The Kryotta blog
Guides and comparisons on working with every AI model — Claude, Gemini, Llama, Mistral, DeepSeek and more — plus what we're building.

I used 3 AI models to review 45 pull requests: which one for bugs, style and tests
I split code review across three AI models—Claude for logic bugs, Gemini for style, DeepSeek for tests—and reviewed 45 PRs to see which one actually catches what matters.

I used 3 AI models to review 60 pull requests: checklist for which model per task
I tested Claude, Gemini, and DeepSeek on 60 real pull requests. Each model excelled at different tasks—here's the checklist for which one to use when.

I used 3 AI models to review 25 TypeScript pull requests: which one caught the VAT rounding bug
I tested Claude, DeepSeek, and Gemini on 25 TypeScript pull requests. DeepSeek caught a VAT rounding bug that would have cost a client thousands—here's how each model performed.

I used 3 AI models to review 40 pull requests: which one caught the logic bugs
I tested Claude, Gemini, and DeepSeek on 40 real pull requests from a Shopify app. Claude caught 9 logic bugs—including a critical multi-tenant security flaw. Here's how they compared.

The developer checklist for code review with AI: which model per task and why
A freelance developer shares how routing different code review tasks to specialized AI models—instead of using ChatGPT for everything—caught bugs that would've cost clients money. Here's the checklist that works.

I used AI code review on 50 pull requests: which model caught the actual bugs
I tested Claude, Gemini, DeepSeek, and GPT-OSS on fifty real pull requests from Nairobi startups. Here's which models actually caught production-breaking bugs versus just complaining about formatting.

Six questions developers actually ask about using different AI models for each task
Claude Sonnet catches bugs in code review. Gemini Flash scaffolds features faster. Using the same model for both wastes time and money—here's what actually works.

5 myths developers believe about AI coding tools (and what actually works in 2026)
Developers often route every task through one AI model and expect it to excel everywhere. In reality, matching the right model to the right task—reasoning for debugging, fast models for quick questions, specialized tools for code review—is what actually works.