SquareNeo – AI in Software Development: Practice, Learnings & Next Steps

Starting point

The development team was already using AI tools selectively, but without a shared vision: How do you deploy AI pragmatically and professionally — developing more efficiently, producing clean code, and releasing safely, instead of blindly trusting prompts and chasing “speed at any cost”?

Solution

A hands-on talk combined with a consulting session: a comparison of the paradigms vibe coding (“build, then fix”) vs. spec-driven development (80% preparation, targeted agent use), a side-by-side look at the AI toolbox (Cursor vs. GitHub Copilot, Lovable), best practices from real-world projects (rules, project plans, conventions, prompt engineering), as well as live demos: a full-stack app in Cursor and a SaaS prototype in Lovable. Including the limits and pitfalls — from package versions to code ownership.

Technology stack

• Cursor & GitHub Copilot (AI coding agents compared)
• Lovable (rapid SaaS prototyping)
• Spec-driven development (rules, project plans, docs, conventions)
• Prompt engineering for development teams

Result & Impact

• A shared vision for the professional use of AI in the development process
• Concrete tool and workflow recommendations, ready to apply directly within the team
• Awareness of the limits: code ownership, quality assurance, and typical pitfalls