Cluster environment with virtualized cores may cause slower performance of Julia's parallel code. People recommend Threadpinnig.jl to solve the issues.
Yeah, Julia's REPL deserves special attention as it allows to do the package management (by pressing "]"), look for the functions help ("?") and do shell operations (";") without leaving it.
Julia is not an alternative to Go. It is the alternative to Python (slow) and to C++ (hard and complex). Go is fast and simple but doesn't have abstractions to create complex code required by math libraries.
For newcomers my main tip is to use views for array slices (for example, via the @view and @views macros), since by default Julia creates copies of slices. Broadcast operation (the dot syntax) is another super useful instrument for speeding up vectorized operations. You can write fast GPU code with them without resorting to GPU kernels, in some cases. Although GPU kernel programming via CUDA.jl is convenient in Julia, once you get used to it.
Yeah, the title may suggest that productivity is still 10% out of 100% after CEOs fired half of developers believing that the rest will do all the job with the help of AI.
Somebody knows how a part of a complex system works. We can't say this for complex systems created with AI. This is a road into the abyss. The article is making it worse by downplaying the issue.
I swear, every fascist has the same playbook. They use the same phrases, same accusations, same lies, sometimes even same wordings. It is like they have a single hive mind - for which everyone else is the enemy and is subject to destruction or enslaving.
The Epstein files are revealing a web of surprising connections. Epstein's network knew about 9/11 and made stock trades beforehand. He was personally involved in some UK corruption to lower taxes on bank executives.
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