Understand. Build. Verify. Diagnose. Review. Defend.
The operating method, not just the technology list, is the product. Here is how a day, a squad, a review and a defense actually work.
The learning method
- UnderstandTurn a business requirement into components, and name what is still unknown.
- LearnUse the architect’s guidance, the material and AI to learn only what this step needs.
- BuildImplement it yourself, collaborating on reasoning and troubleshooting.
- VerifyRun checks, read the logs, confirm it really behaves as expected.
- DiagnoseTreat errors as evidence: form a hypothesis, inspect the failing layer, change one thing.
- ReviewSubmit through GitHub, answer reviewers, review someone else’s work.
- ExplainDocument what exists, why, what failed, how you fixed it, and what changes in production.
- TransferSolve a changed requirement instead of replaying the same lab.
AI may write code you could not write from memory. The work is not done until you can explain what it does, verify that it works, and say how you would investigate a failure.
A normal day
At least six productive hours on weekdays. That means building, troubleshooting, reviewing and documenting, not six hours of passive video.
- Morning · 30 minScrum-of-scrumsFive batch representatives brief the mentor on progress, blockers and risks.
- MorningFocused learningMaterial, the requirement for the day, and AI-assisted concept exploration.
- MiddayHands-on buildIndividual implementation in your private GitHub repository.
- AfternoonSquad troubleshootingReason through errors and logs together; the mentor observes and escalates.
- AfternoonEvidence and reviewNotes, journal, troubleshooting and AI-use evidence, pull requests and checks.
- End of daySquad syncA short status and role check so tomorrow starts clear.
Who teaches you, and who keeps you moving
U.S.‑based Lead Architects
Technical depth: two live sessions every week, review of real cohort errors, production context, capstone design review and final technical certification.
- Session A · Requirement and architectureA new problem: how technology maps to the requirement, just-in-time concepts and production context.What problem are we solving?What is the data flow?Why this service?
- Session B · Engineering reviewReal errors from your cohort, how companies actually do it, and production-scale thinking.What failed, and what proved it?Where did AI mislead us?What changes at scale?
Mentors
Daily momentum: a morning scrum-of-scrums, about an hour a day with each batch, blocker follow-up and escalation to the architects. Mentors don’t replace the Lead Architects.
Learn how real engineering teams work
Every implementation is yours, done individually. Your five-person squad is for reasoning, troubleshooting, review and communication, never answer-sharing. Squads and roles rotate at every major topic, mixing colleges and regions where practical.
- Delivery CoordinatorTracks progress, risks and blockers; runs concise squad syncs.
- Requirements & Architecture FacilitatorKeeps the team clear on the requirement, components and open design questions.
- Troubleshooting & Observability LeadPushes for exact errors, logs and hypotheses instead of random AI changes.
- Quality & Peer Review LeadMakes sure reviews are real, on time and followed through.
- Documentation & Knowledge LeadKeeps requirements, decisions, errors and fixes traceable.
- FoundationalDocumentation & Knowledge Lead
- AWSTroubleshooting & Observability Lead
- DevOps and KubernetesQuality & Peer Review Lead
- Amazon Connect CustomerDelivery Coordinator
- CapstoneRequirements & Architecture Facilitator
New roles first, new teammates each time. Your role history follows you across the course.
GitHub is the engineering system of record
FreshLearn delivers the course content. GitHub records your work: one private repository for the bootcamp, with branches, commits, pull requests and reviews.
- 01-foundations/
- 02-aws/
- 03-devops-kubernetes/
- requirement.md
- architecture.md
- troubleshooting.md
- ai-use.md
- infra/main.tf
- 04-connect-agentic-cx/
- 05-capstone/
- Requirement and acceptance criteria
- What “working” means.
- Architecture rationale
- What each component does, permissions, data flow, failure modes.
- Implementation log
- Milestones and what you verified before moving on.
- Troubleshooting evidence
- The error, hypotheses, root cause, fix and verification.
- AI-use record
- Where AI helped, what you checked, what you rejected.
- Production readiness
- Security, monitoring, testing, cost and rollback thinking.
- Reflection
- What you can do now and what would change under a new requirement.
- Peer review
- A real cross-squad review, answered with evidence.
The peer-review standard
- Your reviewer comes from outside your squad, and pairings don’t repeat.
- Reviewers inspect the real implementation and evidence, not just the README.
- “Looks good” is not a review when there is real uncertainty.
- You answer with evidence or a change, and re-verify before approval.
Your experience becomes your assessment
At the end of each major assignment: a short 10–15 minute defense, recorded or supervised. The questions come from what you actually built, broke, fixed and reviewed. Uncertain or high-stakes results go to human review.
- Q1Common coreA question every student gets on the topic.
- Q2Your architectureDo you understand what you actually built?
- Q3Your errorA real failure of yours: root cause and verification.
- Q4AI, role and reviewYour AI judgment and engineering practice.
- Q5Cohort experienceA verified failure from another student.
- Q6Unseen scenarioA changed requirement or an unfamiliar failure.
How you’re evaluated
What you can build, diagnose, explain and improve. Not commit counts, airtime or a page that says “completed”. Attendance matters, and is reported separately from competence.
- Implementation and verification20%
- Architecture understanding15%
- Troubleshooting and root cause15%
- AI supervision and validation10%
- Documentation and production thinking10%
- Peer-review quality10%
- Role execution10%
- Oral technical communication5%
- Reliability and professional delivery5%
Technical mastery 70% Engineering practice 30%
- Week 1 baselineCode reading, Git, debugging, AI judgment, explanation and cloud terms.
- MidpointComparable transfer tasks and the trend in each competency.
- ExitCapstone, final defense and your whole evidence profile.
Recognition without popularity contests
Awards follow verified behaviour and published thresholds, never commit counts or meeting airtime. There is no public leaderboard.
Role excellence awards
- Delivery Leadership
- Requirements & Architecture
- Troubleshooting & Observability
- Quality & Peer Review
- Documentation & Knowledge
Individual awards
- AI-Native Engineer of the Semester
- Engineering Growth
- Root Cause Excellence
- AI Judgment
- Peer Engineering Impact
- Technical Communication
- Reliability & Delivery
- Production Engineering
Team awards
- Capstone Team of the Semester
- Collaboration Excellence
- Engineering Quality
Achievement badges
- AWS Builder
- AI-Assisted Engineer
- Verified Troubleshooter
- Peer Reviewer
- IaC Practitioner
- CI/CD Practitioner
- Kubernetes Practitioner
- Conversational AI Builder
- Agentic CX Builder
- Technical Communicator
- Production Thinking
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