puck VS hands-on-train-and-deploy-ml

See the full changelog for all changes via the GitHub release.

If you’re starting from scratch, you can also use one of the Puck recipes to spin up a new project:

⭐ Support us on GitHub by dropping a star

Puck is an embeddable, modular, open-source visual editor for React with built-in support for complex drag-and-drop page building. While GrapesJS provides a barebones page building experience, and tools like Builder.io and Storyblok offer fully-fledged CMS platforms, Puck aims to bridge the gap. It combines an extendable ready to use page editor with a fully decoupled page export model—giving you flexibility without locking you into a specific backend or proprietary ecosystem.

That would be wonderful, but will require some further work. Puck doesn't provide a grid, but it supports user grid implementations.

To support something like you're suggesting would likely require an official Grid component, which I'm now tracking here: https://github.com/measuredco/puck/issues/843

The official next recipe now uses Next 15.1 and React 19 by default. If you're using the recipe and want to upgrade, make sure you also upgrade to React 19 as required by Next 15 when using the App Router. See the official Next.js upgrade guide for more info.

I was using another great library called puck, which is basically a very customisable editor, that can create sites/newsletters/pdfs (something visual) and came to the next step which was turning the output of the editor into a PDF.

We're grateful for the community's support and contributions. Join the conversation on GitHub and Discord.

Hello fellow hackers!

We launched Puck in September [on Hackernews](https://news.ycombinator.com/item?id=37391848) after building it for our clients, and had a wild ride to the front page!

I wanted to share a little update on what's happened since:

1. 3.7k starts [on GitHub](https://github.com/measuredco/puck)!

There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)

And a new (but very promising-looking), free GitHub course from Pau Labarta: - Hands-on Train and Deploy ML

For a higher-level, more conceptual overview, Andrew Ng always has great courses on DeepLearning.ai (and they're free to audit if you don't officially need the certificate): - Machine Learning for Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And a new (but very promising-looking), free GitHub course from Pau Labarta (looks like he's still filming some of the lecture videos, but the rest of the course is all there): - Hands-on Train and Deploy ML

- Hands-on Train and Deploy ML

This is also a pretty promising-looking new course that focuses on deployment and automation. It looks like some of the video lectures are still under construction (like I said it's super new), but the code and notebooks are all there.