word2vec VS Tailwind CSS

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The classifier is something I've been struggling with, because the Naive Bayes classifier is very weak when it comes to short phrases. I've been experimenting with word2vec and golearn to produce a more powerful and customizable classifier.

Lastly, Tailwind CSS is a utility-first CSS framework packed with classes like flex, pt-4, text-center, and rotate-90 that can be composed to build any design, directly in your markup.

Tailwind CSS

Sidekiq is already configured along with assets, tailwindsCSS.

Finally, for our front end, we’re going to be pairing Next.js with the great combination of TailwindCSS and shadcn/ui so we can focus on building the functionality of the app and let them handle making it look awesome!

You can use any frontend framework you want — react-based tooling, however, has a natural advantage as it models everything as a function of state, which can map 1:1 with the concept in Burr. In the demo app we use react, react-query, and tailwind, but we’ll be skipping over this largely (it is not central to the purpose of the post).

Tailwind CSS: A utility-first CSS framework for rapidly building custom designs.

First, you need to make sure that you have a working Tailwind CSS project…

Tailwind CSS

With better CSS approaches like TailwindCSS and Vanilla Extract (which we're heavily using) it's much easier to maintain the UI and make sure it doesn't change unexpectedly. No more conflicting CSS classes, much less CSS specificity issues and much less CSS code in general.