We talk a lot about "Docs-as-Code," but we rarely talk about the burnout that comes with it.
A few years ago, I was working on internal metadata projects for a major data intelligence platform. I wasn't just managing a few Markdown files; I was staring down 1,400 RST pages that needed metadata generation. Doing that manually wasn't an option, it was a mathematical impossibility. So, I did what any engineer-writer would do: I wrote a script. That script became a Sphinx Content Mapper, and it saved my sanity. But the workload didn't stop there. Next came the Core releases. Every cycle, I had to analyze around 200 Jira tickets to determine what actually needed release notes. Reading 200 tickets manually, cross-referencing them with code, and drafting notes? That’s days of work. So, I built a Release Notes Generator to do the heavy lifting for me.
Then, as the solo API writer, I was constantly handed OpenAPI specs from developers that needed validation before publishing. Again, manual checks were a bottleneck. I built an OAS Validator to automate the sanity checks.
The Turning Point: Why Scripts Weren't Enough
These tools were lifesavers, and I released them as open source because I knew others were struggling too:
But then the industry shifted. Layoffs happened. Hiring freezes set in. I realized that many technical writers were in the exact same position I was: overloaded, working solo, and expected to maintain quality at an impossible scale.
I had a collection of scattered scripts, but "scattered" doesn't scale. Most writers don't want to maintain Python environments or debug scripts; they just want to do their core job — writing great documentation. I also realized I was missing the biggest piece of the puzzle: Documentation Drift. I wanted a way to detect when the code moved but the docs stood still.
Enter “SudoDocs”: The Consolidated Workflow
That is why I built SudoDocs. It isn't just a wrapper for AI; it is a consolidation of the workflow I needed to survive as a solo writer.
I designed it to solve the B2C problem for individual writers, product owners, and developers who need automation but refuse to blindly trust AI.
Here is how I approached it differently:
1. The "Human-in-the-Loop" is Non-Negotiable:
We all know AI hallucinates. A technical writer cannot afford to publish hallucinations. That’s why SudoDocs integrates a review step (via Google Docs) before anything goes live. The AI does the grunt work: reading the Git diffs, analyzing the Jira tickets, but you own the final draft.
2. Complexity Abstraction:
I know many writers shy away from "getting too technical" with their tooling. SudoDocs hides the complexity of diff analysis and prompt engineering behind a UI that just works.
3. The Future is Open (and Affordable):
Right now, I am bearing the costs of the processing, hosting, database, and AI infrastructure to make this accessible.
However, I am a firm believer in open source. My roadmap includes releasing a self-hosted, open-source package of SudoDocs. The concept of "Bring Your Own AI Key" will be central to that. If you are an organization that wants to run this on your own infrastructure and pay your own API costs, you should be able to.
Final Thoughts
We are in an era where we are asked to do more with less. SudoDocs is my attempt to give technical writers the "force multiplier" they need to keep up with development velocity without burning out.