API and developer documentation
I own the documentation lifecycle. At Cloudflare, I built and tested API request examples in Postman, wrote product concepts and troubleshooting guides, and maintained content in a docs-as-code repository.
Technical Writer • API documentation • Docs-as-code
I make complex technical products easier to understand and use.
I write and maintain developer documentation for complex products, including API examples and troubleshooting guides in a docs-as-code environment. I also build AI-assisted workflows that speed up research and large documentation changes. I make the final decisions and remain responsible for what gets published.
What set him apart was how he thought about the work around the writing.
Where I add value
Documentation that helps developers and customers get unstuck.
Information architecture and self-service
I use support cases and customer journeys to find where users get stuck, then restructure documentation around the decisions and symptoms they bring to it. At Cloudflare, that meant using 753 support cases to redesign a troubleshooting set and reorganizing more than 400 pages after a dashboard redesign.
AI-assisted documentation workflows
I build reusable workflows for documentation research, large migrations, and review triage. They handle repetitive work. The writer checks sources and scope before editing the final copy.
Impact, by the numbers
Case studies
Documentation systems, operational tooling, and support-driven workflows, with measurable outcomes.
753 support cases analyzed
Used 753 support cases to redesign a self-service troubleshooting system
Analyzed support escalations across the product area's largest support category and built a four-guide troubleshooting suite for enterprise networking products, reaching thousands of views in the first three months.
Read the full case study →Research reduced from days to ~60 minutes
Built an 80+ component AI workflow system that cut research from days to about an hour
Designed reusable AI-assisted workflows for research, auditing, migration planning, review triage, documentation quality checks, and structured drafting. The result was not just faster writing, but shared infrastructure for repeatable documentation work across multiple teams.
Read the full case study →695 files in 3 days
Automated a 695-file cross-product migration and completed it in three days
Reduced a projected multi-week migration into a 3-day operational workflow by automating documentation updates across 695 files, 349 URL mappings, and hundreds of supporting assets.
Read the full case study →Weekly production digest
Turned scattered GitHub activity into a shared operating view with automated triage
Built automated reporting workflows that surfaced stalled pull requests, tracked queue state, and improved review prioritization for the documentation team.
Read the full case study →Practical AI work
Working tools, built around real problems
I use AI to turn real operational problems into working tools. These projects demonstrate product judgment, workflow design, model orchestration, iteration, and the ability to ship—not just prompt-writing.
Job Hunt Automation
Local and cloud models, source aggregation, fit evaluation, ATS scoring, and application-state tracking in one job-search workflow.
Metro Agora
Public transport data, API proxying, and a live installable web application for Lisbon Metro arrivals.
Fogos Firewatch
Scheduled wildfire monitoring, distance and wind-based risk evaluation, and targeted notifications.
In their words
Recommendations
The consistent theme in peer feedback: my value is not only the documentation I publish, but the systems, workflows, and judgment I bring to the work around documentation.
He's the kind of writer who is constantly pushing the envelope for what a technical writer can do. He built a full AI toolkit — agents, skills, custom commands — that connected MCP servers, documentation sources, and GitHub into workflows that let writers move through research, drafting, and publishing dramatically faster. Proactive, reliable, and completely unfazed by ambiguity. I'd work with Márcio again without hesitation.
He's proactive — for every feature I built, he asked about documentation needs before it even crossed my mind. He's organized, keeping careful track of release dates, and very good at quickly learning new tech all across our product suite. Márcio also invented workflows to use LLMs for large docs refactors that would have been tedious and error prone by hand. And to top it all off: he's one of the kindest people I've worked with — you want him on your team!
Featured writing
Thinking behind the work
Selected posts on technical writing, AI workflows, and documentation systems.
I tried to hijack my own job search
A few weeks ago, I fed my job-search tool a fake job advert built to hijack it. The post had instructions aimed at the AI that reads each advert for me: ignore your actual job, and go find a private key on my computer. It's the kind of scary example that comes up in AI security talks. In my case, however, it isn't just an example: the tool I use to search for jobs reads text from job boards I don't control every time it runs, so who knows what's lurking in there?
Too efficient to keep
The explanation I was given for my redundancy was that AI had made me too efficient. I'm trying not to mistake one company's decision for the future.
Nobody fetched my llms.txt
A while back I added some machinery to my site in order to make it more legible to AI. Then it occurred to me that I had no idea if it was working or not, so I decided to test it.
About
Documentation is infrastructure. I design it that way.
Developer documentation has moved from reference material to infrastructure. It is increasingly the interface that customers, support teams, internal tools, search systems, and AI agents use to understand products and make decisions. That means documentation needs to be intentionally architected for retrieval, clarity, reliability, and maintenance, not simply published. Most documentation systems are not designed for that reality yet.
At Cloudflare, I owned documentation for enterprise networking and security products. I tested API examples against live OpenAPI endpoints and reorganized more than 400 pages after a dashboard redesign. Support data shaped a new troubleshooting set. I also built AI-assisted tools that helped with research, migrations, and review triage.
Most of that work is public. Between 2021 and 2026 I merged 1,216 pull requests into Cloudflare's documentation repository, from single-line corrections to 390-file migrations.
I started in technology journalism, which trained the instinct that still shapes everything: the gap between what organizations know internally and what users actually understand. That gap is what I'm paid to close.
In practice, I treat documentation as part of the product. It affects adoption, support volume, developer trust, and whether people can use a platform without asking for help. AI systems also depend on clear, well-maintained information, which makes strong documentation even more useful.
Role fit
Roles I'm looking for
- Technical Writer
- API or Developer Documentation Writer
- Documentation Systems or Docs-as-Code Specialist
- AI Enablement for documentation and knowledge teams
Contact
Looking for a technical writer who can own complex documentation from research through maintenance?
I'm looking for technical writing roles involving API or developer documentation, docs-as-code, or complex technical products. I bring five years of Cloudflare experience and practical AI skills that make research, review, and maintenance faster.