simonw

Given that I've shared almost every prompt I've used since I started publishing about this stuff over four years ago I think I've given people a better chance of replicating my results than pretty much anyone else writing about this space.

What did you have trouble replicating?

> Also, it is a different job and it will take some time to adjust. Can you blame them?

Not in the slightest. This stuff is really hard. The changes it makes to our production are disruptive and, in many cases, unwelcome.

From that article:

> The New York Times’ lawsuit, meanwhile, asks everyone to ignore a half century of legal history and progress, and toss aside the longstanding principle of fair use in their own narrow pursuit of an undeserved payday.

The New York Times lawsuit isn't purely about training on content - it's about memorization. They included examples of OpenAI's models being able to reproduce NYT articles entirely from the weights.

There's also stuff about RAG in there - about OpenAI (and Microsoft) features that run web searches and then summarize the results. I've not been following developments closely, I'd be interested to know how that part of the lawsuit is shaking out so far.

The piece says:

> Code spitters are cooked, let's be honest here and accept the fate so everyone can move on.

I'm guessing "code spitters" here means people who consider their job to be manually writing code based on requirements produced by others.

Personally, I think the role of "developer" has already expanded way beyond that. If you're a technology expert who understands what is and is not possible to build, how it can be built, how to prompt that thing into existence, verify that the implementation is good, then get that implementation safely into production, you're not "cooked" at all.

I think they're making 0/month, spending VC.

Their argument seems to be that Fable 5/5.1 are smart enough to avoid being subverted in this way. I'm skeptical!

It this point, a whole lot of teams have been leaning into AI-first, not-entirely-reviewed code since around March this year.

That means we are already 6-8 months in, and not far from having a full year of experience of what that actually looks like, albeit weirdly influenced by the increase in model capabilities during that period.

I'm looking forward to those retrospectives. Did this turn out to work OK for the teams doing it? What were the biggest failings and/or surprises? With hindsight, did this produce good quality software?

I expect Proton's existing employees may be a particularly bad audience for a "let the AI worry about security" message from the top, given that they are more likely to be a) security experts and b) people who care enough about security to actively seek out a job at Proton.

Sadly, I think the label "AI naïve" is better applied to the many experienced professionals who refuse to engage with AI systems as a matter of principle - or who tried out ChatGPT in 2024, saw it make howlingly obvious errors, and wrote off the entire category.

I frequently find myself in conversations with people who are resistant to AI and turn out to have a very skewed idea of what it can and cannot do.

The personal energy usage impact of LLMs remains tiny, even for projects that use millions of tokens. Skip one 20 minute drive and you should be covered for weeks, if not months.

I've recently talked to people at Anthropic who are very confident that they have effectively solved the prompt injection risks that cause lethal trifecta attacks with their latest models.

The open challenge is how to prove it!