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 read; anything written is weeks out-of-date.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.