A shared plugin marketplace lets teams package and distribute reusable AI capabilities, such as skills, agents and integrations. This article explains how it supports consistent workflows, faster onboarding and knowledge sharing. Key takeaway: make proven practices easy to install, reuse and evolve together.
When code generation is no longer the main bottleneck, developers’ value shifts to the human side of engineering: understanding problems, thinking in systems, making decisions and challenging assumptions. Key takeaway: good judgment and knowing when to push back matter more than ever.
As AI-assisted work becomes more complex, prompts alone can lose intent and context. This article explains how lightweight specifications create a shared basis for people, agents and implementation. Key takeaway: clear, durable specs make AI-supported delivery more aligned, reviewable and reliable.
What does it take to use coding agents effectively in day-to-day delivery? Drawing on a two-day summit and lessons from customer projects, this article shares practical guidance on context, code review and team workflows. Key takeaway: strong engineering practices remain essential as AI accelerates implementation.
AI is making implementation faster—but teams can still lose time by building the wrong thing. This article examines how the bottleneck shifts toward discovery, problem understanding and product decisions. Key takeaway: speed only creates value when teams are clear about what to build and why.
I may write more code, but it cannot replace the human work of framing problems, making trade-offs and owning outcomes. This article reframes the debate around how developers can use AI to move beyond code production. Key takeaway: engineering judgment becomes more valuable as implementation accelerates.
Can LLM-based coding agents make legacy modernization less daunting? This Shopware case study compares Claude Code and OpenAI Codex for system mapping, documentation and onboarding. Key takeaway: they can dramatically speed up understanding of unfamiliar codebases, but their results still need expert validation.