Holy shit. Opus 5 found a major superuser privilege escalation CVE for me in a major open source software project within a few minutes of plugging it into my private harness. This is a zero day. WTF. WTF.
My entire office of nerdy engineers has been gathering 'round to watch the world cup for an entire month. Judging by this, the world cup does seem to be of some interest to hackers. ;)
Training and serving large models does require increasingly more compute, though. (The Chinese labs have clearly found some massive optimizations, but my point was that you'd think at some point even those optimizations wouldn't be enough to keep up with exponentially increasing model sizes.)
I wonder how the Chinese labs are training a 3 trillion parameter model on what has to be vastly smaller compute resources. If the U.S. compute advantage is persistent, it's hard to imagine that Chinese labs will be able to keep pace forever, as a matter of physics, but... so far they seem to be doing just fine.
Yeah, it's really tempting to try to fundamentally change the way you interface with the world but it's rarely very sustainable. I've found that trying to change my social environment, and also build skills to make specific tasks easier, are more effective options.
The non-determinism is one of the relevant features of this layer of abstraction! And one can learn to validate that the translation is being done properly. Some of the tools you have include writing extremely detailed specs, generating visualizations of the internals of the tool, or (perhaps) reading the code, though that becomes less feasible with volume.
Basically it turns out that code is full of incidental details and what you really want is to verify the important parts, while receiving a guarantee that the vast tail of incidentals is handled "reasonably."
I was quite worried about having to code when I interviewed recently. A two- or three- year layoff is a lot. Turns out that it didn't really make much difference! After a few weeks of warm-up exercises, coding was as natural as ever and turned out to be the easier part of technical assessments. I guess a couple decades of muscle memory is hard to lose.
Now then, back to using Fable. It is doing work that previously took me months in an evening.
I've had years in which most people in my immediate surroundings were sick for weeks or months (likely exacerbated by mold, school, and travel). Also years in which I never really got sick at all.
This seems kind of weirdly confrontational? Elastic was founded by the guy who created elasticsearch. Why shouldn't he make a living selling services around the software he created? This is a terrific success story!
This feels overly cynical. My long-time friend took a job at Meta (over equally compelling financial alternatives) because the manager pitched the team and growth prospects well. (Meta turned out to be quite disappointing on these fronts. I never heard money as an important factor for joining or for leaving.)
In general, the kind of people who get an offer from any particular big tech company probably can get similar money elsewhere, so it's unlikely to be as big a factor as you suggest.
Given how many studies have built-in sampling bias or other surprising assumptions, I still welcome people gut-checking it vs their experience. (Plus, the stories are interesting, right?)
Having worked for a business that made a serious go of running everything out of stored procedures, I have to say that lack of version control was a huge problem and effectively limited all development to a single person who held all the rules in their head.
I mean, we're pretty deep into Westworld/Blade Runner-style scifi at this point. It's actually a crazy, mind-bending question to try to grasp what is going on with chatclaudini at this point. Regardless of what labels we choose or properties we choose to affirm, we're far too deep into uncanny valley for it to be very helpful.
LLMs often seem to have trouble determining the severity of a bug/incident/problem in a vacuum. If you run an LLM over 1000 items in parallel and ask "is this bad," it will come up with reasons for it to be bad way more than it might if it were considering all 1000 at the same time.