It is proven by science that there is no such thing as an intuitive fear of snakes. Its 100% cultural. Toddlers don't fear snakes for example.

The best system I ever worked with looked incredibly simple. Small, clear functions. Lots of "set a few variables, run some if statements." Incredibly unassuming, humble code. But it handled 10s of millions of transactions per day elegantly and correctly. Every weird edge case or subtle concurrency bug or whatever else you could think of had been squeezed out of the system. Everything fit together like LEGO blocks, seamlessly coming together into a comprehensible, functional, performant system. I loved it. After years of accepting mediocre code as the cost of doing business, seeing this thing in a corporate environment inspired me to fall in love with software again and commit to always doing my best to write high quality code.

EDIT: I think what made that code so good is that there was absolutely nothing unnecessary in the whole system. Every variable, every function, every class was absolutely necessary to deliver the required functionality or to ensure some technical constraint was respected. Everything in that system belonged, and nothing didn't.

Apple has an architectural license that lets them build their own ARM cores:

https://www.electronicsweekly.com/news/business/finance/arm-...

It is very unlikely Apple uses anything from ARM’s core designs, since that would require paying an additional license fee and Apple was able to design superior cores using its architectural license.

There's also no evidence that it won't, so your opinion carries exactly the same weight as theirs.

> Progress in AI has always been a step function.

There's decisively no evidence of that, since whatever measure you use to rate "progress in AI" is bound to be entirely subjective, especially with such a broad statement.

I dunno, first time I tried an LLM I was getting so annoyed because I just wanted it to go through a css file and replace all colours with variables defined in root, and it kept missing stuff and spinning and I was getting so frustrated. Then a friend told me I should instead just ask it to write a script which accomplishes that goal, and it did it perfectly in one prompt, then ran it for me, and also wrote another script to check it hadn’t missed any and ran that.

At no point when it was getting f stuck initially did it suggest another approach, or complain that it was outside its context window even though it was.

This is a perfect example of “knowing how to use an LLM” taking it from useless to useful.

> Having spent a couple of weeks on Claude Code recently, I arrived to the conclusion that the net value for me from agentic AI is actually negative.

> For me it’s meant a huge increase in productivity, at least 3X.

How do we reconcile these two comments? I think that's a core question of the industry right now.

My take, as a CTO, is this: we're giving people new tools, and very little training on the techniques that make those tools effective.

It's sort of like we're dropping trucks and airplanes on a generation that only knows walking and bicycles.

If you've never driven a truck before, you're going to crash a few times. Then it's easy to say "See, I told you, this new fangled truck is rubbish."

Those who practice with the truck are going to get the hang of it, and figure out two things:

1. How to drive the truck effectively, and

2. When NOT to use the truck... when talking or the bike is actually the better way to go.

We need to shift the conversation to techniques, and away from the tools. Until we do that, we're going to be forever comparing apples to oranges and talking around each other.

So about 2x as big as a normal North American beaver.

They're remarkable animals. The scale of impact of their dam-building on ecosystems is only just starting to be appreciated.

Having worked extensively at several companies in the 1990s trying to bring optical measurement of objects into production environments, I assure you that a lot of that bumpiness turns out to be artifacts of the measurement process.

As just one example, there's really no laser-based measuring device in the world, even today, that can rapidly measure a surface near (<1mm) the edge of an object. Something that is trivial to do with ruby-ball touch sensors...