When I'm doing it: I can go on way too long trying to consider way too much, when really, putting down some code and reading it and writing it myself would give me a better understanding.

When others are doing it: they can get very entrenched in a certain way of thinking, and are sure it's correct because of their AI conversations. Some context or data point was missing from their conversations with the AI.

They searched for everyone named SN and narrowed it down to the most likely candidate. Foolproof.

This claim from Ayer -- how do we make the leap from these experiences existing to being evidence of a life after consciousness?

> On the face of it, these experiences, on the assumption that the last one was veridical, are rather strong evidence that death does not put an end to consciousness

What good would it truly be if a 3K line function is split into 8 modules? It’ll be neater and more comprehensible to a human reader. More debuggable, definitely.

But given the business problem the have: winner takes all of a massive market, first mover wins, — the right move is to throw the usual rulebook about quality software out the window, and double down on the bets of the company, that AI will make human code engineering less and less necessary very quickly.

It turned out incredibly well despite the “bad” engineering — which in this case, I really count as good engineering.

But some of the worst experiences I’ve had with coworkers were with those who made programming part of their identity. Every technical disagreement on a PR became a threat to identity or principles, and ceased being about making the right decision in that moment. Identity means: there’s us, and them, and they don’t get it.

‘Programmer’ is much better off as a description of one who does an activity. Not an identity.

There’s simply no advantage to crediting work to someone who’d left the team.

We love to blame those who are misfortunate. It’s called just world syndrome. It’s deeply uncomfortable to realize that this kind of thing is the norm, and justice is the exception. I’ve been extremely fortunate in my career, but not due to any special savviness of my own.

At my workplace now, there’s a senior staff engineer taking credit for work that was done by someone 3 levels below him. And the senior staff engineer still thinks he is not getting enough credit for his work. The senior staff engineer’s manager has been crediting him for the work the less senior engineer had done, since the less senior engineer is no longer at that team, in forums where the less senior engineer has no access to.

The less senior engineer is plenty likeable. As is the senior staff engineer. But the less senior engineer had left that team, and the senior staff engineer and his manager are unscrupulous, and do what they’d like to their advantage.

This is called “just world syndrome.”

Just write a press release and let the tech press publish it. Don't host it yourself. The legalistic language belongs in a filing, not a user-facing blog.

I'm suspecting that once these models hit 'good enough' for ~90% of users and use cases, the providers started optimizing for cost instead of quality, but still benchmark and advertise for quality.

I think for some who are excited about AI programming, they're happy they can build a lot more things. I think for others, they're excited they can build the same amount of things, but with a lot less thinking. The agent and their code reviewers can do the thinking for them.

It's rhetoric. You're making it about manipulation. Should the world consist of bloodless lists of facts without significance or commentary?

The submitter should understand how it works and be able to 'own' and review modifications to it. That's cognitive work submitters ipso facto don't do by offloading the understanding to an LLM. That's the actual hard work reviewers and future programmers have to do instead.

In my mind, they're hardly making any money compared to how much they're spending, and are relying on future modeling and efficiency gains to be able to reduce their costs but are pursuing user growth and engagement almost fully -- the more queries they get, the more data they get, the bigger a data moat they can build.

It's like reading the solution to a math proof instead of proving it yourself. Or writing a summary of a book compared to reading one. The effort towards seeing the design space and choosing a particular solution doesn't exist; you only see the result, not the other ways it could've been. You don't get a feedback loop to learn from either, since that'll be AI generated too.

It's true there's nothing stopping someone from going back and trying to solve it themselves to get the same kind of learning, but learning the bugfix (or whatever change) by studying it once in place just isn't the same.

And things don't work like that in practice any more than things like "we'll add tests later" end up being followed through with with any regularity. If you fix a bug, the next thing for you to do is to fix another bug, or build another feature, write another doc, etc., not dwell on work that was already 'done'.