HN user
never stop questioning.
Blog: fastpaced.com
I really appreciated his coding-style, but the bar is quite low on research/ML-algorithms to be fair. I still wonder how he managed to get „trending“ repositories regularly despite the repositories being empty.
What was happening with this account? I was often seeing popular but empty (only title of the paper and maybe a short readme) repositories that were created directly after a paper was published?
Do you have a link to share? Would be highly interested!
I would say uv provides quite a lot of additional features that can be used in various ways to create plain-old venvs. Note, however that uv-pack can also pack a subset of your uv-monorepo for a specific package (there are still some quirks I have to admit..).
My experience was that it‘s surprisingly painful to „just copy“ a venv and especially a uv-created venv. There are a lot of paths to be modified to get the venv working. Copying a venv felt hacky and wrong, that‘s why I built the tool :)
It's kind of ironic that BMW pushed CATL into the EV market [1].
[1] https://cleantechnica.com/2022/06/30/how-herbert-diess-zeng-...
Why would you say useless? They hopefully make a couple of good decisions. Three good decisions a day [1], maybe?
I am using runtime type and shape checking and wrote a tiny library to merge both into a single typecheck decorator [1]. It‘s not perfect, but I haven‘t found a better approach yet.
Maybe living the blue zone [1] lifestyle?
There appears to be (almost) no true competition in healthcare, therefore no real incentives to improve productivity. Wages in healthcare rise disproportionately without productivity gains (Baumols disease); why invest in digitalization?
The key insight is that smaller, well-bounded changes are exponentially easier to review thoroughly.
I am not sure if that is the real insight. It appears to me that most people prefer small, well-bounded changes, but it's quite tricky to break down large tasks into small but meaningful changes, isn't it? To me, that appears to be the key.
That's basically where I am coming from :). I know about my addiction.
Working in the ML field, I can't hate Python. But the type system (pre-3.12, of course) cost me a lot of nerves. Hoping for a better post-3.12 experience once all libraries are usable in 3.12+. After that experience, I’ve come to truly appreciate TypeScript’s type system. Never thought I’d say that.
That‘s actually quite similar to the nearness library [1]. The main difference appears to be vicinity‘s focus on simplicity while nearness tries to expose most of the functionality of the underlying backends.
That's the point I was trying to make, which is also why I brought up 'pseudo-local' development environments in the article.
Maybe you refer to [1]? These are the two options I like to show students who first get in touch with data visualization.
[1] https://www.data-to-viz.com/
Note: In the about page, they provide an extensive list of historical chart classification schemes :).
ChatGPT answer: There are *two* "R"s in the word "strawberry."
There are libraries that support shape checking and I‘ve written a package to combine beartype’s runtime typechecks with jaxtyping‘s shapechecks, see https://github.com/davnn/safecheck
Once accustomed to shape checking it‘s quite a boost in productivity for us, no more fiddling around with invalid dimensions.
Is 5k an appropriate amount for such a finding? Sounds incredibly cheap for such a large organization. How much would something like this be worth on the black market?
To me it seems like it should be a top priority.
Maybe most people are content with children, the current state of the art against aging communities?
Not yet, I believe. Revenue from iPhone sales is still quite fundamental to Apple‘s success, it‘s more than triple the revenue from all services combined (not including Google‘s search engine deal).
Python as a glue language (as it‘s mostly used in data intensive applications) for something like MPI should not add too much overhead?
Can you expand on your usage of AI tech?
I would be highly surprised if LLMs would NOT be used as individual math tutors. In fact ChatGPT would likely already be very helpful.
Nobody uses WordPad, nobody wants to improve WordPad, WordPad will be sunset.
At least in the German-speaking market there exist said solutions, but they are not very common (yet?). I believe keeping 4L at near boiling temperature is still less efficient than individually heating and it takes less than a minute with 3kW.
A meta model of the real world using the Resource Description Format (RDF) and the Terse RDF Triple Language syntax (TTL), together with validation rules in the Shapes Constraint Language (SHACL).
It probably is from the perspective of an information theorist. Did you read any interesting articles on the connections between deep learning and information theory to come to this conclusion? I‘m highly interested in this space, but the influence of information theory on deep learning developments appears to be negligible.
Do you mean that information theory in general is essential in working with ML systems, or a specific point raised by Shannon?
Do you see any negative aspects in day to day life since the data is published?
They were betting on web technologies instead of native, which was quoted as early Mark Zuckerberg's biggest mistake [1].
[1] https://techcrunch.com/2012/09/11/mark-zuckerberg-our-bigges...