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πŸ”₯ Next-Gen Web Performance and Layouts with Chrome, CSS, and Private AI Tools

PLUS: Explore new Chrome 138 and 139 features, step-based gradients, layout strategies, and private AI assistants with LLaMA 4

Weekly Issue #177 | Subscribe to DS | Daily Sandbox Pro

πŸ›©οΈ QUICK SUMMARY

Hello Developers!

This issue highlights powerful updates in Chrome 138 and 139, including built-in AI APIs and new CSS layout tools like the Viewport Segments API. Learn how to create precise step gradients with just start and end values, and compare HTTP/3 vs HTTP/2 to see if the upgrade improves your site’s speed. Dive into a complete guide on when to use Flexbox vs Grid for responsive design, and see how to set up your own local coding assistant using LLaMA 4.

Dive in and keep coding!

πŸŽ† NEWS, INNOVATIONS, TRENDS, TUTORIALS

New in Chrome 138 - Use the new built-in AI APIs to summarize, translate, or detect the language of text. Check out several new CSS functions. Adapt your web layout to target foldable devices with the Viewport Segments API....

Chrome 139 beta - This release adds six new CSS and UI features. Short-circuiting var() and attr() ...

Step Gradients with a Given Number of Steps - Before reading further, try thinking about this. You are only given the start and end steps and the rest should be obtained via linear interpolation...

HTTP/3 vs HTTP/2 Performance: Is the Upgrade Worth It? - While HTTP/2 delivered major performance gains over HTTP/1.1, the differences between HTTP/3 vs HTTP/2 are more subtle.

CSS Flexbox vs Grid: Complete Guide & When to Use Each - For the longest time, layout systems in CSS were always a bit of a pain, as using tools like floats and tables was far from ideal. But, ...

πŸ”₯ Setting Up My Own Private Coding Assistant with LLaMA 4

This week I have been playing with setting up my private LLM to help me program. I have heard a lot about the next-gen coding assistants that could/would take our developer jobs, and so I wanted to see if there was anything to it. After doing some research, I realized that running llama4 would require a TON of processing power, aka GPUs, and so I decided to rather run the actual LLM in a cluster on runpods, and simply access it from my local environment. Here are my steps…

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