// AI Engineering7 Things That Break LLM Apps in Productionpetarivanov.tech
AI Engineering9 min read
7 Things That Break LLM Apps in Production
The demo works on your machine. Here's what takes it down when real users show up.
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// Architecture3 Resilience Patterns That Keep a Backend Service Up When Its Dependencies Aren'tpetarivanov.tech
Architecture9 min read
3 Resilience Patterns That Keep a Backend Service Up When Its Dependencies Aren't
Timeout, retry with backoff, and circuit breaker — what each one actually does, and the order to stack them.
Read Article// ArchitectureCQRS Without the Astronaut Architecturepetarivanov.tech
Architecture9 min read
CQRS Without the Astronaut Architecture
You don't need event sourcing or a second database. Just split reads from writes, and finally fix the 20-method service nobody wants to open.
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Petar Ivanov
Software engineer, author, and speaker. 8+ years building production systems at Fortune 500s in cloud, finance, and travel. Ex-CEO/CTO of startups. I help JavaScript developers grow from Mid → Senior → Architect.
That now includes the field's hardest new question: how do you architect software around LLMs — RAG, agents, evals, cost, latency — so AI features survive real users? I build these systems in Python and JavaScript and teach the judgment behind them.
In the AI era, the question isn't whether you can code. It's whether you can think like an architect — and that's the gap I help you close.