shoyer

When will this support "trees" of pull requests, with dependent changes? In my experience with stacked changes (from Google), it is often the case that changes do not stack up as a linear history. I imagine that would especially be the case these days with parallel coding agents.

The short answer is that tracing is way, way easier to implement in a predictable and reliably performant way. This especially matters for distributed computation and automatic differentiation, two areas where JAX shines.

AST parsing via reflection means your ML compiler needs to re-implement all of Python, which is not a small language. This is a lot of work and hard to do well with abstractions that are not designed for those use-cases. (I believe Julia's whole language auto-diff systems struggle for essential the same reason.)

ERA5 covers 1940 to present. That's well before the satellite era (and the earlier data absolutely has more quality issues) but there's nothing from 170 years ago.

Not sure if $199 is reasonably priced in your opinion, but the Nest Wifi Pro supports 6E.

+1 one of most common mistakes for PhD students is picking a project based on research interests rather than the adviser. I believe it is relatively uncommon to speak with former students of an adviser, but this is something that everyone entering a PhD should do.

Finding a good mentor -- someone whose values you agree with and who sets you up for career success -- is far more important than working on any particular topic of interest. The world is full of interesting research topics, and very few PhDs work in the precise area of their PhD research for their entire career.

df.pipe(f, ...) is just syntactic sugar for f(df, ...). Nothing slow about it.

I work on weather prediction, both with traditional simulation methods and machine learning. I have not seen any examples of cellular automata used for useful predictions in this space.

I guess "quantum physicist" can mean either an academic credential or a job title. In my case, I have the former but no longer the later. I finished my PhD nine years ago and no longer work in the field.

My experience was that robo-investors are great until you need something special. Then they can become rather painful.

Exmaple: I got divorced last year. Betterment took weeks of time and many phones calls until they were able to figure out a way to divide our assets evenly, without a large difference in cost basis. Their automatic algorithm for dividing accounts just didn't know how to handle it.

If UBS figures out how to offer a higher level of service on top of robo-advising, that could be a real win.

Most of the "bugs" caught here (including in TensorFlow and in my own project, Xarray) seems to actually be typos in the test suite. This is certainly a good catch (and yes, linters should check for this!), but seems a little oversold to me.

NERSC is part of DOE’s Office of Science. Nuclear weapon development is done by DOE’s National Nuclear Security Administration (NNSA), which sponsors labs like Lawrence Livermore and Los Alamos. It definitely isn’t NERSC, NNSA has its own dedicated supercomputers for weapons work.