AI-Native Engineering Story
The article discusses the author's experience with AI in software engineering, highlighting a significant improvement in productivity. Initially, a Kubernetes CSI Driver prototype took months and a team to develop, while a more complex Kubernetes Operator was built solo in just two weeks. The author attributes this change to a new workflow that leverages AI tools effectively.
- ▪In 2020, the author built a Kubernetes CSI Driver prototype during a hackathon, which took months to turn into a production-ready integration with a team.
- ▪Five years later, the author developed a production Kubernetes Operator alone in just two weeks using a new workflow with AI tools.
- ▪The workflow included spec-driven architecture, grounding the AI model with accurate documentation, and using GitHub Copilot for implementation.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3951248) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Daniel Stolf Posted on May 27 • Originally published at linkedin.com AI-Native Engineering Story #ai #programming #claude #githubcopilot In 2020, during the pandemic lockdown, I built a working Kubernetes CSI Driver prototype in a hackathon. It was good enough to win. But turning it into a production-ready integration took months — and eventually required a team of 3 additional engineers to get there. Same person. Same domain. Same company. Months, plus a team.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
