Essays on AI coding agents, spec management, team alignment, and the future of software development.
When Red Hat adopted spec-driven development for their AI coding workflows, AI-generated code accuracy jumped from ~60% to 95%. Here's what changed — and what your team can learn from it.
Your project management tool was designed for human developers. AI agents need something different. Here's why Linear, Jira, and Notion aren't enough for teams building with AI.
A step-by-step walkthrough of using Claude Code with Colign's MCP server to go from a structured spec to a working implementation — without copy-pasting a single line.
Acceptance criteria define 'done.' When AI agents implement your specs, precise Given/When/Then scenarios are the difference between code that works and code that almost works.
Most software specs are written for humans. But in 2026, your spec's most important reader is an AI agent. Here's a template and guide for writing specs that work for both.
AI agents are only as good as the context they receive. Learn how the Model Context Protocol (MCP) and structured specs create a reliable pipeline from team decisions to AI-generated code.
When every developer feeds their AI agent a different version of the requirements, you get different implementations. Here's why a single source of truth changes everything.
Spec-Driven Development (SDD) is emerging as the standard methodology for teams that use AI coding agents. Here's what it is, why it works, and how to adopt it.
We've been optimizing the wrong thing. Better prompts produce marginally better code. Better specs produce fundamentally better code. Here's why the distinction matters.
AI coding agents produce buggy code not because the models are bad, but because the input is bad. The real problem is upstream: vague, incomplete, and fragmented requirements.
Vibe coding is the fastest way for a single developer to build something. It's also the fastest way for a team to build the wrong thing. Here's why, and what to do instead.
Colign's core is open source under AGPL-3.0. Here's why we chose this model, what it means for self-hosting teams, and how we build a sustainable business on top of open source.
AI agents write code in minutes. But the human decision loop — writing specs, reviewing, approving — takes days. Here's how to identify and eliminate the bottleneck.
Most specs die the moment they're written. The Colign Loop keeps them alive through the entire development lifecycle — from proposal to AI dispatch to verification.
Structured specs. Team alignment. AI implementation. Open source.