Happy Wednesday!
Here is issue #94 of our newsletter with news and the best tools for your current or future Rails projects…
1. 🔥 The first three parts of a 5-part series on Machine Learning are live. These articles will explore machine learning principles, specifically binary classifiers, and show examples in Ruby. The emphasis will be on decision trees, particularly CART (Classification And Regression Trees), with a brief exploration of the underlying mathematics. Dive in!
- Machine Learning: An Introduction to Gradient Boosting 🚀
- Machine Learning: An Introduction to Random Forests 🌳🌳🌳
- Machine Learning: An Introduction to CART Decision Trees in Ruby 🌲
💡 Whether you have a feature idea that Machine Learning could help bring to life or a problem that Machine Learning could help solve, we are here to help!
2. ⏳ In this article, Gelsey details How To Delay A Rails Upgrade by outlining steps you can take in your development process, allowing you to postpone the Rails upgrade while still ensuring a smooth transition once the timing is optimal for you to begin your upgrade.
3. 🏋🏽 A great resource by Rishi with techniques to help boost your Rails performance and help you identify many of the common culprits weighing down your performance.
Tired of reading blog posts, FAQs, and ChatGPT answers that don’t answer your questions? Schedule a 45' call with our CTO 🚀
Whether you want to talk shop, technical debt, legacy code, or Ruby/Rails upgrades, Ernesto has some availability to answer these questions.
📞 Schedule a 45' call with FastRuby.io CTO, Ernesto Tagwerker
4. 🤔 This Interesting article from Radan is geared toward helping you decide if you should use Hotwire or a Frontend framework on your next Rails project.
5. 💎 RorVsWild shares their Ruby on Rails load testing experience that they have collected over years in this industry, sharing habits and techniques to aid you along the way.
6. ⏩ Github enabled YJIT for 15-25% and improved request latency.
Bookmark them, share them, or save them. We hope you found these links helpful ;)
Best,
The FastRuby.io Team
