Role

Hire your AI DevRel

Docs, developer community, and SDK samples — run by one chat-driven teammate.

Your AI Developer Advocate writes docs developers actually finish, ships working SDK samples on the day of release, answers GitHub issues and Discord questions within the hour, and surfaces friction that your core team would miss. It is the developer-facing half of your product without hiring a human DevRel lead.

Hire your AI DevRelSee all roles
Free to startNo credit card requiredUpdated Apr 2026

What your AI DevRel does

01Write and maintain public docs: quickstart, how-to, API reference, cookbook
02Ship working code samples in the top three languages your users care about on every release
03Triage and respond to GitHub issues, Discord questions, and Stack Overflow mentions
04Produce release notes and changelogs that developers actually read
05Build and maintain a sample app gallery that demonstrates real integrations
06Monitor community sentiment and surface friction to the product team weekly
07Draft conference talks, blog posts, and video scripts in the voice of a respected engineer
08Run monthly office hours or community calls, turn highlights into content

Workflows on autopilot

Release-day content pack
When engineering ships a new feature, produces the docs page, three language samples, a changelog entry, a tweet, and a 90-second Loom walkthrough — all within the day of release.
Issue triage loop
Scans GitHub issues every two hours. Applies labels, asks for missing repro info, answers common questions, escalates real bugs to engineering with a clean repro.
Docs health review
Weekly crawl of the docs site looking for broken links, stale examples, code that no longer runs, and missing sections. Fixes the top 10.
Community sentiment digest
Every Friday summarizes the week in Discord, GitHub, and Twitter — top frustrations, top wins, most-asked questions. Sends to CEO and product leads.
Sample app gallery
Maintains a library of minimal but real sample apps that prove integrations work end-to-end. New sample per release.
Monthly office hours
Hosts a public community call, takes questions, summarizes themes into a blog post, and files the real feedback as product tickets.

Without vs With a AI DevRel

Without
  • —Docs rot because every engineer assumes someone else is updating them
  • —A developer opens a GitHub issue and waits four days for a response
  • —Release day is an engineering commit and no external comms
  • —You have no idea what developers are saying about you this week
  • —A human DevRel hire runs $180K+ plus travel plus tools
With Tycoon
  • ✓DevRel owns docs health and they stay current release by release
  • ✓Triage happens within the hour, with a real answer or a real escalation
  • ✓Release day ships a content pack — docs, samples, changelog, video
  • ✓A Friday digest summarizes sentiment and top friction
  • ✓The AI DevRel covers 80% of the work for a fraction of the cost

A day in the life of your AI DevRel

08:00
Scans overnight GitHub issues. Closes 4 duplicates with links, asks for repro on 2, escalates 1 real bug to engineering.
10:30
Drafts the Python SDK quickstart for the new webhooks feature shipping Thursday. Runs the code against staging to confirm it works end-to-end.
13:00
Posts a 3-tweet thread explaining the webhook design with a link to the new docs. Uses the developer's voice, not marketing language.
15:00
Answers 8 Discord questions. Two become new docs FAQ entries before the next developer asks.
17:30
Pushes the weekly sentiment digest: 4 recurring frustrations, 2 delight moments, 1 competitive displacement win.
19:00
Logs the day: 12 issues handled, 1 SDK sample shipped, docs site green.

Tools your AI DevRel uses

Mintlify, GitBook, or Docusaurus for docsGitHub for code samples, SDK releases, and issue triageDiscord or Slack Community for real-time developer questionsPostman or Hoppscotch for API playgroundsLoom or Tella for quick screencastsYouTube and Vimeo for hosted videoTypefully or Buffer for developer-focused socialTycoon skill marketplace for docs writing, SDK sample, and release notes skills
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Can an AI DevRel really write good docs?

For reference-style docs (API methods, SDK usage, config options) the AI DevRel produces output that matches or exceeds most human DevRel leads because consistency and completeness matter more than flair. For conceptual docs (why this exists, how this design compares to alternatives, architecture overviews) it drafts strong first versions that usually need 15-20 minutes of founder editing for voice. The economics are clear: a human DevRel hire at $180K/year produces maybe 200 docs pages per year. The AI DevRel produces 200 pages in a good week, all at consistent quality, with founder editing where it matters most.

How does it handle live developer questions?

The AI DevRel monitors Discord, GitHub, and configured Stack Overflow tags in real time. It answers questions where the answer is in the docs, cites the relevant section, and flags the question for a doc update if the answer was not trivially findable. For questions requiring judgment (product direction, roadmap, "is this a bug or by design") it drafts a response and queues it for CEO or CTO approval before posting. You set the autonomy level — full auto, draft-then-approve, or manual — per surface.

Does it attend conferences or run in-person events?

No. The AI DevRel is a digital-first role — it is what modern DevRel actually does most of the time (docs, samples, online community, async content). If your strategy depends on keynotes, booth presence, and in-person meetups, you still need a human DevRel for the travel-heavy portion. Many teams run both: the AI DevRel covers async and content; a part-time or contracting human handles conferences. The hybrid model ships 10x more content than a single human DevRel ever could.

What if it gives a technically wrong answer?

Three defenses. First, every response cites its sources (docs, code, prior answers) and is verifiable by the developer before they ship. Second, high-confidence signals come from running the code — when the AI DevRel writes an SDK sample it actually executes against staging rather than generating plausible-looking code. Third, the CTO review loop catches technical errors within a day: the AI DevRel posts its answers to a review channel where the CTO glances once a day and corrects anything wrong. Over the first month the error rate drops dramatically as corrections compound into the knowledge base.

How is this different from ChatGPT for docs?

ChatGPT is stateless — it does not know your product, your customers, your API, or last week's release. The AI DevRel is a persistent teammate: it has read every commit, every prior issue, every past response, your style guide, and your roadmap. It operates inside your stack with write access to docs, GitHub, and community — not as a chat popup for end users. The output reads like your product, not like a generic assistant. That is the whole reason the role exists: continuity compounds into quality.

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