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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
Kafka vs. Pub/Sub vs. Kinesis for Real-Time AI Pipelines
Most Kafka vs Kinesis vs Pub/Sub comparisons benchmark raw throughput and miss what actually breaks AI pipelines: replay semantics, consumer lag during retraining, and feature freshness. Here's how to pick the right streaming backbone before your next sprint.
Event Sourcing for AI Products: Why Your Model Needs a Time Machine
Your CRUD database can tell you what your AI decided, but not why — because the world it saw at decision time is already gone. Event sourcing is the architecture that gives your model a time machine, and it's the prerequisite for any serious AI audit trail.
Questions to Ask Before Hiring an AI Logistics Partner
A field-tested set of adversarial questions to ask any AI logistics vendor before signing — designed to expose whether they've shipped at real fleet scale or just demoed on clean CSVs. Includes what good and red-flag answers actually sound like.
5 Mistakes We Made Shipping AI to a Live Pharmacy Marketplace
A field-level post-mortem on what breaks when AI substitution, routing, and recommendation features hit a real e-pharmacy catalog. Five specific mistakes, the symptoms you'll see in production, and how to recover without rolling everything back.
Ship a Drug Interaction Alert With a Local LLM in 7 Steps
A runnable tutorial for CTOs at e-pharmacy startups who need drug interaction alerts without sending patient data to OpenAI. Uses Mistral 7B locally, a versioned interaction dataset, and citation-grounded extraction.
How KarroFin Scaled AI Credit Scoring Without Killing Approval Rates
KarroFin's credit model wasn't broken. No alerts, no errors, no engineering fires. But approval rates were quietly compressing at scale — and the fix wasn't where the data science team was looking.
Sync vs. Async AI Inference: Pick the Right Model for Your Product
Most AI features ship synchronously because that's how the tutorial was written. By the time latency, cost, and reliability start compounding, the inference mode has become a UX contract you can't quietly break. Here's how to pick correctly the second time.
LangChain vs. LlamaIndex vs. Raw API: Pick One
Three days into a prototype, every LLM orchestration framework looks the same. Here's how to pick between LangChain, LlamaIndex, and a raw API wrapper based on where you want to own the complexity — not which one had the best quickstart.
Feature Stores Explained: Why Your ML Models Stale Out
Your credit risk model nailed backtesting but production accuracy keeps slipping. The culprit is rarely the model — it's a silent mismatch between how features are computed at training time and at inference. Here's what a feature store actually does about it.
How to Audit an AI Feature Before It Ships to Production
Your AI feature passed internal demos. That's not the same as being ready for real users. Here's the pre-ship audit playbook to either confirm your fear or clear the launch.
AI Observability Stack: What to Monitor and When
Your APM dashboard says the AI feature is healthy. Your users disagree. Here's the observability stack that catches what p99 latency and error rate structurally cannot — drift, hallucination, prompt regression, and feedback loop poisoning.
Your RAG Pipeline Isn't Failing. Your Chunking Strategy Is.
Most broken RAG pipelines aren't broken at the retrieval layer — they were broken at ingestion, when documents were split without respecting semantic boundaries. Here's why chunking is the silent failure mode no metric catches.