It’s common and correct to say that “all code is technical debt”. Adding code is a necessary evil for developing new features: you almost always have to do it, but each line of code adds to the complexity and maintenance burden of the system. All future changes to the system have to work with the existing code, or at least avoid breaking it. Once systems accumulate enough code, they become impossible for a single person to understand: instead of reading the code and understanding what it does, you must rely on guesses, theories and heuristics1. Sensible engineers write as little code as possible.
How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt
The term technical debt is often used to refer to the accumulation of design or implementation choices that later make the software harder and more costly to understand, modify, or extend over time...
Comprehension Debt - the hidden cost of AI generated code.
Comprehension debt is the hidden cost to human intelligence and memory resulting from excessive reliance on AI and automation. For engineers, it applies most to agentic engineering.
The 80% Problem in Agentic Coding
Managing comprehension debt when leaning on AI to code
You’re Not Taking On Enough Tech Debt
TL;DR - The cost of servicing technical debt is plummeting because of LLMs; assuming coding models keep improving. You're better off taking more technical de...
The Five Levels: from Spicy Autocomplete to the Dark Factory – Dan Shapiro's Blog
In my last post, I wrote about technical deflation. We’re seeing the cost of code is dropping so fast that we need to change our tech debt payment plans. The smart teams…
Prompt Engineering Guide – Nextra
A Comprehensive Overview of Prompt Engineering
Code Is Cheap Now, And That Changes Everything | Pere Villega
AI coding agents have made code production nearly free. Drawing on insights from Kent Beck, Paul Ford, and Simon Willison, this post argues that the value has shifted from writing code to defining systems — contracts, invariants, SLAs, and verification.
Research Debt
Science is a human activity. When we fail to distill and explain research, we accumulate a kind of debt...
AI-generated code is 'pain waiting to happen'
The boom is piling up technical debt, warns Lightrun's Moshe Sambol
Ten FOSS Development Fallacies For User Facing Software
This is a list, inspired by the Five Geek Social Fallacies, of common patterns or behaviours that developers of free/open source software (FOSS) fall into when they try to develop software for users who are not technical, or for whom non-technical users would be the most obvious userbase. Hopefully writing these down (which frankly is cathartic more than anything) is helpful to some in recognising these thought patterns and avoiding them when developing software - I’ve got some of my own thoughts on this at the bottom.
What I Found Interesting in Claude Code's Source
A breakdown of the most interesting engineering patterns in Claude Code's leaked source code: composable system prompts, runtime instruction injection, context compression, forking, prompt caching architecture, and more.
The intent pipeline: why most prompt guides miss how people actually use AI
Most prompt guides assume that people interact with AI by carefully authoring prompts. In practice, that is rarely how AI is used. Most…
