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Kevin Fealey, our Founder and CTO, submitted 18 pull requests this morning. Before lunch.
Every single one AI-generated. Every single one human-supervised. Every single one shipped to production.
PR #2443 caught my eye: "Optimize query performance for MongoDB."
The problem: Our new UI was taking 61 seconds to load vulnerability data. For a security product, that's embarrassing.
The fix: AI changed 32 files, added database indexes, generated tests, and validated everything worked.
Result: 61 seconds down to 2.5 seconds. Time invested: under an hour.
That would've taken a traditional developer more than a day. Maybe longer depending on how many files needed touching, how subtle the performance issue was, how much testing you'd need.
That's why fixing performance problems costs $5,000 to $20,000 the old way. You're paying for days or weeks of someone's time.
Kevin did it in under an hour. Here's the part that matters: Kevin doesn't code anymore.
The 400-Line Memory That Never Forgets
We have a file called Claude.md. It's 400 lines long.
Every time AI makes a mistake, Kevin adds to it. "Never do that again." The file documents everything we've learned about how AI should write code for our product, what patterns to avoid, what structures work best.
It's getting better every day. It doesn't make the same mistake twice.
This is the same approach we use in AppSecAI for vulnerability fixes. When our Expert Fix Automation (EFA) learns how your codebase handles authentication or input validation, it doesn't forget. It applies that pattern everywhere it's needed.
From Prototype to Production (Without the Bureaucracy)
Michael Cartsonis, our Founder and VP of Product, created a new user interface prototype using vibe coding. Nice demo. We showed it to customers, validated the direction.
In the old days, what happened next: create detailed specs, multiple review meetings, Figma mockups, formal approval processes, implement piece by piece, test, review, deploy. Months of work.
What actually happened: Kevin took Michael's prototype and fed it to AI along with our current product code. AI generated a seven-step implementation plan. Kevin just started implementing it. No formal process. No review meetings. No Figma mockups.
Why? Because the cost to change things is now so low that getting it perfect upfront doesn't matter. If something's not right, Kevin fixes it in 20 minutes.
Michael was surprised: "It's kind of odd that Kevin is just implementing this based off my prototype that isn't even finished yet."
That's exactly the point. Our cost of change is so small we don't need perfection upfront.
What Kevin Actually Does Now
Kevin examines fixes. Looks at the code. Reviews the tests. Validates everything makes sense. Pushes the button.
This morning's work? All those database optimization changes across 32 files? All automatically tested. All automatically documented. API tests generated and executed. Config validated. The whole QA process automated.
Kevin's job is supervision. Making sure AI is doing what we need. Catching edge cases. Maintaining that 400-line knowledge file so we get better over time.
Sound familiar? It's exactly how we built AppSecAI's workflow. Security teams review AI-generated fixes before they hit developers. They validate the triage. They approve the remediation. Then the pull requests go out.
Humans make the judgment calls. AI does the repetitive work at impossible speed.
The AppSec Parallel
This same shift is happening in application security, except most teams haven't realized it yet.
Traditional AppSec workflow: SAST scanner finds vulnerabilities → Security team manually triages → Developers get tickets → Developers write fixes → Security reviews → DAST testing validates → Repeat for 50,000 findings per year
AI-assisted AppSec workflow (what we do): SAST/DAST scanners find vulnerabilities → Expert Triage Automation (ETA) triages with 97% accuracy → Expert Fix Automation (EFA) generates fixes → Security team reviews and approves → PRs go to developers pre-validated → Fixes merge in days, not months
The security team doesn't disappear. They become automation managers. Just like Kevin.
They're not manually triaging 50,000 findings. They're supervising AI that does it faster and more accurately. They're reviewing fix candidates, not writing every patch from scratch. They're making strategic calls about what matters.
The Bottom Line
18 pull requests before lunch. 61 seconds down to 2.5 seconds. 32 files changed. All tested. All documented. All deployed.
Kevin doesn't code anymore, and our product's never moved faster!
Your developers could be automation managers too. See how AppSecAI turns security teams into fix supervisors instead of ticket managers