The best way to think about TanStack Start is that it’s a thin server layer atop the TanStack Router we already know and love; that means we don’t lose a single thing from TanStack Router.
A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. This is a sequel to my review of 2023.
Refactoring is something developers do all the time—making code easier to understand, maintain, and extend. While IDEs can handle simple refactorings with just a few keystrokes, things get tricky when you need to apply changes across large or distributed codebases, especially those you don’t fully control.
In this post, let’s consider several optimization techniques for improving Core Web Vitals metrics for sites that are built with React. We are a team of speed consultants from the Czech Republic and in this article we would like to share some experiences from the many front-end performance optimizations we did for our clients.
Over the past year, we've worked with dozens of teams building large language model (LLM) agents across industries. Consistently, the most successful implementations weren't using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns.
I hate benchmarking code, just like any human (which, at this point, most viewers of this probably aren’t ¯\_(ツ)_/¯). It is much more fun to pretend that your caching of a value increased performance 1000% rather than testing to see what it did.
My tweet about this blew up, so I thought it'd be helpful to break down exactly why we made this move. I started ComfyDeploy as an open source project (GitHub) - a full stack app with Next.js doing all the heavy lifting for both frontend and backend.