Maven – Elite AI Assisted Coding

Make it code like you do. Turn generic AI assistants into coding partners that actually get your style and have the right context.

Transform generic AI assistants into personalized coding partners

Learning Structure
Each part (week) of the course includes

・2 live lessons

・Office hours session (Q&A and discussion)

・Live practice session

・Homework assignment (with both begginner-friendly and stretch exercises)

・Pre-reads and lesson materials

・Deep-dive collections (optional) for exploring topics in depth

In addition, learning continues with

・Questions and discussions in the course Discord server

・Guest lectures

・Student exchange and collaboration

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

・Google

・X

・Shopify

・Cisco

・LinkedIn

・Red Hat

・DocuSign

・Qualcomm

・Monster

・Booz Allen Hamilton

・TrustLayer

・and more …

Weekly Session Breakdown

?️ Week 1: Foundation & Personalization

? Introduction to AI-Assisted Software Development

・Course orientaion.

・Tools and human in the loop – Claude, Copilot, Cursor, Codex … and you.

・Recognize capabilities, limits, pitfalls, safety, and coding pattern basics.

・Start with any modern IDE, terminal, or browser-based agent with near-zero setup.

? Context Engineering & Frictionless Setup

・Context stack: rules, docs, tools.

・MCP in practice: context and action.

・Supporting docs: importing key library/framework docs; updates; combining static imports with MCP.

・Determinism & validation: smoke prompts, golden answers, scripted checks.

?️ Week 2: Interactive Development & Collaboration

? Interactive Agents & Spec‑First Planning

・Spec‑first flow: requirements → tasks → constraints → evaluation.

・Mastering modern coding agents (in IDE and terminal).

・De‑risking scope: narrow diffs, feature flags, rollback plans.

・Custom MCPs, workflows, commands, and subagents.

? AI Code Review, PR Orchestration & Security

・Coding agent security: private data + untrusted context ⇒ risk.

・Approvals & execution modes and environments: fine-grained permissions, sandboxing, execution modes.

・Auditability: structured logs and git history.

・AI-powered DevOps: PRs, project management, deployment, observability.

?️ Week 3: Async Agents, CI/CD & Advanced Techniques

? Async/Background Agents & Dynamic Context

・Background agent architectures: SaaS, GitHub Actions, scheduled tasks, job runners.

・Effective delegation: specs and environments for end-to-end AI execution.

・Automating software projects with continuous AI.

? Parallelization, Measuring Efficacy, & Continuous Improvement

・Accelerating productivity with parallel agents and workflows.

・Measuring and reasoning about efficiency and quality in AI-powered software development.

・Mining projects for evidence-based decision making and continuous improvement.

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

What you’ll get out of this course
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 D
Practical Applications

Create and maintain context-independent rules across all major AI coding tools
Efficiently manage and sync context for different formats (Amp, Cursor, Copilot, Windsurf, Claude Code)
Analyze conversation history to identify and fix AI pattern failures
Build real integrations that
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

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Maven – Elite AI Assisted Coding

Maven – Elite AI Assisted Coding

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