Course Objective & Scope
Building software primarily through natural-language direction (vibe coding) unlocks dramatic speed for MVPs and internal tools, yet without structure it quickly collapses into fragile, unmaintainable code with exposed secrets. This course teaches disciplined vibe coding practices that keep momentum high while maintaining control.
Starting with a crisp mini-spec, iterative building in Cursor, Claude Code, Lovable and Bolt, taking Git checkpoints after every working slice, adding automated sanity checks, and knowing how to hand off a prototype to production engineering are practised hands-on. Participants build and deploy a working full-stack micro-application from scratch.
Who Should Attend?
- Software developers who want to build MVPs and internal tools rapidly
- Technical product managers, design technologists and data analysts
- Intrapreneurs and R&D / innovation teams
- Prerequisite: basic familiarity with web/software concepts; deep coding experience is not required
Key Takeaways
- Write a concise starter specification covering data models, views and constraints before prompting
- Slice features into small increments so the model does not break existing working code
- Use Git branches and frequent checkpoints to roll back broken iterations instantly
- Apply baseline security checks for authentication, secret handling and input validation
- Distinguish when to keep vibe coding and when to transition to formal engineering review
Curriculum
Vibe Coding Philosophy, Strengths and Failure Modes
- Separating prototypes, internal tools and scripts from mission-critical production systems
- Common failure modes: bloated files, fix-break loops and context drift
Mini-Specs and Choosing an LLM-Friendly Stack
- Selecting simple, type-safe stacks that models generate reliably
- Bootstrapping the project with a one-page
SPEC.md, data schema and rules file
Vertical Slicing and Git Checkpoint Discipline
- Implementing and verifying one vertical feature slice per prompt
- Locking in working states with
git commitand discarding dead ends cleanly
Breaking Out of Error Loops and Keeping Code Modular
- Stopping three-strike fix loops by feeding actual console and network logs
- Scheduling refactoring pauses to split overgrown files into clear modules
Security Checklist and Production Handoff
- Checking for leaked client-side API keys, missing auth/RLS rules and injection risks
- Clean-up and documentation before deployment or engineering handoff
Other Courses in This Category
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Hands-on training in autonomous multi-file software engineering with Claude Code, Cursor Agent and OpenAI Codex CLI, covering Plan-Act-Verify loops, MCP tool integration and parallel development with Git worktrees.
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Advanced training on building execution harnesses around coding agents—fast test/typecheck/lint feedback, custom tool wrappers, AST/LSP verifiers and internal eval benchmarks—so agents succeed reliably in enterprise repos.
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Advanced training on designing closed-loop autonomous coding cycles (generate → compile → test → observe logs/traces → repair) with convergence criteria, budget caps, anti-reward-hacking rules and nested loop architectures.
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Combining static analysis metrics with LLMs to understand unfamiliar codebases, detect technical debt and code smells, execute safe refactoring backed by characterization tests, and optimise performance.
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Developer training on generating unit, integration, contract and property-based tests with AI, using mutation testing to verify that generated test suites actually catch defects rather than mirroring implementation bugs.
- Live Course
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