Don't Trust. Verify. Quality-First AI Development

AI Got Faster. But Did Working Code Increase?

Claude Opus 4.6, Codex 5.3. Models evolved, agents multiplied, processing became incredibly fast.

Flashy demos, parallel execution, autonomous coding.

But have you ever thought this while actually doing AI coding?

"While I sleep, a high-quality product gets completed, and when I wake up, it's out in the world."

...Is that actually happening?

Here's my reality.

I wake up and check the code I left to AI last night. It doesn't work. Full of bugs. Hallucinations. Calling APIs that don't exist.

In the end, time spent debugging has increased compared to before using AI.

Tools competing on speed have multiplied. But have tools that produce "working code" increased?

So I changed my approach.

Not speed. Quality. All in.

Don't let AI run free. Control it thoroughly. Pack in obsessive quality checks.

That's how I built Cognix.

Cognix has 8 quality assurance mechanisms:

Two-Layer Scope Defense - Eliminate AI "overreach"
Formal Proof - Won't execute without proof
Structural Integrity Check - Detect and repair invisible structural breakdown
Validation Chain - Auto-eliminate framework-specific bugs
Multi-Stage Generation - Maintain consistency in large projects
Post-Generation Validator - Auto-complete missing files
Runtime Validation - Never return non-working code
25-Type Comprehensive Review - Catch what lint misses