808 Commits in 30 Days: AI Development Velocity at Scale

February 15 to March 18, 2025. Thirty days. 808 updates shipped across 26 different projects. One person — me.

I know how that sounds. So let me explain what it actually means, why working this way gets faster over time instead of slower, and what it tells you about where business is heading.

What 808 Updates Across 26 Projects Actually Looks Like

Think of each "commit" as a small package of finished work. Some were quick fixes. Some were entire new features. The average was about 27 per day, including weekends where I worked shorter hours.

The 26 projects broke down like this: 8 were for clients — a financial advisory firm, a fitness coaching platform, a labor compliance company. 10 were my own products and tools, including the systems that run my DTC fashion brand here in San Diego. The remaining 8 were behind-the-scenes work that keeps everything running smoothly.

That mix is important. Client work funds the internal work. Internal work creates shortcuts that speed up client work. Everything feeds everything else. It's a flywheel.

A few months before this sprint, I'd written about doing 72 updates across 10 projects in a single week. The obvious question was: does this pace hold up, or does it fall apart?

It doesn't just hold up. It accelerates.

Why Project 26 Was Faster Than Project 1

This is the part most people miss about AI-powered work. The value isn't that one project goes faster. It's that every project makes the next one faster.

Here's a simple analogy. Imagine you're a chef opening restaurants. The first one takes a year — you're figuring out recipes, supplier relationships, kitchen layouts, everything from scratch. By your fifth restaurant, you've got a playbook. You know what works. Opening number five takes a quarter of the time.

That's what happened here, but compressed into 30 days.

By the third project, I had a proven security and login system. That system got reused in projects 7, 12, and 19 with small tweaks. What took 4 hours to build right the first time took 20 minutes the fourth time.

A pricing system I originally built for my own brand — the one that automatically sets prices for 564+ products — became the blueprint for a client's pricing system. Different business, same bones.

My personal toolkit of reusable code grew to 22,000+ lines during this period. Each addition is small, but each one eliminates a whole category of future work. I also set up a system where different AI tools handle different jobs — one that's great at writing handles the text, one that's great at images handles the visuals, and they hand work off to each other automatically. That system stabilized in week one and then just ran.

Here's the math that matters: Project 1 took about 12 hours to get to a working first version. By Project 20, the same level of complexity took about 3 hours. Not because I was typing faster. Because 60% of the building blocks already existed.

A Typical Day — Less Glamorous Than You'd Think

Morning was 4-5 hours of focused building. I'd pick the hardest problem and work through it with AI as my partner. I made the decisions about what to build and why. The AI handled the repetitive parts, suggested approaches based on work I'd already done, and caught mistakes I'd miss when moving fast. About 60% of the day's work happened here.

Afternoon was 2-3 hours of connecting systems and testing. This is honestly where AI saved the most time. Finding and fixing bugs used to mean an hour of detective work. Now it takes 15 minutes because the AI can see the whole project, recent changes, and error messages all at once. It's like having a mechanic who already knows every part of your engine.

Evening was about an hour of reviewing what shipped and planning tomorrow.

Total: 8-9 focused hours. Not 16-hour hustle culture. The difference is that AI handles the tasks that used to eat 40% of my day — the repetitive stuff, the documentation, the tedious testing. My brain stays on the decisions AI genuinely can't make.

What Broke — Because Not Everything Worked

Of 26 projects, 4 had real problems. I think being honest about that matters more than pretending everything was perfect.

Two got abandoned. One was built on a wrong assumption about what customers wanted. I built a working version in 6 hours, showed it to potential users, and learned they didn't need it. Six hours feels like a waste — but two years ago, that same bad bet would have cost 3-4 weeks before I learned the same lesson. Failing fast and cheap is one of the most underrated benefits of speed.

Two others needed significant do-overs. One had security shortcuts I took while moving too fast. The other had a foundation that worked great as a prototype but couldn't handle real-world traffic. Both cost me about a day each to fix.

The lesson: speed makes good decisions better AND bad decisions worse. When you can build something in a day, you can also build the wrong thing in a day. The quality control systems I use now came directly from these failures.

And yes, the 808 number includes work on projects that went nowhere. That's real life.

Where AI Actually Helped — and Where It Was Useless

I talk to a lot of business owners who've been burned by AI promises. So here's the honest breakdown.

Where AI was genuinely 10 times faster: The repetitive, predictable work. Building standard features, writing tests, creating documentation, reorganizing code. Tasks that used to take hours now take minutes.

Where AI helped maybe 20%: Complex business decisions that need to be turned into software. AI can help me build it, but the thinking about what to build is still entirely human.

Where AI was completely useless: Understanding why a client is really asking for something. Deciding which project matters most on any given morning. Negotiating when a project is growing beyond the original plan. Any decision that requires reading people.

The pricing strategy for a client's product line? That was 100% my thinking. AI just formatted the output into a clean document.

What This Means for Your Business

The point isn't that every business owner should be making 808 updates to code. Most shouldn't write code at all.

The point is this: a one-person team with the right AI tools can now produce what required 5-8 people two years ago. That's not a prediction. That's what I just documented over 30 days.

My own DTC fashion brand saw +38% revenue per employee and -42% manual work time after putting these systems in place. Products that used to take 3-4 hours to go from concept to live on the website now take 20 minutes. Those aren't code statistics. Those are business outcomes on a P&L.

For a company doing $5M-$50M, this means your competitors who figure this out first will move faster, ship more, and run leaner. The gap between companies using AI well and those that aren't is widening every month.

Thinking About AI for Your Business?

If any of this resonated — the compounding, the honest failures, the real numbers — I'd like to hear what you're working on. I do free 30-minute discovery calls where we look at your operations and identify where AI could actually move the needle. No slides. No pitch deck. Just a conversation about what's possible.

Book a Discovery Call