AI models can perpetuate biases present in training data, leading to discriminatory algorithms and unfair system behaviors. Types of Bias Gender bias in hiring algorithms Racial bias in facial recognition Socioeconomic bias in credit scoring Age bias in recommendation systems Biased Code Example // AI might generate biased logic: function evaluateCandidate(resume) { let score = […]

AI-generated code can be difficult to debug due to unfamiliar patterns, complex logic, and lack of developer understanding. Debugging Challenges Unfamiliar code patterns Complex nested logic Missing context and comments Inconsistent error handling Debugging Nightmare // AI generates complex code: const processData = (data) => data .filter((item, index, arr) => arr.findIndex(x => x.id === item.id) […]

AI models require massive computational resources, contributing significantly to energy consumption and environmental impact. Environmental Impact High energy consumption for training Continuous server infrastructure needs Carbon emissions from data centers Electronic waste from hardware upgrades The Numbers GPT-3 training: 1,287 MWh of energy Daily ChatGPT usage: ~500,000 kWh Carbon footprint: Equivalent to 300+ cars annually […]

Amazon Q Developer is AWS’s AI-powered coding assistant. Here’s how to get it running in VS Code quickly. Installation Steps Open VS Code Go to Extensions (Ctrl+Shift+X) Search for “Amazon Q” Click “Install” on Amazon Q extension Sign in with your AWS account Setup Requirements VS Code version 1.70 or higher AWS account (free tier […]

The quality of Amazon Q’s responses depends heavily on how you write your prompts. Here are proven techniques for better results. Prompt Structure Context: Describe what you’re building Task: Specify what you want Requirements: Add constraints or preferences Good vs Bad Prompts // ❌ Bad prompt: “Create a function” // ✅ Good prompt: “Create a […]