DanielJohnson

Engineering Hub

Engineering

Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.

Engineering is often taught as tools, frameworks, and techniques. In practice, it is a craft of understanding system behavior, failure points, and the trade-offs behind architecture choices.

This hub focuses on architecture, production thinking, and engineering decisions across frontend systems, distributed services, and modern AI applications.

What This Section Covers

The content is organized around core themes where modern software complexity tends to appear.

AI Systems

Large language models are quickly becoming part of modern software architecture. However, the model itself is only one component in a much larger system.

Real AI products combine prompts, retrieval, orchestration, tool interfaces, memory systems, and application logic. Once these components connect, challenges shift from model behavior to system design.

Topics explored:

  • Model Context Protocol (MCP)
  • Tool-using LLM architectures
  • Retrieval-augmented generation (RAG)
  • Agent orchestration
  • AI system design patterns
  • Trust boundaries in AI applications

Example Article

Model Context Protocol (MCP)

A deep dive into the protocol that standardizes how AI systems interact with external tools and data sources.

AI Security

Introducing AI into production systems significantly expands the attack surface.

Prompt injection, data leakage, tool misuse, and context poisoning emerge at boundaries between system components.

Topics explored:

  • OWASP Top 10 for LLM Applications
  • Prompt injection threat models
  • AI system trust boundaries
  • Securing tool-using agents
  • Retrieval and context poisoning
  • AI infrastructure risk management

Example Article

The LLM Attack Surface in Fintech

A breakdown of where AI security vulnerabilities appear across real fintech architectures.

Frontend Architecture

Modern frontend systems include rendering strategy, state management, design systems, performance constraints, and accessibility considerations.

As applications grow, frontend becomes a system in its own right and requires architectural thinking similar to backend services.

Topics explored:

  • Component system design
  • Design systems and design tokens
  • State management patterns
  • Rendering and data-fetching architecture
  • Performance optimization
  • Scalable frontend architecture

Example Article

Frontend Architecture

Patterns, principles, and systems thinking for building frontend applications that scale across teams, devices, and years.

Distributed Systems

Many modern products behave as distributed systems, whether teams explicitly describe them that way or not.

When services, jobs, event pipelines, and async workflows combine, reliability and failure handling become core engineering concerns.

Topics explored:

  • Event-driven architectures
  • API design and service boundaries
  • Observability and reliability
  • Asynchronous workflows
  • Data consistency and trade-offs
  • Failure modes in distributed systems

Why This Content Exists

Frameworks and tools move fast. The underlying system-design ideas last longer.

This section focuses on architecture fundamentals that remain useful as technologies evolve: trade-offs, reliability, performance, and security boundaries.

What You'll Find Here

  • Architecture breakdowns of real systems
  • Explanations of engineering trade-offs
  • Perspectives on AI infrastructure and security
  • Frontend architecture analysis
  • Distributed systems thinking applied to practical problems
  • Conceptual models for modern production software