A curated reading list exploring AI's impact on tech management, software development, and SRE, with links to articles and blogs.
Practical habits for using AI effectively as a software engineer, focusing on fundamentals, context, and understanding.
A curated reading list covering AI's impact on software engineering, CI/CD for coding agents, Google's Agent Executor, Go error handling, and tech trends.
Explores using AI tools like Claude and Codex to write high-quality code slowly by finding and fixing bugs in PRs.
An article arguing that speed in software engineering leads to better learning, decision-making, and overall effectiveness.
Analysis of how engineering career levels have shifted from skill progression to a focus on scope ownership and hiring expectations.
Reflection on software quality vs. speed in the AI era, emphasizing engineering and user-centric design.
Explores how AI changes the definition of a 10x engineer, shifting focus from writing more code to higher-impact decisions and judgment.
A software engineer shares personal thoughts on AI, fears, opinions, and evolving mental journey through the tech landscape.
A keynote on AI literacy for developers, exploring human-AI collaboration and the shift from code wrangling to habitat engineering.
Review of Fred Brooks' 'The Mythical Man-Month' and its enduring lessons on software project management and conceptual integrity.
Explores designing autonomous AI agent teams for software engineering, from subagents to parallel implementations.
Agent Skills project enforces senior engineering workflows like specs and tests for AI coding agents, preventing shortcuts to 'done'.
Explores the end of traditional coding, shifting from writing syntax to expressing intent and leading AI agents in software development.
Article discusses the importance of using evals to measure AI improvements when adding AI skills to your resume.
Summary of a fireside chat at Sequoia Ascent 2026 discussing AI agents, Software 3.0, and the shift to agentic engineering.
Analysis of rising token costs in AI coding tools at tech companies, with insights from engineers on budget impacts and potential solutions.
Explores why Enterprise AI is an engineering challenge, not the end of software engineering, emphasizing system integration and control.
Explains why tech leaders should adopt proven solutions instead of building custom systems, focusing on cost, maintenance, and competitive advantage.
Analysis of cognitive debt in AI-driven development, comparing manual and infrastructural solutions proposed by researchers and platforms.
