There are these people that write software the old way, and the old way is going to go away. They call it 'vibe coding.' I think vibe coding is a slur. What's wrong with the term is that it implies ease. They don't understand that it's a skill.
Baron traces the origin story back to his time building high-scale systems at Instana (which exited to IBM in 2020), where the reality of "always-on" platforms made one thing obvious: the tooling we rely on is often too low-level, too rigid, and too disconnected from real-world use cases. That gap has only widened as environments have exploded in complexity-more cloud providers, more managed services, more hybrid setups, more internal APIs, and "gillions" of tools stitched together into brittle workflows.
A mid-sized fintech company with 150 engineers rolled out AI coding assistants in early 2025. The productivity gains on greenfield projects hit 40%-better than the vendor's optimistic projections. Engineers building new microservices from scratch reported that AI pair programming felt like having a competent junior developer working alongside them, handling boilerplate, suggesting tests, catching edge cases before they became bugs.
AI is changing the role of developers: from authors to orchestrators. Instead of writing code line by line, developers increasingly manage AI output, introducing new architectural concerns such as "context engineering" to keep generative agents within constraints. Developer onboarding is faster, but with limitations. AI lowers the entry barrier for junior developers by helping them navigate unfamiliar codebases, but trust and long-term skill growth depend on mentorship, runtime feedback, and a strong ownership culture.
I thought I loved coding because that's what I could do, and what I was able to do very effectively. It felt like it was the thing for me to do, and nothing else ever came close. It was my superpower, my identity, my craft. But now that I have AI assistance, I'm realizing that the coding part was never actually the thing.
The product is called "Chad: the Brainrot IDE." It is yet another vibe coding integrated development environment - an IDE is the software developers use to code - but with a twist. While waiting for the AI coding tool to finish its task, the developer can mess around with their favorite brainrot activities, within a window of the IDE. Or, as the company's website advertises: "Gamble while you code. Watch TikToks.
It's amazing to see how quickly web developers have adopted artificial intelligence (AI) apps. Many have added this technology to their workflow and haven't looked back. There are pros and cons to this seismic industry shift. On the bright side, AI speeds up the development process. We don't have to spend hours poring over WordPress documentation. Instead, we explain our coding needs in plain language. AI helps us build new things from scratch and troubleshoot existing code.
This is true even when new features are generated with the assistance of AI. What better way to ensure your AI coding assistant provides suggestions that are efficient and consistent with the rest of your codebase than by having it use the same curated building blocks? Using this approach, an AI coding assistant can suggest new components that are composed of existing components, ensuring consistency and maintainability across your codebase.
In good news for the States, GitHub's numbers show that the country has been a "steady number one" in terms of developer population for the last five years, with a current coding community of 28 million. But India will add another 35.6 million-plus devs between now and 2030, taking it to 57.5 million or more, ahead of the projected 54.7 million calling the US home.
Zed, built in Rust, has a small market share but a lot of influence on open standard development. JetBrains, the creator of IntelliJ IDEA and PyCharm, is now joining as a co-developer and wants to integrate ACP into its IDEs. The company emphasizes that the protocol prevents vendor lock-in, allowing developers to use their preferred AI assistant within the environment of their choice.
infamously boasting that the tech was doing the work of " 700 full-time agents" last year - only to regret his decision months later, admitting that humans play an important role after all.
When I first started working with Amazon Q Developer, I was impressed by its capabilities, but I quickly found myself in a familiar pattern. Development teams using AI assistants face a common challenge: repeatedly explaining coding standards, workflow preferences, and established patterns in every conversation. This repetitive setup reduces productivity and creates inconsistent AI guidance across team members. Sound familiar?
Solid.js takes a unique approach to reactive state management that has broadly influenced other reactive front-end JavaScript frameworks. It provides a compact set of reactive primitives built on signals, rather than state, and leverages those to support higher-order functionality. With a clean core design built for speed and a full set of enterprise features, Solid charted an impressive 90% developer satisfaction in the January 2025 State of JavaScript survey. Let's get started with Solid.js.
Large language models (LLMs) are just another fancy compiler. Back in the 50s and 60s, everyone was working in Assembly, and then C showed up, and we didn't stop coding in Assembly because C was suddenly perfect. C isn't perfect, but we stopped doing it because C is good enough, and we're more productive coding in C. And to me, LLMs are a very similar trade-off. They're not perfect yet, but at some point they will be good enough to make us more productive.
