Carbon Chooses, Silicon Scales
AI may become better than humans at many forms of thinking without replacing humanity. The real question is whether the intelligence extending us will remain accessible to everyone.
LogCTL — AI Systems, Engineering & Linux Deep-Dives
A control interface for engineering thoughts.
LogCTL
Deep-dives on AI systems, software engineering, Linux, infrastructure, and developer tooling — by Ravi Kumar Singh.
AI Feels Faster. The Data Is Less Certain.
AI coding tools feel transformative, but the productivity evidence is mixed. Part 2 of the LogCTL series on AI-native engineering examines the gap between perceived speed and measured outcomes, and why review is becoming the new bottleneck.
AI-Native Software Engineering Isn’t About AI
AI-native software engineering is not defined by how much AI a company uses. It describes a development model in which AI is treated as a primary participant in software construction rather than an occasional assistant.
The Future of Software Engineering Is an Operating Model
AI-native engineering is not defined by removing humans from software development. Part 5 of the LogCTL series examines how roles, teams, economics and accountability change when implementation is no longer the dominant activity.
The Model Is Only One Part of the System
A useful AI-native workflow requires more than a better prompt. Part 3 of the LogCTL series maps the practical method: specifications, context engineering, agent workflows and the harness that enforces permissions, boundaries and stopping conditions.
When Agents Scale, Coordination Becomes the Product
One agent can misunderstand a requirement. Several agents can misunderstand it in different ways. Part 4 of the LogCTL series on AI-native software engineering maps coordination, multi-agent orchestration, layered verification and governance at scale.
New to Tiling Window Managers? Start With Omarchy
Omarchy removes the dotfile rabbit hole and lets beginners experience a complete Hyprland workflow before deciding what they want to customise.
The Model Is Not the Development Environment
I spent 12 million input tokens on a small monorepo change. The problem was not the model. It was the development environment around it.