AI Coding Assistants

Use AI coding tools like a professional: how they work, writing specs and prompts that get correct code, reviewing and testing AI-written code, debugging with AI, agentic coding tools, security and licensing risks, and how to keep learning real skills. Every bug shown is real, runnable Python.

1. 01How AI Coding Assistants Work — Tokens, Context Windows, Autocomplete vs Chat vs Agents, and Their LimitsWhat happens inside an AI coding assistant, why it sometimes writes confident nonsense, the three kinds of tools (autocomplete, chat, agents), what "context" means for code quality, and the mindset that separates developers who benefit from AI from those who get burned.
2. 02Prompting for Code — Write Specs, Not Wishes (Context, Constraints, Examples and Acceptance Tests)Turn vague one-line prompts into clear specs that produce correct code the first time — with a reusable template, a worked example comparing a weak and a strong prompt, and techniques like asking for a plan first, giving examples and splitting big tasks.
3. 03Reviewing AI-Written Code — A Checklist and 6 Real Bugs That Look CorrectThe review checklist professionals use on AI-generated code, and six runnable examples of plausible-looking bugs — mutable defaults, floating-point money, off-by-one pagination, timezone-naive dates, swallowed exceptions and quadratic loops — with the fixes.
4. 04Testing AI-Written Code — Tests First, Edge Cases, Property Checks and Letting AI Write Tests SafelyThe fastest way to trust AI-generated code is to test it. Write acceptance tests before asking, use edge-case tables, compare against a simple brute-force version on random inputs, and use AI to suggest tests without letting it grade its own homework.
5. 05Debugging with AI — Reading Errors, Minimal Reproductions and Asking the Right QuestionsUse AI to debug faster without guessing: read stack traces yourself first, build a minimal reproduction, give the model the error plus the code plus what you expected, test one hypothesis at a time, and know when AI is leading you in circles.
6. 06Agentic Coding Tools — Letting AI Edit Files and Run Commands Safely and EffectivelyHow AI coding agents work (plan, act with tools, check, repeat), how to give them good tasks and project instructions, the permission and Git safety rules that prevent disasters, and how to review a large AI-made change efficiently.
7. 07AI Coding Risks — Secrets, Privacy, Insecure Suggestions, Hallucinated Packages, Licensing and Academic HonestyThe risks that get developers and students into trouble with AI tools — leaking secrets and personal data in prompts, insecure code patterns, fake package names that attackers register, licence and copyright questions, company policies and academic integrity — and the simple habits that prevent them.
8. 08Learning and Building a Career with AI — Stay Skilled, Pass Interviews and Stand OutHow to use AI to learn faster without losing the fundamentals, a weekly practice routine, preparing for interviews where AI isn't allowed, portfolio projects that show AI-era skills, and what employers now expect from freshers.