To state the obvious, there are many LEDs, an ATmega chip, and there are the beginnings of a charlieplexed arrangement... It still has a long way to go, though!
(Addendum: Follow along my thought process below with this extra pedagogical material: https://www.slideshare.net/StephenWylie3/process-of-arcade-rom-hacking ) An interesting work project, right? I was invited to take my Giant NES Controller to a recruiting event for work taking place at a local brewery. But you can't just have an NES controller without anything to play, right? Thus, I pitched a couple ideas on custom games to go along with the giant controller since I didn't want participants playing anything standard either. The thing they agreed to was to feature the Tapper arcade game, by Bally/Midway in 1983, but modified to show the brewery's logo instead of being Budweiser-branded like it was originally. My handiwork, about 80% done, and with a tiny glitch. Can you spot the remaining issues? Now you might be thinking Tapper wasn't ever ported to NES, and that's correct. However, with the help of the MAME arcade emulator and a Hyperkin USB-&
Are you looking to find the one simple command that can kick off a pipeline from another pipeline in Azure DevOps? You may have found a lot of annoying restrictions using straight YAML because so many things have to be known ahead-of-time (i.e. set using compile-time variables). This prevents you from doing such things as conditionally running pipelines, utilizing arrays defined at runtime as parameters into many ADO tasks/commands, or performing manipulations on runtime data for use in subsequent commands. You can't even kick off a pipeline without the previous one finishing (and invoking a pipeline trigger ). Follow along as I explore a scripted solution to solve my problem. My Use Case I want to provide fresh Databricks images on a private Azure Container Registry (ACR). This way, data scientists are not confounded by changes to Databricks runtimes when their clusters start and stop, since changes can prevent their package & library installation scripts from running successf
Software developers have taken note of the efficacy of large language models such as ChatGPT towards automating particular development tasks. Depending on the language in use and what is to be accomplished, LLMs can provide a concise answer that circumvents the shortcomings of documentation that is either incomplete or tries to "boil the ocean." LLMs work best at generating code when the problem is well-defined and the language is fairly static and consistent. For instance, I have had a great deal of success when asking for code to perform basic operations on Amazon DynamoDB in the Node.js SDK 3. It was a bit more difficult to have it write infrastructure as code for AWS using the CDK framework in Python 3, but it still produced decent enough code that I at least knew where to start in looking at lengthy documentation. However, writing native mobile code such as Kotlin for Android or Swift for iOS yields some rather incomprehensible and outdated results. Besides ChatGPT, ther
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