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Architecture and engineering resources organized around the problem you are solving.

Browse practical thinking across software architecture, platforms, AI-native systems, and engineering strategy.

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AI EngineeringSep 22, 2026

Jev AI Explained: When AI Needs to Decide, Not Generate

A simple explanation of Jev, TypeSafe AI's System One model: what it does, how a request works, why it differs from an LLM, and where decision models fit in AI systems.

Explainer · Working Knowledge9 min read

Software ArchitectureSep 20, 2026

The Architecture of Waiting: Why Queue Depth Is a Business Metric

Queues are usually drawn as small boxes between services. In production, they are something more consequential: stored promises. When work waits, customer expectations, recovery time, capacity and business risk accumulate with it.

Essay · Working Knowledge11 min read

Software ArchitectureSep 13, 2026

Your Dependencies Are Part of Your Architecture

We draw architecture diagrams around services we own, while much of the software we actually run arrives through packages, base images, build actions, plugins, and transitive dependencies. Those are not implementation details. They are part of the system's trust architecture.

Essay · Working Knowledge11 min read

Software ArchitectureSep 06, 2026

Portability Is Not Resilience: Rethinking Cloud Concentration Risk

Cloud resilience is often reduced to a portability question: could we run this somewhere else? The harder and more useful question is whether the business can continue when a dependency it assumes will always be there suddenly is not.

Essay · Working Knowledge11 min read

AI EngineeringSep 03, 2026

Inside Modern AI Search: Embeddings, Hybrid Retrieval, RRF, and Reranking

A practical, easy to follow guide to what actually happens between a user's question and the context an LLM receives, from keyword search and embeddings to hybrid retrieval, RRF, and reranking.

Guide · Deep Dive13 min read

Engineering StrategyAug 30, 2026

A Practical Architecture Decision Framework

A reusable way to make consequential architecture decisions without turning every choice into a committee meeting or reducing architecture to a technology comparison table.

Decision Guide · Deep Dive12 min read

AI-Native SystemsAug 27, 2026

How AI Systems Get Work Done: From Tools and Loops to Agents and Graphs

A beginner-friendly learning series on graph engineering, AI agents, tools, loops, harnesses, and multi-agent workflows, built around one practical example.

Learning Series · Quick Read3 min read

Software ArchitectureAug 20, 2026

Feature Flags at Enterprise Scale: A Practitioner’s Guide to Choosing the Right Approach

A practical enterprise framework for deciding whether to build, self host or buy feature management, what to evaluate, how to score options and what questions to ask before standardizing on a platform.

Practitioner Guide · Deep Dive18 min read

Engineering StrategyAug 16, 2026

Cost Is an Architecture Property, Not a Finance Problem

Cloud cost is often treated as something to optimize after a system is built. But the largest cost decisions are usually made much earlier, when architects choose boundaries, data movement, resilience models, platforms, and operating assumptions.

Essay · Working Knowledge9 min read

Software ArchitectureAug 13, 2026

Feature Flags: From a Boolean Switch to Enterprise Feature Management

A guided feature management series from basic release flags through OpenFeature, implementation patterns, platform choices, operations, cost, AI runtime controls, governance and enterprise architecture.

Learning Series · Quick Read5 min read

AI-Native SystemsAug 11, 2026

Self-Evolving Coding Agents: When AI Starts Learning From Its Own Engineering Work

Coding agents are beginning to move beyond one-off assistance toward systems that can learn from previous engineering work. That could change not only developer productivity, but how we think about software architecture, governance, and engineering knowledge itself.

Essay · Working Knowledge8 min read

AI-Native SystemsAug 06, 2026

How Modern AI Works: From Machine Learning to Large Language Models

A structured path of 15 lessons from AI and machine learning fundamentals through self-supervised learning, transformers, training, alignment, RAG, and fine-tuning.

Learning Series · Quick Read4 min read

AI EngineeringJul 30, 2026

AI Software Development Patterns: From Vibe Coding to Agentic Engineering

AI software development is no longer one workflow. This guide separates vibe, intent, context, spec, verification, agentic, multi-agent, and autonomous approaches so teams can choose the right level of structure and autonomy for the work.

Guide · Deep Dive15 min read

AI EngineeringJul 23, 2026

From Vibe Coding to Agentic Development: What Actually Improves AI Generated Software?

I tested vibe, intent, context, spec, and agentic development on the same software tasks. The results suggest that reliable AI development depends on three separate things: information quality, observability, and bounded repair opportunity.

Essay · Deep Dive14 min read

Platforms & DevExJul 16, 2026

The New Software Bottleneck: When the Constraint Keeps Moving

AI is compressing more of the software delivery lifecycle. As generation, review, testing, security analysis, release validation, and operations become increasingly agentic, the industry's challenge is shifting from coding capacity to managing moving constraints across the engineering system.

Essay · Deep Dive13 min read

AI EngineeringJul 09, 2026

AI-Ready Repository Reference Architecture

A practical reference architecture for repositories that support planning, implementation, verification, review, stack-aware guidance, reusable skills, security boundaries, and behavioral evaluation for coding agents.

