A benchmark for docs that AI agents can actually use
Public benchmarks need explicit scoring, narrow claims, and vendor neutrality to create useful feedback loops.
Insights on documentation, developer experience, and best practices for building better software.
Public benchmarks need explicit scoring, narrow claims, and vendor neutrality to create useful feedback loops.
I spent the week redesigning DocsAlot's landing page. The harder part wasn't the CSS. It was making the same page work for humans in a browser and for agents that would rather have markdown.
The docs market looks crowded on paper, but most teams still ship docs that are increasingly unreadable to both humans and agents. This is the memo for why DocsAlot should exist.
A good CLI solves part of the problem. It does not solve cross-OS distribution, desktop client compatibility, or non-technical adoption. For Docsalot, MCP matters because it gives us one protocol surface instead of a growing pile of binaries.
Without detailed in-CLI docs, agents guess and fail. Here’s the man-file pattern we implemented, inspired by recent agent-first CLI writing, plus how matching works today and what we’re improving next.
Every documentation site on Docsalot now serves a skill.md file that AI agents can install with one command. Here's the standard, what we learned from Anthropic's best practices, and how you can implement it for your own docs.
Inference-as-a-service looks like easy revenue from the outside. In practice, it's a brutal utilization game where bad unit economics can kill you even when demand is real.
Everyone's adding MCP servers to everything. Here's how the protocol actually works under the hood, why documentation teams should care, the security risks that come with it, and what it looks like in practice.
llms.txt solves discovery. Content negotiation solves consumption. One of these matters 27x more than the other.
Your docs will be read by AI agents more than humans. Here's how to structure llms.txt, serve markdown versions, and actually get found by AI tools.
A new 'standard' for AI-powered installation has emerged. But is it solving a real problem, or is it a solution that wouldn't exist if building things wasn't so cheap now?
I spent three years watching documentation rot. Here's why it happens, what's actually changing, and the uncomfortable truth about keeping docs alive.
After six months of using AI to generate documentation, I have strong opinions about where it's magic and where it's garbage. This is that list.
I've tried every documentation generator since 2018. Most are garbage. Here's what actually works, and the hard-won principles behind why.
Learn why treating documentation as code through version control, automation, and developer workflows is the best approach in 2026.
A deep dive into why documentation drifts from code, the patterns that cause it, and the automation strategies that actually work to fix it.
Compare the best documentation automation tools in 2026, including Docsalot, and learn which solution fits your development workflow.
A brutally honest look at what makes documentation terrible, why we keep shipping bad docs, and the specific techniques that actually work.