In what way does this or anything else Trump has done or indicated to do advance the state of education towards the goals of the reform you are talking about?
I also don't think I claimed that "Trump has done or indicated to do advance the state of education". His administration has addressed grievances that I agree with but they have not introduced the positive reforms that I would support.
Oh I see - did you reply to the correct comment initially?
So far Trump's administration has seemed to address perceived grievances that they have with the university administration. In the comment you replied to I outlined some positive reforms that I would personally like to see as someone who cares deeply about education and wants to see a successful system. Trump's administration hasn't and probably won't make progress in this direction. In other words they are saying "don't do X" and aren't saying "Do Y". I approve of their progress on the former and think they are unlikely to make progress on the latter.
Nobody will watch anyone's "creations". The TV or whatever device watch on will observe what you and everyone else engage with and how you interact with it and create content for you on the fly, just like Instagram and TikTok's feeds do now.
AI creatives can enjoy the brief blip in time where they might get someone else to watch what they've created before their skills become obsolete in an exponentially faster rate just like everyone else's.
Then everyone can just get their own personal movies and infinite content stream. Honestly people would probably like that given how atomized society has become.
> Often times, "Stream vs. Batch" is discussed as if it’s one or the other, but to me this does not make that much sense really.
Just seems like a flawed premise to me since lambda architecture is the context in which streaming for data processing is frequently introduced. The batch vs stream discussion is more about the implementation side - tools or techniques best used for one aren’t best suited for the other since batch processing is usually optimized for throughput and streaming is usually optimized for latency. For example vectorization is useful for the former and code generation is useful for the latter.
The tooling is pretty easy to learn if scary looking at first and becoming a JVM expert, at least in the context of GC tuning, might take you a week or less. Some of the defaults are surprising though.
And I think there is some parallel with the kernel vs GC and mmap vs buffer pools - the GC simply has better context in the scope of the application. With other processes in the picture, though, yeah there is some provisioning complexity there.
My school started us off in microcontroller programming in C then Java for intro to data structures and OOP then back to C (and MIPS assembly) for systems/OS/concurrency. One thing I appreciate Java over Python for DS/Algo is the clear delineation between abstract data type and underlying data structure - IMO is easy to get a wrong mental model with Python. But teaching OS concepts in Java seems a little crazy to me.
Having a hard time finding it now, but someone put together a benchmark with two categories - naive and optimized - comparing implementations across languages with a workload vaguely resembling a real-world business application server with a mix of long and short lived objects. Java was at the top of the naive benchmark by a landslide and behind C and C++ (edit: and probably Rust) for the optimized ranking, but with a gap before the rest of the field.
With the JVM you basically outsource all the work you need to do in C/C++ to optimize memory management and a typical developer is going to have a hell of a time beating it for non-trivial, heterogenous workloads. The main disadvantage (at least as I understand) is the memory overhead that Java objects incur which prevent it from being fully optimized the way you can with C/C++.
