Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!

There were all these theories like Comparative Advantage that people would trot out to point out that, if you don't like outsourcing, not only are you ignorant and backwards you're also probably racist.

This is already showing not to be true with AI. Institutional knowledge is not the same as knowing how to implement low-level software details. Even today, every company does not need engineers to remember git cli syntax by memory, how to write parsers for JSON, or write the large amount of boilerplate from scratch that is at every software company. Most company's already hire engineers who have zero experience in the existing code base, yet they are productive despite this lack of institutional knowledge. For a company to maintain institutional knowledge they may only need N/K engineers.

Do not share videos with si parameters. It links together the accounts of the sender and receiver.

To build a $100M software company, you need 6 engineers and 6 laptops.

To build a $100M hardware company, you need 60 engineers and $100M.

Everyone decided on the most logical choice. It gets even worse if you jump into profit margins, as software is the unambiguous winner there too.

The resilience is more in ensuring archival process to ensure it's never lost forever, and a continuity plan to ensure enough (but few) people still know.

Cyclomatic complexity has been pretty solidly discredited within the maintainability research community for decades.

Sonar's cognitive complexity metric is a bit better, but here's a study that found that it still only has about a 0.5 correlation with how much difficulty programmers actually had reading code as measured by multiple methods.

They found that the most accurate way to measure code complexity that didn't involve something like an eye tracker or EEG is still basically just vibes - asking programmers if they thought it was hard to understand.

https://www.frontiersin.org/journals/neuroscience/articles/1...

Halstead Effort came in second, and scored pretty well, but here's another one where it doesn't do so well, either. And it scores the SonarQube metrics even worse, with only a 0.35 correlation: https://www.sciencedirect.com/science/article/abs/pii/S01641...

There's the rub. It requires knowing about and caring about maintainability. And a lot of the people who "haven't written a line of code since 2025" don't care

> There are dozens of ways to measure code maintainability.

There are no good ways. I'm averse to making absolute statements, but here I'll take that chance. I worked in dev producitivy for years with people who spent decades in that domain across multiple companies with very high volumes of code production. Everybody agreed: All metrics are flawed and even a combination of metrics is insufficient.

Just to give one fundamental reason (in addition to a lot of the sibling comments): for any given metric there are an infinite set of counter-examples that don't trigger any thresholds but are clearly bad code. So these metrics typically only help in trivial cases, don't catch a majority of the cases, and so often become more of an annoyance due to low SNR. A lot of dev productivity work ends up being wiring these metrics in and then providing escape hatches when they inevitably get too noisy!

And most relevant to this discussion: these tools do not say anything about higher-level concerns like architecture, over-engineering and design, which IME is where agents tend to mess up most. I've almost never had a complaint about the code itself; the logic, naming, functions, data structures, even a lot of the testing, are all on point. It's always been the higher-level structure and design: over-engineering, duplicate classes, suboptimal abstractions, redundant operations across layers that could be solved by adding a single variable in a class, etc. etc.

I think the problem, like with code written by humans, is lack of sufficient context while doing a task leading to tunnel-vision. This is why we need to oversee and ensure things are good holistically. I suspect models are now good enough to play the role of an architect as well, though, and I've read some indications of that online... I just haven't tried giving them that much control yet.

You say that like adding "Make it maintainable." to your prompts solves the problem. But the reality is we only have weak metrics for measuring maintainability. For example, you can trivially optimize for Cyclomatic complexity by blowing away abstractions and duplicating code everywhere. That doesn't make the code better. Cyclomatic complexity is a tool that has to be applied judiciously.

That doesn't mean you can't or shouldn't use AI to generate code. But it does mean if you want your project to scale, you're still going to need a lot of developer involvement at the code level to ensure the code remains maintainable so that future developers can build on top of it. AI is not like compilers, which allow developers to build complex solutions without being proficient at the next level down (assembly).

Yes. But I think the idea is without "hand written domain driven design development" the result trends to "vibe coded by someone with no technical knowledge or inclination," as developers de-skill.

Code examples are literally bread and butter when it comes to learning.

How can you learn without looking at code? That's like saying that you can learn to be an architect without looking at drawings...

It is fault tolerant, distributed broker which gurantees durable queues, pub/sub and RPC all into one easy to use programming model. The application is in production and passing millions of messages every day with sub-millisecond performance, you can crash a server and replica set invokes within seconds without losing any messages. The entire project is created in less than a month with part time working, just because of AI. Its in production and already proven.

You can make a choice not to become a button pusher and still do things by hand. You dont have to fry your brain. You're falling for a massive trap to strip you of your value.

I understand why people are resistant to this from an emotional perspective, but I really don't see a plateau in sight. RLVR is clearly still cooking and narrow RSI seems to be on the horizon.

But I'm also a realist. If the technology exists, it will be used to the maximum economical extent.

Why would companies employ human engineers then? What is the value addition to justify high human salaries. If AI is going to get so good (and I am not saying it won’t happen, that’s a separate debate), why can’t AI figure out the prompts itself?

Also a confirmation to people who have the same inner thoughts and are ashamed to admit in public that they think the exact same thing.

I think we need such kind of posts to combat the influx of AI news.

What you don't see from most perspectives are the silent masses who simply don't engage, don't care about the discussion, and/or are too busy doing what they enjoy.

That mindset has also deteriorated my working environment due to some coworkers buying into it.

So it's kind of nice to see some sanity checks that align with my beliefs too. I can share articles like this with my teammates. I can see that I'm not alone in thinking most LLM code is slop.

I think too junior devs NEED to see this. My team had a couple of promising juniors who are now completely brain rotted by AI and can't even write "Hello World" without consulting Claude anymore.

Anyone could say the same about any post they don't agree with, doesn't seem very helpful.