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Tux Machines
Posted by Roy Schestowitz on Dec 19, 2022
> While the internet is flooding with mentions of what ChatGPT failed to do, what it can do is surprisingly advanced. Below are two such examples. It is not an argument that validates (or invalidates) abilities of the AI.
> Couple of decades back, the thought of computer's ability to identify people was highly futuristic. Today it has become a part of life for many. The progress of AI is perhaps exponential. We are trying to build AIs whose intelligence could eb comparable to that of humans. Recently, OpenAI opened up their latest AI powered Natural Language processing model ChatGPT for a short duration. While we acknowledged its abilities, we also pointed out the failures, reminding us that AI systems are still far away from becoming human like.
> Docker Extensions is a new beta feature in Docker Desktop. It allows you to run third-party tools in Docker Desktop, complete with a simple React-based GUI. For example, you might run the Tailscale extension, which sets up a Tailscale node inside Docker for your tailnet. Or you might run one of the many GUI dashboards from the Marketplace. Of course, there's nothing new about Docker Extensions in terms of functionality – nothing is stopping you from passing through an API token to a Docker container that's running Tailscale. Or running a Kubernetes pod with the right configuration for a local daemon-like service. But for local development, do you want to manage those extra resources yourself? Sometimes it's nicer to break outside the terminal configuration (vs. environment variables) and dashboards (vs. text or web-hosted).
> How to arrange training and testing datasets in R, To divide a data frame into training and test sets for model construction in R, use the createDataPartition() function from the caret package.
> The project I am working on involves several different reports, each at least 30 pages, and each with about 20 plots and 20 tables per document.
> As well as a myriad of functions, I had 7 very large R scripts doing the data munging and processing.
> I thought they were well ordered, but I had to burn everything down a couple of times and it was quite nerve wracking building it back up. The thought of adding additional phases of the project to this code base made me uncomfortable. I decided I needed to learn{targets} to ensure this project can be reproducible a few years down the line.
> Here are some notes on getting started with {targets}.
> The project I am working on involves several different reports, each at least 30 pages, and each with about 20 plots and 20 tables per document.
> Every year or so, I revisit the current best practices for Python packaging. This was my summary for 2021 – here’s the update for 2022.
> [...]
> pyproject.toml finally got mature enough to replace setup.py and setup.cfg in most cases. Recent versions of setuptools and pip now fully support pyproject.toml and even PyPA’s packaging tutorial completely switched their example project from away setup.py towards pyproject.toml, making it an official recommendation.
> So, now you can replace your setup.py with pyproject.toml. If you had already some kind of declarative configuration in setup.cfg you can move that as well into pyproject.toml. Most tools, like pypy or pytest also support configuration in pyproject.toml (flake8 being a notable exception…) so there’s no reason to keep setup.cfg around anymore. Actually, if you migrate to pyproject.toml it is best to do it properly and remove setup.py and setup.cfg as setuptools behaves a bit buggy when building a package that has either of them and the pyproject.toml.
> Option -W can be used to write all files to disk. The only accepted value for -W is vir (for the moment). When this option is provided, all files are written to the local disk (ignoring contained paths) with their original name, and appended extension .vir.
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