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Deep Engineering #53: Rick Spencer on Matching the Right AI to the Right Engineering Work

Running AI under strict compliance, sorting tools by the job, and keeping cost and control in the engineer’s hands
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Recent posts

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How SUSE Runs AI Without Losing Control
Open source enterprises treat data sovereignty, MCP governance, and cost predictability as one connected problem
 • Saqib Jan
Sovereign AI and Agentic Infrastructure with Rick Spencer
On running AI disconnected from the internet, the three-tier framework SUSE uses to match tools to work, why output metrics are vanity metrics, and MCP…
 • Saqib Jan
Deep Engineering #52: Sam Keen on the Context Tax You Pay in Every Claude Code Session
Why every AI coding session starts from zero, and how to fix it with a system instead of a better prompt
 • Saqib Jan and Sam Keen
Deep Engineering #51: Francesco Ciulla on Rust, Go, and Service-Level Engineering Decisions
On Rust versus Go, latency-sensitive services, memory overhead, deployment workflows, and the backend constraints that shape language choices
 • Saqib Jan and Francesco Ciulla
Try Rust With Your Own Hands and Eyes with Francesco Ciulla
On adoption strategy, flat latency, and the year he stopped hedging on Rust for production
 • Saqib Jan and Francesco Ciulla
Deep Engineering #50: Brian Allbee on Building Better Python Software
Brian Allbee on why most Python developers are optimising for correctness when they should be optimising for sustainability, and what that shift…
 • Saqib Jan and Brian Allbee
Hands-On Software Engineering with Python with Brian Allbee
Brian Allbee joins Deep Engineering to discuss the mindset shift from writing code to engineering systems.
 • Saqib Jan and Brian Allbee
Deep Engineering Specials: Enterprise AI has an API problem
The next enterprise AI bottleneck is not model capability. It is whether agents can discover, understand, and safely use the systems they need to act on
 • Saqib Jan and Erik Wilde
Deep Engineering #49: David Knickerbocker on Open Source Intelligence and Real-World AI Systems
Why messy, contradictory data changes how engineers should think about retrieval, judgment, and production AI
 • Saqib Jan
Compute Obsession Is Slowing Down AI Systems
Why data movement costs more than computation and what most engineers building AI systems are getting wrong because of it
 • Saqib Jan and Jim Ledin
Deep Engineering #48: Erik Wilde on Agent-Ready APIs, Widespread MCP Adoption, and the OpenAPI Standards That Matter
On the abstraction level problem, the limits of linting, and why investing in your API foundation matters more than chasing the current delivery…
 • Saqib Jan and Erik Wilde
Building Agent-Ready APIs in Production with Erik Wilde
On OpenAPI 3.2, agent-ready APIs, and why MCP might not be the answer you think it is
 • Saqib Jan and Erik Wilde

Practical Deep-Dives

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From NIC to P99: Engineering Low-Latency C++ Trading Systems in 2026
A practical look at the hardware, OS, and code-level optimizations you need to stop burning your latency budget.
 • Shreyans
Coroutines
The complete Chapter 15: Coroutines from Kotlin for Java Developers by José Dimas Luján Castillo and Ron Veen (Packt, 2025)
 • Ron veen and José Dimas Luján
Go + Eino ADK Quickstart: Master Core AI Agent Design Patterns
Build composable, interruptible multi-agent workflows in Go with Eino’s unified Agent abstraction—ReAct, WorkflowAgents, Supervisor…
 • Pandeng Li
The Rise of the AI Architect
How AI architects can bridge AI and engineering to ship reliable decision-making systems
 • Richard D Avila
Thinking Computationally
The complete Chapter 1: Thinking Computationally from The C++ Programmer's Mindset by Sam Morley, Packt 2025.
 • Sam Morley
Fundamentals of AI System Architecture
The complete "Chapter 1: Fundamentals of AI System Architecture" from the book, Architecting AI Software Systems by Richard D Avila and Imran Ahmad
 • Imran Ahmad and Richard D Avila
Implicit Memory Systems for LLMs
When Code Surrenders to Context
 • Sam Keen
Merkle Trees and Anti-Entropy — Concepts and Implementation
How Distributed Systems Like Cassandra and DynamoDB Stay Consistent at Scale — And How You Can Build It Too
 • Archit Agarwal
Security Practices and User Management
The complete "Chapter 15: Security Practices and User Management" from the book, GitHub Foundations Certification Guide (Packt, 2025), by Ayodeji…
 • Ayodeji Ayodele
Introduction to Application Development Frameworks
The complete "Chapter 1: Introduction to Application Development Frameworks" from the book, Building an Application Development Framework (Packt, 2025…
 • Roman Voronin and Ivan Padabed
From Prototype to Production: Why AI Systems Need Architecture Discipline
How architecture makes model- and data-driven systems production-ready
 • Richard D Avila
Quantum Error Correction and Fault Tolerance
The complete “Chapter 14: Quantum Error Correction and Fault Tolerance” from the book A Practical Guide to Quantum Computing by Elías F. Combarro and…
 • Elías F. Combarro and Samuel González-Castillo

Interviews

View all
Sovereign AI and Agentic Infrastructure with Rick Spencer
On running AI disconnected from the internet, the three-tier framework SUSE uses to match tools to work, why output metrics are vanity metrics, and MCP…
 • Saqib Jan
Try Rust With Your Own Hands and Eyes with Francesco Ciulla
On adoption strategy, flat latency, and the year he stopped hedging on Rust for production
 • Saqib Jan and Francesco Ciulla
Hands-On Software Engineering with Python with Brian Allbee
Brian Allbee joins Deep Engineering to discuss the mindset shift from writing code to engineering systems.
 • Saqib Jan and Brian Allbee
Building Agent-Ready APIs in Production with Erik Wilde
On OpenAPI 3.2, agent-ready APIs, and why MCP might not be the answer you think it is
 • Saqib Jan and Erik Wilde
Design Patterns, Ownership Models, and Building Resilient Systems in Rust with Evan Williams
Why experienced developers have the hardest time, and what that tells you about the language
 • Saqib Jan
Computer Architecture in an AI-accelerated World with Jim Ledin
On memory hierarchies, GPU mechanics, hardware abstractions, and what engineers get wrong by ignoring the hardware layer
 • Saqib Jan and Jim Ledin
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100 Days of Networks
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