The Engineer's Journal
Explore
Browse current investigations, deep dives, mental models, and durable engineering knowledge.
Steelmanning and Strawmanning: Building Arguments That Hold
Why defeating a weak version of someone's view proves nothing, and how restating the strongest version first makes your own arguments sharper and fairer.
Goodhart's Law: When the Measure Becomes the Target
Why any measure, once it becomes the goal people are judged by, stops accurately measuring the thing it was built to track.
Falsifiability: The Test for Real Information
A claim only carries weight if you can say what evidence would prove it wrong. Learn to tell testable claims from statements that can never lose.
Thinking With AI: Setting Up Your Workspace
A practical guide to organizing AI sources, instructions, personal understanding, and verification into a workspace that supports better thinking.
Thinking With AI: Students
A worked example of how a student can use AI to extend learning while keeping the actual thinking, explanation, and judgment her own.
Building the Extended Mind Thesis and First Principles of Thinking With AI
A practical mental model for using AI as an extension of thinking through clear prompts, organized data, and a disciplined loop of questioning and revision.
The Extended Mind Thesis: Where Does Thinking Actually Happen?
A mental model that treats cognition as extending beyond the brain into the body, tools, and environment rather than stopping at the skull.
The Latticework: Why One Mental Model Is Never Enough
A way to combine mental models from different disciplines so problems are tested through several lenses instead of one narrow frame.
The Confirmation Bias
A cognitive bias where people favor information that confirms their preexisting beliefs.
First Principles Thinking
A mental model for reasoning from fundamental truths by separating what is actually known from what is merely assumed.
Who Is an Engineer?
A working definition of engineering as a practice, not a credential, and a map of what this Journal is for.
The Essence:Two Paths of Processing
GPU Processors for Laymen
The Architecture of Belief: From Bayesian First Principles to Silicon Systems
A mental model for updating engineering beliefs as new evidence changes what is plausible.
Why Distributed Systems Fail in Unexpected Ways
An investigation into cascading failures, partial failure, retry storms, and the second-order behavior of distributed systems.
Evaluating AI Systems as Engineering Instruments
A practical investigation into treating AI evaluation as measurement, not as a ceremonial checklist.
How Queue Backpressure Works
A deconstruction of queues as shock absorbers, pressure signals, and hidden failure surfaces in distributed systems.
Building a Small Observability Lab
A build log for a local service playground designed to make latency, retries, and queue depth visible.
Post-Mortem: The Retry Storm That Looked Like a Capacity Problem
A failure analysis of a dependency outage where well-intended retry logic amplified load and delayed recovery.
Feedback Loops
A model for seeing how systems observe state, react, and sometimes amplify the conditions they are trying to correct.
Redundancy
A reusable way of thinking about reliability by adding backup capacity, alternate paths, or duplicate capability.
Second-Order Effects
A discipline for asking what a decision will cause after the immediate effect has already happened.
Trade-offs
A way to reason about engineering choices where improving one property changes cost, complexity, risk, or behavior elsewhere.
Field Note: A Latency Budget Is a Design Constraint
A short observation on why latency budgets make distributed design conversations more concrete.
Investigation: What Should an AI Evaluation Actually Measure?
An open investigation into useful evaluation signals for AI systems that operate inside real products.
Foundations of Systems Thinking
A route through concepts, mental models, and articles for reasoning about complex software and infrastructure systems.
Artificial Intelligence
Intelligent systems studied as engineered, evaluated, constrained, and operated artifacts.
ARPANET Packet Switching
A historical event that showed how resilient network architecture emerged from constraints and failure assumptions.
CAP Theorem
A principle describing trade-offs between consistency and availability during network partitions.
Cognitive Biases
Recurring shortcuts in human judgment that can distort how we notice evidence, assess risk, and make decisions.
Consensus
A family of techniques for getting independent components to agree on state despite failures.
Containers
A deployment technology for packaging processes with their runtime dependencies and isolation boundaries.
Distributed Systems
Systems whose components coordinate across network boundaries while tolerating latency, disagreement, and partial failure.
Economics
The study of incentives, scarcity, trade-offs, and the systems that shape collective choices.
Fault Tolerance
The ability of a system to continue providing useful behavior when parts of it fail.
Geopolitics
How geography, institutions, technology, resources, and power shape the conditions in which systems operate.
Infrastructure
The shared technical foundation that allows systems, teams, and societies to depend on services at scale.
Kubernetes
A control plane for scheduling, reconciling, and operating containerized workloads.
Leadership
How groups set direction, make decisions, learn from outcomes, and coordinate under uncertainty.
Message Queues
Infrastructure for decoupling producers and consumers through durable or buffered messages.
Observability
The ability to infer what a system is doing internally from the signals it emits.
Software
The discipline of designing, building, operating, and evolving executable systems under real constraints.