Ideas Weave Every Narrative with AI.
Most AI-generated documents are unreadable because the model is designed for reasoning, not for natural language. The fix isn’t better prompts—it’s choosing the right model and ruthlessly pruning the context you feed it. A product manager’s hard‑won lesson from testing Grok, Claude, and GPT on real project docs.
Colors’ Package Skills Catalog is generating confusion with its galaxy-brain documentation. But that’s not a bug — it’s a signal. The real product is the communication layer, and it’s optimized for AI agents, not humans. If you can’t explain it to a colleague, the tool’s power doesn’t translate into leverage.
AI agents hallucinate not because models are dumb, but because they lack real-time access to current documentation. An MCP server bridges that gap, turning agents from stale-training-data guessers into grounded retrievers. The real strategic asset isn’t the model — it’s the documentation layer. Whoever controls clean, machine-readable context controls how useful AI becomes.
Most documentation is terrible not because writers lack skill, but because they mix tutorials, how-to guides, explanations, and references into one confusing mess. Diátaxis fixes this by separating content into four distinct cognitive modes—learning, doing, understanding, referencing—so readers get exactly what they need, when they need it. If you’ve ever struggled with unclear docs, this framework will change how you write and consume them.
Most teams treat specifications as static artifacts that inevitably decay. Instead, embed spec quotes directly into the implementation code, creating a self-verifying, bidirectional link between requirements and execution. This simple practice makes maintenance trivial and coverage checking automatic. No more hunting for where a spec change impacts the codebase.
AI-generated text is bloated and ambiguous. By forcing AI agents to write in ASD-STE100 Simplified Technical English, we reverse the problem: using extreme complexity to achieve extreme simplicity. The result? Crisp, unambiguous instructions that save time and reduce errors. This isn’t about making AI smarter—it’s about making it shut up and say exactly what it means.
Simplified Technical English limits vocabulary to 900 words and bans synonyms — not to dumb things down, but to make misreading impossible. Born in aerospace, STE cuts translation costs by up to 40%, eliminates interpretation drift across global supply chains, and creates documentation that holds up in court. Most companies ignore it. The ones who don’t are the ones who can’t afford ambiguity.
As AI generates code, READMEs become the last bastion of human intent. To maintain trust, developers must explicitly flag AI-generated content. The Human README is a warning label that inverts the traditional software hierarchy—code is no longer truth, the disclaimer is.
Most developers hate writing documentation. But here’s the truth: writing documentation first is the single most effective way to build software that actually works. It forces clarity, prevents disaster, and saves you from the nightmare of retrofitting docs to a tangled codebase. The code will always lie to you. The documentation won’t, if you write it first.
AI coding assistants routinely ignore your carefully written documentation. The problem isn’t your writing — it’s that agents have no incentive to read it. Trigger-tree, an open-source tool, now offers a CI gate that checks whether your AI agent actually opened the docs you intended it to. If an AI doesn’t read your docs, they are effectively dead code. Writing for AI requires enforcement, not just authorship.