Education For Software Engineers
Get weekly insights about LLMs and software craftsmanship
Ship what matters the most and measure impact.
Modularize, design boundaries, own reliability.
Accelerate coding, learning, and exploration.
Three pillars. Twelve topics. Practical knowledge.
Not just prompting - architecture, math, and how models think
Demystify LLM internals (transformers, attention) to use AI with intent, not trial‑and‑error.
Key outcome: Make better prompts and design choices by understanding model behavior.
Structured learning workflows, unknown‑unknowns discovery, and model‑assisted research.
Key outcome: Cut learning cycles from weeks to days.
Model Context Protocol in practice plus real pricing/optimization trade‑offs.
Key outcome: Build multi‑tool flows and control costs with eyes wide open.
Beyond basic prompts - structured patterns, context management, and techniques that turn trial‑and‑error into repeatable workflows.
Key outcome: Build reliable AI workflows with predictable results.
Strategic thinking tools developers rarely learn
Strategic thinking tools that guide what to build (and what not to).
Key outcome: Make product‑level decisions that stand up to change and constraints.
From domain exploration to boundaries and language everyone can work with.
Key outcome: Align teams and architecture around the business.
Capabilities, processes, stakeholders, and value - the foundation under your system design.
Key outcome: Design architecture that maps to how the business actually works.
Rapid domain discovery before you build - use LLMs to map unknown territories, identify blind spots, and validate assumptions.
Key outcome: Understand new business domains faster, even without immediate expert access.
Practical implementation across the stack
Practical containerized environments and the bits that actually matter.
Key outcome: Ship professional environments solo.
Modeling, modularization, and data in one coherent system.
Key outcome: Build software that scales and is maintainable.
Metrics, logs, tracing - predictability over surprises.
Key outcome: Detect and fix issues before users notice.
Contracts that reduce integration pain with automation where it pays off.
Key outcome: Fewer errors, faster collaboration.
Microsoft MVP and software architecture expert active since 2007. Architect, developer, consultant, trainer, and speaker. Hands‑on with both code and business. Led hundreds of commercial trainings and consultations. Author of numerous education projects with thousands of students. Focused on practical AI‑assisted engineering.
AI changed how fast we can code - and learn.
One developer can now deliver what used to take a team.
The difference is architectural thinking, product sense, and using AI effectively.
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