CompCode learning catalog
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Practical modules for AI-assisted engineering — from foundations to agentic workflows. 24 courses available.
Agentic Workflows and AI Governance
Agentic Workflows and AI Governance
This module is for senior engineers and tech leaders who need to scale AI use beyond individual productivity. Learners design agentic workflows with approval gates, define governance policies (least privilege, auditability, redaction), and practice incident response for AI-assisted systems.
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AI-Assisted Development & Vibe Coding
AI-Assisted Development & Vibe Coding
The signature module of the program. Learners spend most of the session **doing**, not listening. We tour the major AI coding tools (Cursor, GitHub Copilot, Claude Code, Aider), introduce four prompt patterns, then run a debug lab where each learner fixes a broken FastAPI endpoint using AI as a pair-programmer and documents every prompt. The point is to make AI a habit, not a curiosity.
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AI-Augmented Workflows end-to-end
AI-Augmented Workflows end-to-end
The capstone of the AI-augmented track. We take a small feature from request → branch → code → tests → PR → review → deploy → observe, with AI involved at every phase and a single prompt log as the spine. Then we triage a production incident with AI as a triage partner, while protecting secrets and avoiding fabrication risks. The deliverable is an **evidence pack** that future reviewers (or hiring managers) can…
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Career & Industry Readiness
Career & Industry Readiness
The bridge between this program and your first (or next) role. Portfolio, OSS, interview prep that doesn't game the test, feedback skills, and the 90-day plan.
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CI/CD: pipelines, environments, and AI-generated workflows
CI/CD: pipelines, environments, and AI-generated workflows
Pipelines are how software actually ships. This module teaches the **anatomy** of a CI/CD pipeline (independent of any one tool), then makes it concrete in GitHub Actions and GitLab CI side-by-side. We add environment gates, secrets, and a choice of deployment strategy. The lab generates a pipeline with AI as a pair and then critically reviews what AI got wrong — pipelines are exactly the place where AI is fluent…
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Cloud Fundamentals (cloud-agnostic)
Cloud Fundamentals (cloud-agnostic)
A vendor-agnostic mental model: IaaS / PaaS / SaaS; compute primitives; an equivalence map across AWS / Azure / GCP; and the decision frame for "where should this run?".
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Containerization with Docker and Compose
Containerization with Docker and Compose
The minimum container literacy a working engineer needs. Multi-stage builds, Compose for local dev, the difference between `docker run` and `docker compose up`, and how to debug from outside the container.
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Context, APIs, and AI IDE Debugging
Context, APIs, and AI IDE Debugging
This module is the transition from “vibe MVP” to “engineering workflow”. Learners practice debugging with AI without blind trust, integrating an external API with secrets, and using multi-file context in an IDE safely. The output is a small feature implemented as a PR with evidence: tests, logs, and decisions.
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Foundations: Modern SDLC + the AI-Augmented Engineer
Foundations: Modern SDLC + the AI-Augmented Engineer
A grounding module. We define the modern SDLC (Plan → Design → Develop → Test → Release → Operate → Learn), show where AI augmentation slots in, and reset expectations about what an engineer's day looks like when an AI assistant is part of the workflow. By the end, every learner has a one-page diagram of an SDLC for a real product and a written list of where AI would or would not help.
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Infrastructure as Code (Terraform + Pulumi)
Infrastructure as Code (Terraform + Pulumi)
A pragmatic intro: HCL fundamentals, a real `plan` / `apply` cycle, why state matters, and what GitOps looks like for cluster IaC.
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Intro to Web Development
Intro to Web Development
A hands-on introduction to building websites with HTML, CSS, and JavaScript. Perfect for absolute beginners.
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Mastering AI-Assisted Development & Vibe Coding
Mastering AI-Assisted Development & Vibe Coding
A 3-month, day-by-day program that takes you from your first structured AI prompt to shipping a production-grade, AI-built application. Covers prompting, GitHub Copilot, Cursor, Claude Code, no-code builders, full-stack AI development, MCP, AI agents and Agentic AI, small language models, and automation with n8n.
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MCP and Legacy Code Steering
MCP and Legacy Code Steering
This module teaches mid-level engineers to steer complex systems with AI safely. Instead of pasting huge context into chat, we introduce MCP as a structured way to expose tools and resources, then apply it to a real problem: refactoring legacy code behind tests while keeping security boundaries and auditability.
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Observability: logs, metrics, traces
Observability: logs, metrics, traces
Observability is what tells you whether the thing you just deployed is OK. We teach the three pillars (logs / metrics / traces), then instrument a real service with OpenTelemetry and watch the data flow into a Prometheus + Grafana stack. The signature artifact is a triage of a synthetic incident — given only logs, metrics, and traces, the learner finds and fixes the root cause without reading source code.
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Orchestration: Kubernetes basics (and when not to)
Orchestration: Kubernetes basics (and when not to)
Just enough Kubernetes to be productive: pod, deployment, service, ingress; local cluster via Kind; and the equally important skill of recognising when K8s is overkill.
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Project Management & Agile (in practice)
Project Management & Agile (in practice)
Agile minus the dogma. Practical Scrum / Kanban patterns, real-tool walk-through (Jira or Linear), and the soft skills early-career engineers most often miss: written communication and retro facilitation.
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Prompt Engineering for Developers
Prompt Engineering for Developers
Module 02 introduced four patterns. This module goes one layer deeper: few-shot, chaining, output evaluation, and the discipline of a prompt library. The lab is hands-on: each learner builds a small reusable library for one task they actually do.
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Security & Supply Chain
Security & Supply Chain
Practical security for the AI-augmented engineer: OWASP Top 10 you'll actually face, secrets management, SBOM + signing for the container, and a triage discipline for scanner output. Plus the constraint of not leaking sensitive context to AI during triage (callback to Module 17).
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Software Design: SOLID, clean architecture, DDD basics
Software Design: SOLID, clean architecture, DDD basics
Most early-career engineers can recite SOLID but can't spot violations in their own code. This module teaches recognition — then practice — in two languages. We finish with a refactor lab where AI is the pair and the rule is "tests stay green".
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System Architecture: monolith, microservices, event-driven, serverless
System Architecture: monolith, microservices, event-driven, serverless
Architecture choices compound. This module teaches the decision framework — not the trends — and applies it through a case-study walkthrough. Learners leave with a written ADR for a product they own (or imagine).
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Technology Selection: ADRs and weighted scoring
Technology Selection: ADRs and weighted scoring
Picking a framework, database, queue, or cloud is the most common high-stakes decision early-career engineers face. This module gives them a repeatable framework: weighted scoring + ADR. The lab is to make and document a real choice.
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Testing & Quality
Testing & Quality
The testing pyramid, plus modern wrinkles: integration tests with real services via Testcontainers, and AI-generated tests with calibrated trust.
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Version Control & Collaboration
Version Control & Collaboration
Git is universal but most early-career engineers know only the surface — pull, commit, push. This module goes one layer deeper into Git's object model, then jumps straight into the workflows that real teams use: trunk-based development, short-lived branches, AI-assisted PR reviews, conventional commits, and merge-conflict resolution with an AI pair. By the end, learners can run a PR cycle from `gh repo fork` to…
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Visual AI Builders: Idea to MVP
Visual AI Builders: Idea to MVP
This module teaches the “vibe MVP” loop: start with a clear problem, get to a working demo fast using a visual AI builder, and keep quality by adding constraints, checks, and a small evidence log. The aim is not perfect architecture — it is a usable prototype with a credible story for how it works and what it doesn’t do yet.
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