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2023

  • in General
  • 3 min read

Vibe Coding - What, What Not, and How

title: "Vibe Code? The What, Why, and How (And When to Hit the Brakes)" date: 2024-01-31

Vibe Code? The What, Why, and How (And When to Hit the Brakes)

What is Vibe Coding? At its heart, vibe coding is about using AI to generate code from a high-level intent or "vibe." Think of it less like a co-pilot and more like an automated sidekick. You describe a function or a script, the AI generates the code, and you run it. You're less focused on the syntax and more on the desired outcome. It’s a workflow of describe-generate-run-refine.

This is a stark contrast to responsible AI-assisted development, where the human developer remains firmly in the driver's seat, meticulously reviewing and guiding every line of code. Vibe coding is fast, fluid, and often done with the explicit goal of getting a quick result, sometimes with the intent to throw the code away later.

When to Ride the Vibe 🏄 Not all projects are created equal. Vibe coding is a superpower for the right task.

For Developers: Rapid Prototyping: Got a weekend idea for a simple web app or a data script? Vibe code it. You can spin up a proof-of-concept in hours, not days.

Automating Repetitive Tasks: Need a Python script to rename files or parse some logs? A simple prompt like "write a Python function to read a CSV file" can save you the boilerplate.

Learning a New Library: Want to see how a new library works? Ask an AI to generate a simple example. It's like having a personalized, instant documentation assistant.

When to Hit the Brakes 🛑 Just as a sports car is a bad choice for a family road trip, vibe coding has its limits. This is where it gets critical for tech leads and project managers.

For Tech Leads and Project Managers: Critical Systems: Never, ever vibe code mission-critical or security-sensitive applications. The code generated might have unknown flaws, and the cost of debugging or a security breach will far outweigh the speed benefits.

Long-term Projects: If a project needs to be maintained for months or years, a vibe-coded mess will become a nightmare. It will accumulate technical debt that cripples the team and makes scaling impossible.

Ensuring Code Quality: Vibe-coded solutions often lack documentation, modularity, and proper error handling. This can lead to an "entropy loop" where every fix introduces more problems.

Common Vibe-Coding Mistakes (And How to Fix Them) The true danger isn't the AI—it's the over-reliance on it. Here are some pitfalls to watch out for, with actionable advice for everyone on the team.

  1. The Security Trap 🔒 The Mistake: Blindly accepting AI-generated code that contains vulnerabilities. In one notable case, an AI assistant naively used eval() on user input, creating a critical arbitrary code execution vulnerability. Another common mistake is hardcoding API keys directly into a script.

Fix: Developers, maintain a "human in the loop" mindset. Always review code for common security flaws like insecure input handling. Tech leads, mandate static analysis tools and code reviews for any AI-generated code, no matter how small.

  1. The Technical Debt Vortex 🌪️ The Mistake: Treating a vibe-coded prototype as a production-ready solution. The code works, but it's a tangled mess that's impossible to debug or extend.

Fix: Developers, refactor aggressively. If a prototype is promising, treat it as pseudo-code and rewrite it with proper structure. Project managers, plan for a "refactoring phase" in your sprints. The AI got you 80% there; now build the last, most crucial 20% responsibly.

  1. The Skills Erosion 🧠 The Mistake: Over-relying on AI to the point where developers stop understanding the fundamentals. You lose the ability to debug complex issues because you never truly learned how the code works.

Fix: Developers, use AI as a tool to explore, not a crutch to lean on. Always ask the AI why it made a certain choice. Tech leads, foster a culture of learning. Encourage pair programming and discussions on how to improve AI-generated code.

  • in General
  • 2 min read

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title: "DO's and DOnt's with Vibe Coding" date: 2024-01-31

DO's and DOnt's with Vibe Coding

Summary notes of https://www.infoworld.com/article/4029093/9-habits-of-the-highly-ineffective-vibe-coder.html

  • Do's Do experiment with different models. Different LLMs have varying strengths and weaknesses, and you might need to try a few to find the right one for your specific coding problem.

  • Do treat the AI as a search tool. LLMs excel at finding and recalling information from their vast training data, such as obscure documentation or code snippets.

  • Do use the AI for simple, repetitive tasks. The article suggests AI can be helpful for basic programming chores.

  • Don'ts Don't trust the LLM completely. The article warns that LLMs can provide incorrect information and even "hallucinate" non-existent code or URLs.

  • Don't treat the LLM like a "dumpster." Avoid dumping large blocks of code on the AI, as this can be inefficient, costly, and even confuse the model.

  • Don't assume all models are the same. Each LLM has a unique internal structure, parameter count, and training data, which can significantly affect its performance.

  • Don't ignore the costs. AI tools charge by the token, and repetitive requests or large inputs can lead to surprisingly high costs.

  • Don't hand over full control. The article highlights the risk of trusting an LLM with critical tasks, as their inherent randomness can lead to unpredictable and destructive outcomes.

  • Don't expect the AI to "think" like a human. LLMs are clever mimics and excellent at information retrieval, but they are not always good at deep synthesis or providing novel insights.

  • Don't create inconsistent code. The randomness in LLM output can lead to a "patchwork quilt" of different coding styles, making the codebase messy and hard to maintain.