement in ways that increase learning speed.

So, you havent really learned anything from any teacher if you could not do it again without them?

I could definitely maintain the infrastructure without an llm. Albeit much slower.

And yes. If LLMs disappear, then we need to hire a lot of people to maintain the infrastructure.

Which naturally is a part of the risk modeling.

I think this is a bit dismissive.

It’s quite possible to be deep into solving a problem with an LLM guiding you where you’re reading and learning from what it says. This is not really that different from googling random blogs and learning from Stack Overflow.

Assuming everyone just sits there dribbling whilst Claude is in YOLO mode isn’t always correct.

>> I am learning a new skill with instructor at an incredible rate

> Could you do it again on your own?

Can you you see how nonsensical your stance is? You're straight up accusing GP of lying they are learning something at the increased rate OR suggesting if they couldn't learn that, presumably at the same rate, on they own, they're not learning anything.

That's not very wise to project your own experiences on others.

The challenge is not if you could do all of it without AI but any of it that you couldn't before.

Not everyone learns at the same pace and not everyone has the same fault tolerance threshold. In my experiencd some people are what I call "Japanese learners" perfecting by watching. They will learn with AI but would never do it themselves out of fear of getting something wrong while they understand most of it, others that I call "western learners" will start right away and "get their hands dirty" without much knowledge and also get it wrong right away. Both are valid learning strategies fitting different personalities.

I believe some professor warned us about being over reliant on Google/reddit etc: “how would you be productive if internet went down” dilemma.

Well, if internet is down, so is our revenue buddy. Engineering throughput would be the last of our concerns.

If you've seen further it's only because you've stood on the shoulders of giants.

Having other people do work for you is how people get to focus on things they actually care about.

Do you use a compiler you didn't write yourself? If so can you really say you've ever learned anything about computers?

It is easy to not believe if you only apply an incredibly narrow world view.

Open your eyes, and you might become a believer.

It's partly that, but also reading and surface level understanding something vs generating yourself are different skills with different depths. If you're learning a language, you can get good at listening without getting good at speaking for example.

Yep and in a vendor lock in scenario, fixing deep bugs or making additions in surgical ways is where the value is. And Claude helps you do that, by giving you more information, analyzing options, but it doesn’t let you make that decision 10x faster.

Well it might.

If the actual rate is .9x then it matters a lot.

Or even if it's like 1.1x, is the cost worth the return?

What if the rate is negative?

Would it matter?

yea that is probably the worst part of these techs becoming mainstream services and local-LLM'ing taking off in general: working with them at many points in any architecture no longer feels... deterministic i guess. way too fucking much "heres what i use" but no real best practices yet, just a lot of vague gray area and everyones still in discovery-mode on how to best find some level of determinism or workflow and ways we are benchmarking is seriously a moving target. everyone has their own branded take on what the technology is and their own branded approach on how to use it, and it's probably the murkiest and foggiest time to be in technology fields that i've ever seen :\ seems like weekly/monthly something is outdated, not just the models but the tooling people are parroting as the current best tooling to use. incredibly frustrating. there's simply too much ground to cover for any one person to have any absolute takes on any of it, and because a handful of entities are currently leading the charge draining lakes and trying to compete for every person and every businesses money, there's zero organized frameworks at the top to make some sense of this. they all are banking on their secret sauce, and i _really_ want us all to get away from this. local inference has to succeed imo but goddamn there needs to be some collective working together to rally behind some common strats/frameworks here. im sure there's already countless committees that have been established to try and get in front of this but even that's messy.

i don't know how else to phrase it: this feels like such an unstable landscape, "beta" software/services are running rampant in every industry/company/org/etc and there's absolutely no single resource we can turn to to help stay ahead of & plan for the rapidly-evolving landscape. every, and i mean every company, is incredibly irresponsible for using this stuff. including my own. once again though, cat's already out of the bag. now we fight for our lives trying to contain it and ensure things are well understood and implemented properly...which seems to be the steepest uphill battle of my life

Right; but every major generation has had diminishing returns on the last. Two years ago the difference was HUGE between major releases, and now we're discussing Opus 4.6 Vs. 4.7 and people cannot seem to agree if it is an improvement or regression (and even their data in the card shows regressions).

So my point is: If you have the attitude that unless it is the bleeding edge, it may have well not exist, then local models are never going to be good enough. But truth is they're now well exceeding what they need to be to be huge productivity tools, and would have been bleeding edge fairly recently.

> I completely see your point, but when my / developer time is worth what it is compared to the cost of a frontier model subscription, I'm wary of choosing anything but the best model I can.

Don't you understand that by choosing the best model we can, we are, collectively, step by step devaluating what our time is worth? Do you really think we all can keep our fancy paychecks while keep using AI?

At the rate its consuming now, I'd probably blow $10k in a month easy.

For a business with ten or more engineers/people-using-ai, it might still make sense to set this up. For an individual though, I can’t imagine you’d make it through to positive ROI before the hardware ages out.

What near SOTA open models are you referring to?

I'm backing up a big dataset onto tapes, so I wanted to automate it. I have an idle 64Gb VRAM setup in my basement, so I decided to experiment and tasked it with writing an LTFS implementation. LTFS is an open standard for filesystems for tapes, and there's an implementation in C that can be used as the baseline.

So far, Qwen 3.6 created a functionally equivalent Golang implementation that works against the flat file backend within the last 2 days. I'm extremely impressed.

The Codex TUI harness is also open source and you can use open models with it, so you can stay in even more familiar territory.

pi-coding-agent (pi.dev) is also great. I've been using it with Gemma 4 and Qwen 3.6.

The Mac Minis (probably 64GB RAM) are the most cost effective.

Qwen’s 30B models run great on my MBP (M4, 48GB) but the issue I have is cooling - the fan exhaust is straight onto the screen, which I can’t help thinking will eventually degrade it, given the thermal cycling it would go through. A Mac Studio makes far more sense for local inference just for this reason alone.

For a 30B model, you want at least 20GB of VRAM and a 24GB MBP can’t quite allocate that much of it to VRAM. So you’d want at least a 32GB MBP.

There's DeepInfra. There's also OpenRouter where you can find several providers.

Why?

One shoting has a very specific meaning, and agentic workflows are not it?

What is the implied meaning I should understand from them using one shot?

They might refer to the lack of humans in the loop.