I spoke about vibe coding at SETU School last week.

Transcript: https://sanand0.github.io/talks/#/2025-05-10-vibe-coding/

Here are the top messages from the talk:

What is vibe coding

It’s where we ask the model to write & run code, don’t read the code, just inspect the behaviour.

It’s a coder’s tactic, not a methodology. Use it when speed trumps certainty.

Why it’s catching on

  • Non-coders can now ship apps - no mental overhead of syntax or structure.
  • Coders think at a higher level - stay in problem space, not bracket placement.
  • Model capability keeps widening - the “vibe-able” slice grows every release.

How to work with it day-to-day

  • Fail fast, hop models - if Claude errors, paste into Gemini or OpenAI and move on.
  • Don’t fight sandbox limits - browser LLM sandboxes block net calls; accept & upload files instead.
  • Cross-validate outputs - ask a second LLM to critique or replicate; cheaper than reading 400 lines of code.
  • Switch modes deliberately - Vibe coding when you don’t care about internals and time is scarce, AI-assisted coding when you must own the code (read + tweak), Manual only for the gnarly 5 % the model still can’t handle.

What should we watch out for

  • Security risk - running unseen code can nuke your files; sandbox or use throw-away environments.
  • Internet-blocked runtimes - prevents scraping/DoS misuse but forces data uploads.
  • Quality cliffs - small edge-cases break; be ready to drop to manual fixes or wait for next model upgrade.

What are the business implications

  • Agencies still matter - they absorb legal risk, project-manage, and can be bashed on price now that AI halves their grunt work.
  • Prototype-to-prod blur - the same vibe-coded PoC can often be hardened instead of rewritten.
  • UI convergence - chat + artifacts/canvas is becoming the default “front-end”; underlying apps become API + data.

How does this impact education

  • Curriculum can refresh term-by-term - LLMs draft notes, slides, even whole modules.
  • Assessment shifts back to subjective - LLM-graded essays/projects at scale.
  • Teach “learning how to learn” - Pomodoro focus, spaced recall, chunking concepts, as in Learn Like a Pro (Barbara Oakley).
  • Best tactic for staying current - experiment > read; anything written is weeks out-of-date.

What are the risks

  • Overconfidence risk - silent failures look like success until they hit prod.
  • Skill atrophy - teams might lose the muscle to debug when vibe coding stalls.
  • Legal & compliance gaps - unclear licence chains for AI-generated artefacts.
  • Waiting game trap - “just wait for the next model” can become a habit that freezes delivery.