What is Vibe Coding?

Notes from a talk by S Anand:


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.
  • Coders think at a higher level - stay in problem space.
  • Model capability keeps widening - the "vibe-able" slice grows daily.

How to work with it day-to-day
  • Fail fast, hop models - if Claude errors, paste into Gemini or OpenAI.
  • 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.
  • Quality cliffs - small edge-cases break; drop the use case or wait for next model upgrade.

What are the business implications
  • Vendors 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 vibe-coded PoC could 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 by Barbara Oakley.
  • Best tactic for staying current - experiment > read; anything written is weeks out-of-date - Experimenting is far superior to reading these days

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 license chains for AI-generated artefacts.
  • Waiting game trap - "just wait for the next model" can become a habit that freezes delivery.

Comments

Post a Comment

Popular posts from this blog

Kai-Fu Lee on China-US AI Race - Q&A Transcript from a Bloomberg Interview

Image
Kai-Fu Lee, Chairman of Sinovation Ventures and author of  AI Superpowers and  AI 2041: Ten Visions for Our Future on the China-US AI Race Q: There's still a lot of challenges when it comes to the whole monetization of AI, according to his perspective. In the last three years, though, how have you looked at this whole evolution of China AI versus the US, and what are the advantages and challenges for China? A: Yeah, so firstly, I disagree on the prospects for the USA. USA because yes, you look at OpenAI, it's making half billion spending 40 billion. It looks like a bad balance sheet, but most of that 40 billion is spent for future revenues. And if you believe there are 2x, 3x, 5x growth for the next three years, it's going to justify that valuation at some point. The bubble is merely that it's gotten ahead of itself, not the likelihood of growth in the future. Not saying it's worth its price, right, but there's absolute substance under the thumb. Now back to t...
Read more

The Mercurial Grok AI Assistant Understands & Speaks Indian Languages

Image
Grok , the AI model developed by Elon Musk's xAI, can understand when you type Indian languages like Hindi, Telugu, Odia or other Indian regional languages using English letters (like when you type 'namaste' instead of 'नमस्ते'), and it can respond by mixing English with those languages. Grok doesn't necessarily need the native script of these languages. It has natural language processing abilities that extend to multiple languages. This is great innovation because many people in India, especially in online communication, use transliteration. Grok can generate responses that combine English words and phrases with words and phrases from the regional language you used in your input. For example, if you ask a question using a mix of English and Telugu transliteration, Grok might respond with a sentence that includes both English and Telugu words. Check these Hindi, Telugu, Odiya samples - Grok, ab Hindi mein Grōk, ippuḍu telugulō Grok, Oḍiā re This way Grok is more...
Read more

40 Talks from the Google Web AI Summit 2025

Image
Front-end leaders share their experience on pushing the boundaries of Web AI and run machine learning models entirely client side in the browser via JavaScript to get privacy, lower costs, and lower latency. From traditional AI models to Generative AI such as large language models (LLMs) and diffusion models, discover the latest advances of Web AI in 2024 that are accelerated by technologies such as WebAssembly, WebGPU, and WebNN.
Read more