| Heads Up Founders’ takes: AI isn’t the end of developers, it’s their evolution
A new wave of “vibe coding” tools promises to build software from plain-language prompts. No syntax, no semicolons, just tell the AI what you want. It sounds like the end of coding as we know it. But it’s not.
These tools are great at handling the repetitive stuff: setting up frameworks, filling in templates, writing the same snippets over and over. What they can’t do is the thinking part: designing smart architecture, making tradeoffs, and turning an idea into something people actually want to use.
The smartest developers are moving from writing code to guiding systems, shaping intent, and managing complexity.
“Vibe coding” isn’t replacing developers. It’s just changing what great ones do best.
Read the full article at The Next Web → | | | Watch GitLab: What is AI-Powered DevSecOps?
In a recent CXOTalk episode, GitLab executives made a compelling case for how AI is reshaping DevSecOps. For years, development, security, and operations have worked in silos; developers building fast, security teams catching up later. AI is breaking those barriers.
By embedding intelligent automation into every stage of the lifecycle, vulnerabilities can now be flagged as code is written, not weeks after release. The result: faster releases, stronger defenses, and less friction between teams.
But it’s not without challenges. Governance, transparency, and trust in AI-driven decisions remain critical. Still, the shift is clear. Software innovation is moving from “build fast, fix later” to “build securely by default.”
For tech leaders, this isn’t just a tooling upgrade, it's AI forcing organizations to rethink what “secure by design” really means. And those who adapt fastest will own the next wave of software innovation.
Jump to the full video → | | | Listen Python, Go, Rust, TypeScript and AI with Armin Ronacher
In this episode of The Pragmatic Engineer, Armin Ronacher, creator of Flask and early Sentry engineer, challenges long-held beliefs about programming languages in the AI era. Python still dominates for its vast ecosystem, but as AI tools reshape workflows, the question is shifting from which language to how teams use them.
Ronacher points out that while Rust offers power, it slows iteration. TypeScript promises safety, yet real-world impact is less clear. And Python, while accessible, can struggle to scale without strong architecture. The real differentiator, he argues, isn’t syntax, it’s how well teams integrate AI-assisted development into their process.
As AI systems begin to generate and manage code, technical leaders are rethinking language choice as a strategic decision: one focused on agility, orchestration, and building systems that can evolve as fast as the tools shaping them.
Listen to the full podcast → | |
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