Vercel, the cloud platform behind Next.js, has released react-best-practices, an open-source repository containing over 40 performance optimization rules for React and Next.js applications. The framework, which encapsulates over a decade of engineering knowledge from Vercel's production codebases, is structured specifically for consumption by AI coding agents and LLMs, though the team notes it is equally valuable for human developers.
"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue."
One thing I always do when I prompt a coding agent is to tell it to ask me any questions that it might have about what I've asked it to do. (I need to add this to my default system prompt...) And, holy mackerel, if it doesn't ask good questions. It almost always asks me things that I should have thought of myself.
This rapid change is largely due to the efficacy of AI tools and how they've tripled productivity. Just a few years ago, we were debating whether tools like GitHub Copilot were even reliable enough for basic autocomplete. Fast forward to today, and AI isn't just generating components; it's scaffolding entire full-stack applications, leading many to wonder if it might truly "take our jobs" in the future.
Programming with AI is still in its infancy, and yet it has already redefined what programming will be about in the immediate future. As a programmer, you are going to interact with a frigging robot that writes code on your behalf. What was science fiction yesterday is now a new reality, and it raises many questions. I won't pretend to have any answers here, just assorted thoughts.
For IT leaders navigating multicloud environments, success depends on strategic alignment across business units, robust governance frameworks, and proactive security postures. While multicloud offers agility and vendor flexibility, it also introduces challenges in visibility, compliance, and developer productivity. In this special report, you'll learn how to take advantage of the benefits of using multiple clouds, avoid common pitfalls, and ensure that multicloud is worth the investment.
The core challenges impacting developer productivity and experience are clear. In the 2025 Stack Overflow Developer Survey, nearly half (45%) of developers spend more time debugging AI-generated code than writing it. This, coupled with constant context-switching and manual troubleshooting, leads to significant time loss. New Relic's unified Intelligent Observability Platform addresses these issues by integrating disparate systems and performance data, providing crucial context, and driving actionable insights.
Just over a hundred visitors had crowded into an office building in the Duboce Triangle neighborhood for a showdown that would pit teams armed with AI coding tools against those made up of only humans (all were asked to ditch their shoes at the door). The hackathon was dubbed "Man vs. Machine," and its goal was to test whether AI really does help people code faster-and better.
Developers often spend significant time on necessary, but undifferentiated work, or "toil". Toil is often manual, repetitive, and of limited enduring value, making it a strong candidate for automation or delegation to generative AI tools. The re:Invent 2024 session Unleashing generative AI: Amazon's journey with Amazon Q Developer (DOP214) discussed how toil and productivity have an inverse relationship. Amazon Q Developer can help decrease toil and free up your developers to work on more productive tasks.
GitLab has launched the public beta of its GitLab Duo Agent Platform, an orchestration tool that enables developers to collaborate asynchronously with AI agents across the DevSecOps lifecycle. The platform, now available to GitLab.com Premium and Ultimate customers as well as self-managed installations, transforms traditional, linear development workflows into dynamic, multi-agent systems where AI handles routine tasks such as refactoring, security scanning, and research, while developers focus on complex problem-solving.
