Insights on documentation, developer experience, and best practices for building better software.
What AI-native documentation actually means, how to test it, and which platforms and frameworks are strongest for automated upkeep, agent retrieval, API docs, and source ownership.
Compare the best GitBook alternatives for developer docs, API portals, Git workflows, open-source ownership, knowledge bases, AI-readable docs, and pricing.
Compare seven ReadMe alternatives for API documentation, developer portals, SDK generation, OpenAPI governance, mixed product docs, and self-hosting.
Compare the best Mintlify alternatives in 2026 for developer docs, API docs, Git sync, AI-readable docs, MCP, pricing, and migration.
Most skill.md files fail for the same reasons: too much branding, not enough routing, and no explicit boundaries. Here is how to write one that actually improves agent behavior.
Most skill.md files are too abstract to help an agent do real work. Here are concrete patterns and templates for API docs, SDK docs, and CLI docs.
llms.txt and skill.md solve different problems. One is a map of your docs. The other is a compact playbook for how an agent should use them.
skill.md is not a replacement for docs, llms.txt, or MCP. It is a compact playbook that tells an agent how to use your product and where to start in your docs.
Most of the confusion around skill.md is really confusion about discovery and install paths. Here is where the file lives on your docs site, on disk, and inside agent-specific skill directories.
Adding llms.txt is mechanically simple. The hard part is choosing the right pages and keeping the file useful instead of turning it into a second sitemap.
The difficult part of llms.txt is not the format. It's deciding what deserves to be in the file at all.
sitemap.xml tells crawlers what exists. llms.txt tells AI agents what matters. If you run docs in 2026, you probably want both.
Most llms.txt files are either too vague or too bloated. Here are three patterns that actually fit API docs, help centers, and developer documentation.
llms.txt is a machine-readable index for documentation. Here's what it does, why it matters, and where it stops being enough.
MCP is straightforward in theory and messy in practice. This guide covers what MCP is, why remote MCP matters for SaaS products, the main deployment options, and how to get to tokenless onboarding without forcing users through local setup.
I launched a narrow product on Product Hunt without paying a hunter, influencers, or a launch consultant. It ended with 51 votes and a 19th-place finish, and the useful part was learning what organic launches actually depend on.
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.
Docs as code uses Git, plain text, reviews, tests, and deployment automation for documentation. Here is the workflow, where it helps, and where it fails.
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.