Git Kraken seems to be popular but I couldn't get used to it myself, start with whichever appeals to you the most and change if you didn't like it. pdb.meta.Īgain very opinionated thing, I've used SourceTree for a long time and recently switched to using Tower which I'm liking so far. No, This will only exclude the meta files that ends with ta and. If you want more info about how gitignore works then I suggest reading the answers here What is. I usually use to generate gitignore files, you can add your IDE too and it'll combine the gitignore for both. In this post from reddit it's suggested to use the. That depends on your needs and usage, but switching from a service into another is not a hard task so just pick whichever you feel comfortable with and use that, or try whichever one since both Bitbucket and GitLab are pretty good and you are looking for the free tiers anyways. What's the best repository hosting service for Unity right now? This is a very opinionated question, but I'll try to answer some of the questions. How important is this? Should I really care? What about Sourcetree? Also I've heard that Gitkraken has gotten a lot better recently, can somebody confirm if it works for bigger projects?Īlso this point form the post: When you add something to gitignore, it doesn't ignore the associated meta file. It's also recommended to use SmartGit instead of GitKraken and Github desktop. gitignore file contains these lines: # Unit圓D generated meta files gitignore file from github but also that When you add something to gitignore, it doesn't ignore the associated meta file. I've heard that many people recommend Gitlab as well, but I can't figure out what the specific limits for repo/LFS are and how expensive additional storage is. What's the best repository hosting service for Unity right now? I thought about using Bitbucket since it has 2GB hard limit for repo size and 10GB for LFS for free. There are a few things I'm not sure about: Overall, I think this is a good book to balance your thinking if you are a tech practitioner like me, but it might be even more valuable to managers who are being thrust into managing implementation of AI and need to quickly come up to speed and, importantly, understand risks.I'm starting to work on a project and need to figure out what's the best way to do version control right now. We must, therefore, find transparent ways to measure and instrument ethics, especially as AI becomes more integrated into our daily lives and society at large." I believe we do have a large deficit in methods to measure the impact of AI. Importantly, he notes "But here’s the challenge when it comes to discussing AI ethics – if we can’t measure it, we can’t monitor it, judge it, or improve it. It's timely given the recent concerns raised at high levels about the dangers of AI. This book is wide ranging including some descriptions of the technology, but also focusing on ethics, cost-benefit analyses, critical thinking, and understanding unintended consequences. In a way, I think this book complements the goals of Andrew Ng (to bring AI/ML into the hands of millions), in that he offers the "Citizens of Data Science Mandate"-to wit "Ensuring that everyone, of every age and every background, has access to the education necessary to flourish in an age where economic growth and personal development opportunities are driven by AI and data." Bill is an author of several books in multiple languages, and educator, and promoter of data literacy. I was given a chance to review "AI & Data Literacy" by Bill Schmarzo (available here: ). On Crashing the Barrier of Meaning in Artificial Intelligence Babies seem to selectively pay more attention to these edge cases than to normal language, and to readily generalize from them." Studies have shown that Motherese does not consist of independently distributed samples of phonemes, but rather pushes extremes phonemes that are close to the phonetic boundary. The example is given of "Motherese: the language samples that mothers (unconsciously) target to their babies. An interesting section is on the fundamental framing of "the learning problem", pointing to the ubiquitous requirement of IID (independent and identically distributed) data. It's a short read and you can ask yourself along the way "would they have concluded differently if this was post-LLM?". It's a beautiful time capsule-the workshop and the publication pre-date the current LLM frenzy. The linked paper summarizes a 2018 workshop at the Santa Fe Institute regarding "On Crashing the Barrier of Meaning in Artificial Intelligence".
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