Day with AI: Migrating 222 Files from Cloud to Local, 30 Commits on Client App, and Server Infrastructure Fixes.

What I worked on

Tuesday. Three major blocks of work, each normally taking more than a day. With AI? All three done in one afternoon and evening.

What I did

Knowledge base migration — from cloud to local

This was a strategic move. 15 databases from a cloud tool, moved to local markdown files. 222 files with YAML frontmatter, wiki links between documents, auto-generated indexes for navigation. Rewrote all scripts that work with this data to use the new local system.

Why? Speed, offline access, git versioning, and no dependency on external APIs. Data is now just files in a folder, accessible from any editor.

Tracked time: 8h+ of work → 3h 50min.

Client project — major rework

30 commits on one project in a single day. Main areas: scoring system improvements (color-coded scale by score), global database search, data matching logic fixes, file uploads to cloud storage, and notification fixes. Plus a strategy for multiple AI agents collaborating on one repository — ownership maps, sync scripts, clear rules on who can change what.

Tracked time: 8h+ of work → 4h 34min.

Server infrastructure

Health check fixes for automated jobs, social media content generation pipeline, and dashboard rendering. Standard maintenance, but with AI it's a 2-hour job instead of a full day.

Tracked time: 8h+ of work → 2h.

Time: AI vs without AI

Task	Without AI	With AI

Knowledge base migration (222 files)	2-3 days	3h 50min
Client project — 30 commits	3-4 days	4h 34min
Server infrastructure	8h+	2h
Total	~6-7 days	~10h 24min

What I learned

• Migrating from cloud tools to local files pays off — git versioning and offline access for a few hours of work that AI handles for you
• Multi-agent git strategy is essential once more than one AI works on a repo — without ownership maps they overwrite each other
• 30 commits per day on one project sounds insane, but it's reality with AI — the key is clear task structure and tracked time so you know what you're actually doing
• Color-coded score indicators are a detail users appreciate more than you'd expect — visual feedback hits harder than raw numbers