77 karma · joined August 11, 2010
GPT 6.1 X > GPT 6.0 X GPT Y Astra > GPT Y Sol > GPT Y Terra > GPT Y Luna
This order is very important.
We need students to work together and share their experiences. Then we need each one to achieve the learning goals (and we should not assess work they did far from our eyes. We should assess their understanding and skills in front of us).
We have designed education around exams and evaluation. If I design an exercise that does not contribute to their final grade, students don’t even look at it. If I design a 30min tutorial on a programming subject, students will not look at it and will spend 8 hours on the (graded) assignment that was designed for 1 hour on top of the 30 minute tutorial.
I am making some changes: evaluation will be as randomly as possible to minimize overfitting to our exercises. Draw from real world if possible.
I would also like to get rid of bologna’s modular structure and go to the UK’s one-exam-at-the-end-of-the-degree model.
But overall, we need more passionate students. Those don’t care about all this cheating and would appreciate that professors and future employers see their effort and success.
Asking honestly, because none of the ones I use is.
These restrictions make it possible to send the sub typing check to an SMT solver, and get the result in a reasonable amount of time.
In Lean (and I believe Rocq as well), the Type of Int is Type 0, the type of Type 0 is Type 1, and so on (called universes).
They all come from this restriction.
Instead, he would have put motion/light sensors on the screen, so it would automatically wake up when you are sitting in front of it. Macs don't shutdown, they just go to sleep and wake up when you need them.
That's where the comparison to Python comes from: getting speedup on slow interpreters is not very _relevant_. Now if your interpreter has the same optimisations as Python (or v8 or JVM), even a small fraction of what you show would be impressive.
Having said this, the work your team did is a really challenging engineering feat (and with lot more potential). But I do not believe the current speedups will hold if the interpreter/compilers have the level of optimisation that exist in other languages. And while you do not claim it, people expect that.
(disclaimer: I really like most of his ruby stuff)
I mostly follow Dilbert and Questionable Content. I used to follow Wizard of Id and a few more, but they also lost support for RSS.
Also notice that in Europe you can get a bachelor and masters in 4-5 years (3+1 or 2), which can be the length of a US undergrad degree.
Also, NVidia and Cuda drivers mess the upgrade path as well. And ubuntu is the most supported distro.
Kotlin was Google's backup if the suit went Oracle's way.
[0] https://en.wikipedia.org/wiki/Google_LLC_v._Oracle_America,_....
That said, it’s the author’s prerogative.
https://wiki.alcidesfonseca.com/blog/online-boardgames-platf...
Boardgamearena and yucata have the largest collection and the best user interface in 2D. Tabletopia and tabletopsimulator are 3D and (mostly) paid.
The runtime can be added via a jar file, but lambda-based operations must be converted to OpenCL/Cuda/PTX/LLVM or other low-level GPGPU language.
Aparapi did the latter using runtime byte code instrumentation. TornadoVM also does the same thing as a JDK compiler plugin. AeminiumGPU did the same using a transpiler [0] before the actual Java compilation step.
Recursion is now supported in CUDA via dynamic parallelism (or faking the stack) but it is not performant at all.
The ideal language for GPU programming is closer to Julia or a MATLAB-like language where arrays and matrices are first-class.
Does webapps and mobile as well