Practitioner Guide · Deep Dive12 min read

AI EngineeringJul 02, 2026

The AI-Ready Repository: Designing Codebases for Coding Agents

Coding agents do not need more prompts nearly as much as they need better repositories: explicit engineering rules, architecture context, repeatable skills, safe tool boundaries, native verification, and human approval points.

Essay · Deep Dive14 min read

Software ArchitectureJun 25, 2026

Cognitive Load Is an Architecture Constraint

We routinely design systems around limits in CPU, memory, latency, and network capacity. But one of the most important constraints in a software system is rarely shown on an architecture diagram: how much complexity a team can actually hold in its head.

Essay · Deep Dive13 min read

Software ArchitectureJun 18, 2026

Who Owns What? A Practical Guide to Architecture Roles

Enterprise Architect, Domain Architect, Solution Architect, Platform Architect, Software Architect: the titles matter less than the decisions each role owns, influences, and deliberately leaves to others.

Practitioner Guide · Deep Dive17 min read

Platforms & DevExJun 11, 2026

The AI-Native Engineering Platform

A practical reference architecture for engineering platforms that must serve both human developers and AI agents—without giving either a shortcut around identity, policy, evidence, or production controls.

Decision Guide · Deep Dive14 min read

AI-Native SystemsJun 04, 2026

The SHIELD Architecture Model

SHIELD explores a simple question: should a resilient system trust the number of failures it sees, or the independence and diversity of the evidence behind them?

Architecture Model · Working Knowledge11 min read

AI-Native SystemsApr 19, 2026

aeg-intent-gate: A Practical Approval Gate for AI Tool Calls

A small TypeScript package that turns AI tool calls into governed intents, decisions, and ApprovedCommand objects before execution.

Project · Quick Read5 min read

AI-Native SystemsApr 10, 2026

Decoupling Intelligence from Authority: A Foundational Principle for Governed AI-Native Systems

A canonical architectural principle for AI-native systems: intelligence may generate intent, but only an independent governance layer may authorize execution.

Foundational Principle · Working Knowledge9 min read

AI-Native SystemsDec 31, 2025

The Agentic–Event–Governed (AEG) Architecture Model

The Agentic–Event–Governed (AEG) Architecture Model separates autonomous intent from execution authority through structured intent, observable coordination, and independent runtime governance.

Canonical Model · Working Knowledge9 min read

AI-Native SystemsDec 31, 2025

Agentic–Event–Governed (AEG) Architecture Model — Technical Paper (v1.0)

The full technical paper for the Agentic–Event–Governed (AEG) Architecture Model, including formal definitions, system layers, and governance rationale.

Technical Paper · Deep Dive18 min read

AI-Native SystemsDec 02, 2025

Agentic AI: When Software Starts Taking Action

The important shift in agentic AI is not that models can reason better. It is that software is beginning to decide what to do next and act on those decisions.

Essay · Working Knowledge8 min read

Platforms & DevExNov 28, 2025

Platform Engineering 2.0

Platform Engineering 1.0 built paved roads for developers. Platform Engineering 2.0 is becoming the control plane through which humans and AI agents build, deploy, operate, and govern software.

Essay · Working Knowledge11 min read

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Projects

Architecture and engineering resources organized around the problem you are solving.

Explore the library

Jev AI Explained: When AI Needs to Decide, Not Generate

The Architecture of Waiting: Why Queue Depth Is a Business Metric

Your Dependencies Are Part of Your Architecture

Portability Is Not Resilience: Rethinking Cloud Concentration Risk

Inside Modern AI Search: Embeddings, Hybrid Retrieval, RRF, and Reranking

A Practical Architecture Decision Framework

How AI Systems Get Work Done: From Tools and Loops to Agents and Graphs

Feature Flags at Enterprise Scale: A Practitioner’s Guide to Choosing the Right Approach

Cost Is an Architecture Property, Not a Finance Problem

Feature Flags: From a Boolean Switch to Enterprise Feature Management

Self-Evolving Coding Agents: When AI Starts Learning From Its Own Engineering Work

How Modern AI Works: From Machine Learning to Large Language Models

AI Software Development Patterns: From Vibe Coding to Agentic Engineering

From Vibe Coding to Agentic Development: What Actually Improves AI Generated Software?

The New Software Bottleneck: When the Constraint Keeps Moving

AI-Ready Repository Reference Architecture

The AI-Ready Repository: Designing Codebases for Coding Agents

Cognitive Load Is an Architecture Constraint

Who Owns What? A Practical Guide to Architecture Roles

The AI-Native Engineering Platform

The SHIELD Architecture Model

aeg-intent-gate: A Practical Approval Gate for AI Tool Calls

Decoupling Intelligence from Authority: A Foundational Principle for Governed AI-Native Systems

The Agentic–Event–Governed (AEG) Architecture Model

Agentic–Event–Governed (AEG) Architecture Model — Technical Paper (v1.0)

Agentic AI: When Software Starts Taking Action

Platform Engineering 2.0

The article is not always the endpoint.

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