jit | JavaScript InfoVis Toolkit provides tools | Data Visualization library

Edit: This issue is now deprecated since version 3.0.0 of tailwind works with react without having to use CRACO.

While trying to use Tailwind with React in JIT mode the classes that I add have no styles, even after refreshing the page. I have to restart the server for the styles to take effect.

tailwind.config.js:

I am trying to run Oracle db in docker on M1 Mac. I have tried images from both store/oracle/database-enterprise:12.2.0.1-slim and container-registry.oracle.com/database/enterprise:12.2.0.1-slim but getting the same error.

docker run -d -it --name oracle -v $(pwd)/db/oradata:/ORCL store/oracle/database-enterprise:12.2.0.1-slim

I also tried non-slim version and by providing the --platform linux/amd64 to the docker command. Result is same.

Here's the result of docker logs -f oracle

I installed Erlang and Elixir as recommended in the Elixir documentation for Ubuntu & Debian.

The results of running elixir -v:

I've been following Tailwind's tutorials and when. I get to the part of the tutorial where they ask me to run npx tailwindcss-cli build css/tailwind.css -o build/tailwind.css, I get the following error. How do I solve this?

I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (maximum sphere diameter) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by cKDTree.query, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). It is contrary to my expectations about scipy spatial searching speed. So, I tried to use some list-loops instead some numpy lines for speeding up using numba prange. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.

When you are working with secret keys, if your code branches unequally it could reveal bits of the secret keys via side channels. So for some algorithms it should branch uniformly independently of the secret key.

On C/C++/Rust, you can use assembly to be sure that no compiler optimizations will mess with the branching. However, on Java, the situation is difficult. First of all, it does JIT for desktop, and AOT on Android, so there are 2 possibilities for the code to be optimized in an unpredictable way, as JIT and AOT are always changing and can be different for each device. So, how are side channel attacks that take advantage of branching prevented on Java?

I am currently building a component library for my next project with TailwindCss, I just ran into a small issue when working on the Button component.

I'm passing in a prop like 'primary' or 'secondary' that matches a color I've specified in the tailwind.config.js then I want to assign that to the button component using Template literals like so: bg-${color}-500

I have the following 2 query plans for a particular query (second one was obtained by turning seqscan off):

The cost estimate for the second plan is lower than that for the first, however, pg only chooses the second plan if forced to do so (by turning seqscan off).

What could be causing this behaviour?

EDIT: Updating the question with information requested in a comment:

Output for EXPLAIN (ANALYZE, BUFFERS, VERBOSE) for query 1 (seqscan on; does not use index). Also viewable at https://explain.depesz.com/s/cGLY:

I have a numerical routine that I need to run to solve a certain equation, which contains a few nested four loops. I initially wrote this routine into Python, using numba.jit to achieve an acceptable performance. For large system sizes however, this method becomes quite slow, so I have been rewriting the routine into Fortran hoping to achieve a speed-up. However I have found that my Fortran version is much slower than the first version in Python, by a factor of 2-3.

I believe the bottleneck is a linear interpolation function that is called at each innermost loop. In the Python implementation I use numpy.interp, which seems to be pretty fast when combined with numba.jit. In Fortran I wrote my own interpolation function, which reads,