Vibe coders nowadays 🙃
Summary
A job candidate, new to the workforce, described their programming workflow primarily through a tool called CloudCode. When asked about TypeScript and secure dashboard development, the candidate explained they use CloudCode to generate the dashboard, specifying TypeScript. Their process involves spinning up a project manager sub-agent for planning, an engineer sub-agent for coding, and a tester sub-agent for review. The project manager agent is granted full access to manage other agents. For expedited delivery, multiple parallel coding agents can be deployed, with approvals linked to WhatsApp. Security is addressed by using CloudX to review CloudCode's output, aiming for "NASA-level security."
Key takeaway
For AI/ML Directors evaluating junior talent, this exchange highlights a potential gap in foundational programming knowledge when candidates rely solely on AI-driven development tools. You should assess candidates' understanding of underlying languages and secure coding principles beyond tool-specific workflows to ensure they can troubleshoot and innovate independently.
Key insights
The candidate relies entirely on AI agents and tools for software development, including coding and security.
Principles
- Delegate tasks to specialized AI agents
- Grant full access for agent management
Method
Generate code with CloudCode, then use sub-agents for planning, coding, and testing. Review security with CloudX, and parallelize coding for speed.
In practice
- Use CloudCode for initial code generation
- Employ CloudX for security auditing
Topics
- CloudCode
- CloudX
- AI Agents
- Automated Software Development
- TypeScript
Best for: CTO, VP of Engineering/Data, AI Architect, Software Engineer, AI Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by What's AI by Louis-François Bouchard.