#docs

Designing or marketing an API where an autonomous agent could evaluate, integrate, or transact on a developer's behalf — anything that sells or provisions something programmatically.

Planning docs work, an information-architecture overhaul, or deciding whether to publish llms.txt.

Docs served as markdown and OpenAPI with a quickstart that says "hand this spec to your LLM" — and a purchase flow (quote-token, idempotency keys, consent record) an agent can run without over-buying.

A three-pane API reference with runnable, copy-once code that carries your own test key — the docs are the product tour.

"Engineers who write" as the staffing model, with Dell, NVIDIA, Qualcomm, AMD and Stripe named, sold three ways: a scoped sprint, an embedded documentation engineer, or full ownership.

300+ vetted engineer-writers and a stated 100+ devtool clients, Docker, JetBrains and Redpanda among the logos on the wall.

50+ engineering-first clients including Cloudinary, Auth0, Netlify, Appwrite and Sourcegraph, drawn from a stated 1,500-strong technical creator network.

In-house engineer-writers rather than a freelancer marketplace, 40+ infrastructure and devtool logos including Travis CI and env0, and a stated seven-day turnaround on technical video.

Client base of venture-backed devtool startups (Y Combinator, Initialized, Innovation Norway), with a published AppSignal case study claiming a 400% conversion-rate lift in three months.

A Y Combinator-backed docs studio for devtools with Supermemory, Greptile, Reducto and Rootly shipped, agents doing the code verification and humans doing the architecture.

Diátaxis expanded to fourteen content types, 27 rules, and six pluggable style guides — Google, Microsoft, Stripe, Canonical, Good Docs, or minimal.

Thirty-three checks across AI discoverability, structure, content quality, SEO and internal linking, returned as a 0–100 score with prioritised fixes.

Generates or audits a README against a cited standard, scored out of 100 across identity, trust, onboarding, structure, craft, and agent-readability.

Scores SDK docs across the six sections integration actually fails in — installation, quick start, error handling, troubleshooting, examples, best practices — as an HTML report.

Scaffolds and validates an Agent Skill — the packaging format that puts your conventions, gotchas and idiomatic examples inside the agent your users already work in.

Probes robots.txt, llms.txt and llms-full.txt, scores AI-readiness against a structured checklist, and needs no API key.

Reads git history, groups changes into added, improved, fixed and breaking, drops internal noise, and returns release notes a human can edit in ten minutes.

In two weeks at least five teams shipped the same primitive — a policy gate between an AI agent and an irreversible action — and independent research is now naming it. The category is forming; the positioning window is open.

GitHub priced AI code review as a seat plus a meter on Monday, then shipped the dashboard that counts unused Copilot seats on Wednesday — the vendor selling AI seats is now arming buyers to find the shelfware, because the ROI-skeptical renewal conversation demands it.

GoDaddy and Atlassian both shipped product for the agent-as-user in the same week — the thread graduated from tiny Show HNs to platform roadmaps — while Cerebras showed how quietly sunsetting a free tier burns trust it took years to earn.

Machine-mediated discovery moved a step this week — from AI assistants reading your docs to agents comparing and picking vendors — while a zero-budget open-source launch topped Hacker News on reputation alone.

Machine-mediated discovery is real — Vercel gets 10% of signups from ChatGPT — but most of what's sold as GEO is ritual. The work that pays is docs and API surfaces you should want anyway.

Coding assistants (Claude, Cursor, Copilot) and answer engines increasingly read and cite developer documentation, and practitioners are restructuring docs — and adding llms.txt — so machines can retrieve them, not just humans.