Course Overview

・Duration: 2 weeks, 6 intensive sessions

・Format: Cohort-based course

・Goal: Transform generic AI assistants into personalized coding partners that understand your codebase, patterns, and needs

Core Value Proposition

Stop wasting time with generic AI suggestions. Learn to create a personalized AI coding partner that actually understands YOUR specific context and requirements, regardless of which AI tool you use (Cursor, Copilot, Amp, Claude Code, Windsurf, etc.).

Instructors

・Eleanor Berger: Engineering and AI leader with experience in DevOps/SRE/Cloud, Applied AI, and Engineering Leadership.

・Isaac Flath: Dev efficiency expert with experience at big tech, open source, and advising companies.

Companies We've Helped

・SpecStory

・Travel and Leisure

・Cable & Wireless Communications

・GitHub

・Microsoft

・Google

・Canonical

・Answer AI

・and more ...

Companies Our Students Are From

・Amazon (AWS)

・Microsoft

・X

・DocuSign

・Monster

・TrustLayer

・and more …

Curriculum

📚 Mise en Place: Preparing tools, environments, and context for working with AI

・Rules and config setup

・Documentation strategies

・Tools & environments configuration

・LLM as assistant paradigm

📚 Good Vibrations: Interactive AI coding

・AI coding IDEs and tools comparison

・Interactive agents and workflows

・Real-time collaboration patterns

📚 While You Were Gone: Delegating work to background agents and workflows

・Background agents configuration

・CI/CD workflows with AI

・Autonomous task delegation

Weekly Session Breakdown

🗓️ Week 1: Foundation & Personalization

📋 The Context Progression Journey

Move from Generic → Curated → Personalized context

Real before/after examples showing 10x effectiveness improvements

Mise en Place principles for AI setup

📋 AI Assisted Development Toolkit

Comprehensive tool comparison and selection guide

Hands-on evaluation of latest tools worth trying

Interactive AI coding patterns

📋 Personalizing Your Static Context

Practical examples and transformations

Context curation best practices

Efficient management techniques

Rules and configuration management

🗓️ Week 2: Automation & Advanced Techniques

📋 Automating Updates from Chat Interactions

Tools for automatic context evolution

Mining conversation history for improvements

Pattern analysis to fix recurring AI failures

📋 Model Context Protocol (MCP) and Agent-Based Context

Building practical MCP servers

Real-world examples used in daily development

Automated repetitive information gathering

Background agent architectures

📋 Dynamic Context and Tools

Tool calling and dynamic information access

Building useful integrations for daily workflow

Production-ready tools useful for immediate projects

CI/CD integration strategies

Key Learning Outcomes

🛠️ Technical Skills

・Universal AI Setup System: Build context systems that work across ALL major AI tools (no vendor lock-in)

・Automated Context Evolution: Automate context updates based on actual coding patterns

・Pattern Mining & Analysis: Turn every AI mistake into a learning opportunity

・MCP Server Development: Create practical automation tools for daily use

🏗️ Practical Applications

・ Create and maintain context-independent rules across all major AI coding tools

・ Efficiently manage and sync context for different formats (Amp, Copilot, Cursor, Copilot, Windsurf, Claude Code, etc...)

・ Analyze conversation history to identify and fix AI pattern failures

・Build real integrations that drastically enhance workflow

🔁 Workflows

・Plan and task-based agentic processes

・Targeted human augmentation approaches

・Matching tasks to optimal AI assistance methods

・Enterprise deployment strategies and processes

🏢 Enterprise Integration

・ Implementing AI coding tools in enterprise settings

・Security and compliance considerations

・Team adoption strategies

・Scaling personalized context across organizations

・Integration with existing development workflows

Target Audience

Developers who:

・Use AI coding assistants but feel limited by generic suggestions

・Want to maximize productivity with personalized AI tools

・Need a vendor-agnostic approach to AI coding assistance

・Seek practical, production-ready solutions over theoretical concepts

・Know AI should be better, but doesn't see how to get there

Course Philosophy

・Focus on real-world applications

・Vendor-agnostic approach ensures long-term value

・Continuous improvement through automated pattern analysis

・Practical tools you'll use daily in production environments