Digital Shelf

It’s more of a personal collection—random things I come across, resources worth remembering, and thoughts I might want to come back to later. Just a place to keep track of it all.

Github Copilot - Across Environments

Code Completion with GitHub Copilot Supported Languages (High Priority): Python JavaScript Java TypeScript Ruby Go C# C++ Note: Also supports many other languages, but above are strongest. Auto Suggestions (Ghost Text): Real-time suggestions while typing Can: Complete a line Generate full code blocks Actions: Accept → Tab Ignore → keep typing Saves time on: Syntax Logic Repetitive code Multiple Suggestions Pane: Shows alternative solutions Navigate: Windows/Linux → Alt + ] /

4 days ago3 min read

GitHub Copilot - Notes: Spaces

Exercise: https://github.com/skills/scale-institutional-knowledge-using-copilot-spaces What is Copilot Spaces: A dedicated Copilot chat with curated context Uses selected: Files Issues Pull requests Custom instructions More focused than general Copilot Chat Key Difference vs Copilot Chat: Copilot Chat → broad, general suggestions Spaces → focused, context-driven responses Why Spaces Improve Quality: Narrow context → more accurate and consistent answers Better for repeatable a

5 days ago3 min read

Guthub Copilot - Notes: Prompt Engineering

What is Prompt Engineering: Process of writing clear instructions for AI (Copilot) Ensures generated code is correct, relevant, and useful 4 Core Principles (4 Ss): Single → focus on one task Specific → give clear, detailed instructions Short → keep prompts concise Surround → provide context (files, comments, open tabs) Best Practices: Be clear and explicit Provide context using comments or related files Use examples to guide output Iterate: Refine prompt instead of rewriting

6 days ago3 min read

GitHub Copilot - Notes: Part 1

Exercise: https://github.com/skills/getting-started-with-github-copilot Mitigating AI Risks AI provides benefits (innovation, efficiency) but also introduces risks Main Risks: Lack of transparency → AI decisions are difficult to understand Bias and harmful outcomes → unfair decisions, privacy issues Risk Mitigation: Governance frameworks → establish rules and policies for AI use Transparency → ensure AI systems are explainable Human oversight → humans monitor and guide AI dec

6 days ago3 min read