I had the Air Pod Pro 1 for many years, but unfortunately lost in an airport. I upgraded to the Pro 3, but the pods are shaped differently and now fall out of my ears. I didn't even research ear fit, not thinking this could be a regression.
From what I've seen it seems to be a very common opinion that the pro 3 is worse than the pro 1 and 2. Hopefully the new tip design is just a temporary indiscretion like the touch bar.
I get that the data science world has moved on to python, but I always felt that R's data.table had the slickest dataframe developer experience. I have toyed with Polars for a few hours, maybe I should give it a better chance.
The bare R experience is not that great, to put it mildly, but it's a whole other story if you add tidyverse on top of it. The data work becomes really easy then, but I still prefer Python because of familiarity and a better experience & ecosystem when you want to do anything beyond data wrangling & analysis.
I found `polars` to be a better experience than `pandas` even though I'd say it leaks some "Rustisms" in its Python APIs. But LLMs alleviate those pains and it's easy enough to review. I'd say it's even easier when there's less of a chance of implicit behavior.
Why do you say that? Base R is arguably nicer to work with data than pandas is for example. Happy to provide specific examples to prove my point if you want.
my main frustration with pandas was certain idioms did not work efficiently because they escaped the pandas kernel, causing memory copy and other bad behavior.
If your work is more focused on statistics or pure modeling, then I agree R wins hands down. The issue is that most projects have "unclean" parts where you have to gather data from multiple sources, use connectors for services, S3 buckets and whatnot; dealing with that mess is where Python really shines.
AI probably changes the equation to some extent, but I still believe I'd rather maintain a complicated data pipeline like that in Python rather than R.
Agreed. If the R community developed more data pipeline frameworks, following the "tidyverse" way of doing things, R would be my go to choice for all data related work.
Data science is a broad church, but my own branch of it (molecular biology, genomics and epigenetics, bioinformatics) and my partner's (ecology) are still very much R-based and entrenched. If you primarily care about statistical modeling, then R still wins (easily).
That doesn’t justify lots of stuff that R lacks vs Python. Anytime someone new joins my corp, they reluctantly move off of R from their academic days on Python and once in the ecosystem, they never look back
I first learned about R at a python user group meeting. It was when Pandas was new, and we were having a bunch of talks about it. Wes McKinney even came to give one before going to Pycon.
Anyway, the general consensus at the time was that R was much nicer once you had your data, and if all you had to do was transform it. But that everything else was better in Python.
One of our group members did an experimental project, where you could open R inside of python and share memory. So you could theoretically do your API calls and screen scraping and whatnot in Python, then transform your data in R, then take the output and use it to do something else in Python. It was pretty cool, but I think it was just a POC and never really went anywhere.
I tried learning R after that, but didn't get very far with it.
I use rpy2 for that. The scientists wrote the calc engine in R (via my Python transscription of the Excel original :) and I wrote the dashboard and data pipeline in Python. The data pipeline batch process embeds the R interpreter to avoid spawning processes all the time.
The efficient markets hypothesis says the market prices incorporate all available information and there's no such thing as a cheap stock, so there's no easy money to be made by trading.
The math equivalent is presumably that all easy problems have been solved and all open problems should be very very hard. This has turned out not to be the case as LLMs have found simple counterexamples to long held conjectures.
Not really, the incentives for each are not binary values. There are bounties on the 10 millennium problems (that haven't been increased in 25 years), but there isn't much motivation beyond that. You get some street cred and your name in a prestigious journal. That's not-nothing but can't hold a candle to the potential fortunes of the market.
> There are bounties on the 10 millennium problems (that haven't been increased in 25 years) ...
Strictly speaking, if you constructively prove P=NP and the solver has reasonable time complexity, e.g. quadratic, it could make you incredibly rich. If you don't attract the wrong kind of attention from the world's intelligence agencies first...
> That's not-nothing but can't hold a candle to the potential fortunes of the market.
a market is where things are sold and bought. there are lots of markets which aren't as well capitalized as financial markets - your local farmer's market doesn't "hold a candle to the potential fortunes of the market" but it's still a market (and presumably the efficient market hypothesis still applies).
The markets arent efficient without enough participants. This is why the price of gasoline only fluctuates cents between competitors, but vehicle prices fluctuate thousands. It's a very simple concept that you aren't seeing.
The market for solving math problems is tiny compared to selling/ running algos for trading. You solve a pure math problem you might get accolades from some professors.
My first thought as well. drag-and-drop is also fantastically useful for data you cannot Cut (e.g., moving an email attachment from one draft message to another)
Emacs users (myself included) would feel less like cramming every aspect of our work into Emacs if more tools embodied the freedom and hackability that comes with using Emacs. It's not that Emacs is better, it's that other tools are more restrictive and not self-documenting.
It's also helpful that you get a certain degree of UI consistency from things like completing-read being used everywhere. And even when some package departs from convention, everything is still a buffer, so anything in it is easy to reuse.
https://discussions.apple.com/thread/256143886?sortBy=rank
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