This summer, with the help of AI, I found an inconsistency in the way Postgres handles timestamp vs. timestamptz comparisons under a DST spring-forward gap for the datetime_ops btree family [0]. Essentially there are scenarios where expression B > A and B < C, but also C = A, which can cause queries using a btree index (among other things) to return an incorrect result.
The assessment in the mailing list was that this was a bug, but there were no good ways to fix it.
> Backpatching a behavioral change like this seems awfully scary.
For the moment I'm just contemplating what we could potentially
change in master. So far I don't like any of the choices :-(
I don't know, this feels kind of overly judgy. It's pretty healthy and normal to have some goals in life and even to try to do something impressive. The fact that he is trying hard to balance this with caring for his family seems pretty reasonable to me.
Interestingly, the case you linked was a purely civil action. However, in 2016, the DOJ announced that in the future, it would prosecute similar cases criminally. [0][1] Take that for what you will!
I think that the drive and energy to build one's own business (even if the business itself is very banal, like a laundromat or app-builder) is impressive and counts as ambitious. It's not easy. I tried starting a business and couldn't hack it.
Unfortunately, I think that this is possible, but it requires a superhuman well of energy, sleep deficit tolerance, and benevolence that very very few people possess. :/
The reality is that it's probably a skill issue. Scaling a mature platform 10x can be a really, really hard problem, they obviously don't have their arms around a solution, and are probably spending a majority of their time on ops to keep the bleeding down. In the old days you would have Jeff Dean come down from heaven and invent a new database for you or something like that. It doesn't really seem like Github has that kind of technical ability, so they're probably trying to cobble together Azure ops with internal bandaids while everyone internally is cranking out AI code and it's just not going to be enough.
The labs are all very interested in bio right now. Demis is working on Isomorphic rn (Google's version of that) but I could imagine a lab tempting him away to work on their equivalent if it had stronger momentum
Reward hacking will continue to be a problem. For sufficiently astonishing AI-created results, we must remember to ask if AI has not instead found it easier to elaborately fool us.
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.