AI | Bastab C - Notes to Self

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AWS AI and ML - Series Overview

The AI side of my work kept producing notes worth keeping in one place - which AWS service to reach for, the concepts behind the choice, and what changes once you move from reading about Bedrock to building with it. Chapter 1 (Fundamentals) - concept-focused, assumes no prior AI/ML background. Chapters 2-3 (build) - assume Chapter 1 as background, hands-on with specific AWS services. Chapters land as the hands-on work behind them gets done, not on a fixed schedule. The series Chapter 1 - Fundamentals Algorithm types, performance metrics, AWS AI services, inference options, Bedrock vs SageMaker, and prompt engineering techniques - the concepts I kept returning to. ...

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AWS Builder Center Challenge: WeekWrap

For the AWS Builder Center’s July Weekend Challenge, I built WeekWrap - an AI-native work log app where Bedrock is a functional part of what the app does at runtime, not a development aid. ...

AWS Certified AI Practitioner (AIF-C01) Study Notes

I sat the AWS Certified AI Practitioner (AIF-C01) and passed. This is the retrospective I wish I had read before starting - what the exam actually tests, the services and concepts to know well, and which prep resources earned their place. AIF-C01 covers more ground than the name suggests: classical ML, generative AI, foundation models, and responsible AI. I came in with a working knowledge of the AWS services landscape and had been building on Bedrock before sitting this - Domains 2 and 3 still took the most prep time, and the vocabulary is denser than it looks. Where your depth sits in that space will shape what needs the most attention. ...

AWS AI and ML - Fundamentals

This is Chapter 1 of the AWS AI and ML series. These are the concepts and service distinctions I kept returning to while building on Bedrock and working through the AIF-C01 exam. Writing them down in one place made the concepts stick. Different types of ML algorithms Three broad families, each suited to a different class of problem. Supervised learning - trains on labelled input-output pairs. Two sub-types: Classification - predicts a category (spam/not spam, image labels). Algorithms: logistic regression, decision trees, random forests, SVM, neural networks. Regression - predicts a continuous value (house price, demand forecast). Algorithms: linear regression, gradient boosting, neural networks. Unsupervised learning - finds structure in unlabelled data: ...

AWS Kiro CLI - Managing Infrastructure from the Terminal

Kiro CLI is an AI-assisted terminal tool for AWS, rebranded from Amazon Q Developer CLI in November 2025. It sits on top of your existing AWS credentials and tooling. The q and q chat shortcuts from Q CLI still work but kiro-cli is the current entry point. Install macOS brew install --cask kiro-cli Or via script: curl -fsSL https://cli.kiro.dev/install | bash Windows (PowerShell) irm 'https://cli.kiro.dev/install.ps1' | iex Linux / WSL2 (Ubuntu / Debian) - .deb ...

Configuring GitHub Copilot Custom Instructions

I set up Copilot instructions on a CDK project and went looking for confirmation that the structure actually matched GitHub’s model, rather than carrying over an assumption from Claude Code. Claude Code works by walking the directory tree and picking up any CLAUDE.md it finds in the current or parent folder - drop a file next to the code it should govern, and Claude finds it. Copilot has no equivalent “drop a file in the folder” convention. It centralises everything under .github/ instead, and scopes by path through frontmatter rather than by file location. ...