Get direct CTO mentorship and hands-on experience solving engineering problems that meet production level standards.
Tutorials build familiarity. Production builds engineers.
You’ve completed projects. But you’ve never operated inside a real, evolving production system.
Tutorials don’t evaluate how you think. Without architectural review and real critique, you don’t know what you’re doing wrong.
In production, decisions have consequences. Real responsibility builds judgment, confidence, and engineering maturity.
We treat AI as an engineering tool — not a substitute for system thinking. Participants learn how to combine modern AI-assisted development workflows with disciplined architecture, data modeling, and software engineering practices.
Participants begin by configuring a modern development environment where AI tools are integrated directly into the engineering workflow. Instead of treating AI as a code generator, participants learn how to collaborate with LLMs while maintaining architectural thinking and engineering control.
The environment combines AI-native IDE workflows with structured prompting, repository-aware context, and disciplined verification practices inspired by modern AI engineering workflows.
A structured transition from foundational knowledge to production-level responsibility.
You work on meaningful, real-world projects — not artificial exercises. Your deliverables reflect real constraints and real environments.
Your work is evaluated across the full software development lifecycle — from requirements and architecture to implementation and deployment.
Communication, domain reasoning, and system-level awareness. Learn how technical decisions connect to business outcomes.
Your domain expertise is converted into a strategic advantage. Your CV, online presence, and marketplace positioning reflect real production capability — not tutorial projects.
After evaluation, you receive a structured direction for transitioning into paid work — aligned with your skills and domain expertise.
Participants don't just study engineering concepts — they build real system components that combine data engineering, architecture design, and AI-assisted development workflows.
Participants build Model Context Protocol (MCP) integrations that expose structured system data to AI assistants and developer tools. Instead of relying on isolated prompts, AI models receive access to structured business context, data schemas, and operational APIs.
Through these integrations, engineers learn how modern AI-enabled systems provide context-aware interactions between software platforms and LLM-based tools.
Participants design and implement data pipelines that aggregate and transform information from multiple operational systems. These pipelines create structured data layers used for analytics, reporting, and operational decision-making.
The resulting architecture combines traditional data engineering practices with modern analytics and AI-assisted data enrichment techniques.
A real welcome message recorded for participants of Learn By Doing. No marketing — just how the program actually operates.
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