twitchard

although I'm unwilling to hook it ip to my main google account, I had it make its own. If you're willing to give it the keys to the kingdom maybe you get faster benefits.

I used it to try and buy a gift for my mom and Capital One froze my credit card.

I used it to try and work with my Obsidian and my GitHub but mostly I ran into snags with its permission model, basically there's no way for it to do anything useful here because it gets all weird about credentials.

I did eventually use it to successfully make some purchases but mostly I think it has been more of a time suck than a time saver.

If rule-based highlighting of syntax is meaningfully helpful for reading code then how come nobody uses rule-based syntax highlighting of prose?

Why don't we visually emphasize the verb in a sentence and deemphasize articles and prepositions, for example?

In prose the author can *bold* or italicize text to emphasize ad-hoc, or make ## headings for scanning. Why can't we do such ad-hoc emphases in code?

where's the thesis statement

Lol

404 This page could not be found.

Clearly they did delete it.

Not sure DORA is that much of an indictment. For "Change Failure Rate" for instance these are subject to tradeoffs. Organizations likely have a tolerance level for Change Failure Rate. If changes are failing too often they slow down and invest. If changes aren't failing that much they speed up -- and so saying "change failure rate hasn't decreased, obviously AI must not be working" is a little silly.

"Change Lead Time" I would expect to have sped up although I can tell stories for why AI-assisted coding would have an indeterminate effect here too. Right now at a lot of orgs, the bottle neck is the review process because AI is so good at producing complete draft PRs quickly. Because reviews are scarce (not just reviews but also manual testing passes are scarce) this creates an incentive ironically to group changes into larger batches. So the definition of what a "change" is has grown too.

Re: model Supremacy -- I think there's so much cross-pollination between the AI labs that this is unlikely. Everybody at these labs basically know what everybody at the other labs are doing. *data* can be hard to get but I don't think acquiring good data is positive feedback loop-y where whoever wins just skyrockets away from everybody else.

Model stagnation, I thought stagnation was coming last year, then Opus 4.5 came out. I think maybe the models are slowing at getting *smarter* per se, but they are still getting better at coding. And even if they stopped getting better at coding, if they got as good as they have gotten at coding on other fields of inquiry (like, say, writing) so I think we've got a ways to go yet before the progress in terms of economic usefulness slows down.

I don't think 10 minutes of AI experience is enough to be a master? AI-assisted software engineering is a skill that I want to get better at, just like traditional software engineering is.