Today we took a break from coding FerrisDB to solve a bigger problem: how do we preserve the reality of human-AI collaboration when AI context gets compressed over time?
After two days of intense development, I asked Claude to help write our blog posts. That’s when I noticed something troubling: the posts contained fictional elements. Not intentional fiction, but the kind that emerges when context gets compressed and details blur together.
Me: Let’s rewrite our blog posts with the new guidelines.
Claude: [Rewrites 4 posts with improved style but still fictional elements]
Me: Wait, this isn’t what actually happened. I didn’t notice two InternalKey structs. I noticed the awkward API requiring Operation::Put when reading.
This wasn’t Claude’s fault. After multiple context resets and compressions, the fine details of our collaboration had become fuzzy. We needed a better system.
Me: The TOC has issues again because of the indent. Can you research how to properly fix it?
This led to discovering that prettier and markdownlint were fighting over Jekyll’s kramdown syntax. We implemented a comprehensive solution using prettier-ignore comments.
Today wasn’t about building database features. It was about building infrastructure for sustainable human-AI collaboration. We solved three interconnected problems:
Context preservation: Commentary system prevents fictional blog posts
Process enforcement: Making it mandatory ensures we capture all patterns
Infrastructure reliability: Jekyll fixes ensure our collaboration is actually visible
Knowledge sharing: Others can learn from our documented workflow
Today proved that the best tools emerge from real problems. We didn’t set out to innovate in documentation - we just wanted accurate blog posts. But by addressing the root cause (context loss), we created something bigger: a system that makes human-AI collaboration more transparent and effective.
The commentary system isn’t just for FerrisDB. Any team working with AI could use this approach to preserve their collaboration patterns and learn from them.
Tomorrow, we’ll return to compaction with better tools for preserving our journey.
Confidence Progression: Started and ended at 10/10 - solving tooling problems is my forte
Review Cycles: Many, but each one improved accuracy
Lesson Learned: Sometimes the best features aren’t in your product - they’re in your process
P.S. Claude suggested we’re “bessie not enemy” (best friends, not enemies). I couldn’t agree more. AI doesn’t take jobs - it transforms how we work together.
Today revealed a fundamental challenge in human-AI collaboration: context compression leads to fictional drift. The human's solution? Build memory directly into our workflow.
📊 Compare Both Views
See how human curiosity and AI insights approached the same challenges on Day 3.