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Programming Leftovers
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[Old] Julia Programming Language ☛ Preface - Julia Data Science
There are many programming languages and each and every one of them has its strengths and weaknesses. Some languages are very quick, but verbose. Other languages are very easy to write in, but slow. This is known as the two-language problem and Julia aims at circumventing this problem. Even though all three of us come from different fields, we all found the Julia language more effective for our research than languages that we’ve used before. We discuss some of our arguments in Section 2. However, compared to other languages, Julia is one of the newest languages around. This means that the ecosystem around the language is sometimes difficult to navigate through. It’s difficult to figure out where to start and how all the different packages fit together. That is why we decided to create this book! We wanted to make it easier for researchers, and especially our colleagues, to start using this awesome language.
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Sean Conner ☛ I've just implemented a Forth system
I've had a fascination with Forth since college. I have a copy of both the first edition and the second edition of Starting Forth. I have a copy of Thinking Forth I also have a copy of Threaded Interpretive Languages with the Robert Tinney cover art. I even wrote my own Forth-like langauge in college, which I used for a class project and a few work-relelated programs.
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Julia Programming Language ☛ This Month in Julia World
Please feel free to post below with your own interesting finds, or in-depth explanations, or questions about these developments.
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Senthil Thyagarajan ☛ Benchmarks vs. Baselines: When 'Above Average' Isn't Good Enough
It always starts the same way. A campaign wraps, and the team pulls up the performance report. The click-through rate? Above benchmark. CPM? 30% lower than the platform average. There’s a general sense of relief, maybe even celebration. Everyone’s nodding. Job well done. But then someone asks the one question that doesn’t show up on the dashboard: “Did it actually drive anything?”
That’s when things get murky.
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Ted Unangst ☛ modern software 2025 edition
Back in the olden times, software was hard to build and hard to use, but remarkable improvements have been made, and entire ecosystems of ergonomic languages are now available. I happen to think the old ways still have some merit, but don’t want to spend all my time staring at the cave walls.
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Frederico Bittencourt ☛ Fun with Futex: building my own mutex in C for funtex
Implementing an optimized lock in Linux requires some Operating System help. You can only get so far by doing everything in user-land. We are going to take a look how one can implement a simple spin lock in C (just like the spin lock in Go I implemented a while back) and then a slightly more elaborate lock using operating system’s primitives. The idea behind a mutex (mutual exclusion) is straightforward: we want some part of the code to be accessed only by a single thread at a time. If the resource a thread is trying to access (the critical zone) is already being accessed by something else: wait. The trick about implementing them is how you to do wait part!
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Rlang ☛ Repost: The Modern R Stack for Production AI
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Rlang ☛ Containerizing Shiny Apps with {shiny2docker}: A Step-by-Step Guide
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Rlang ☛ Benchmarks vs. Baselines: When ‘Above Average’ Isn’t Good Enough
Why relying on external benchmarks in media analytics can be misleading, and how building your own baselines leads to smarter, more context-aware decision making.
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Rlang ☛ Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)