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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
FastAPI vs. Django for AI Model Serving: Pick the Right One
Your p95 latency isn't creeping past 800ms because Django is slow. It's creeping up because a synchronous, GIL-bound model call is blocking your event loop — and FastAPI won't fix that on its own. Here's how to actually choose.
Questions to Ask Before Hiring an AI Logistics Dev Partner
Most AI logistics vendors can demo a dispatch dashboard. Far fewer can model lane exclusions, HOS rules, and carrier telemetry dropouts. Here are 15 questions that expose the difference before you sign.
How to Migrate a Live E-Commerce Catalog to AI Search
Your semantic search prototype beats keyword search in offline eval. That's not the hard part. The hard part is rolling it out on a live catalog with thin product data without watching conversion rate slide for two weeks before anyone catches it.
5 Mistakes Teams Make When Automating Pharmacy Operations with AI
Most pharmacy AI automation rollouts fail not because the model is inaccurate, but because it was trained on staff workarounds instead of the real dispensing workflow. Here are the five mistakes we see most often, with the symptoms and how to recover.
How to Hire an AI Development Partner in the UK
Hiring an AI development partner in the UK isn't about portfolio size or day rate. It's about whether the vendor can produce a UK GDPR Article 28 controller-processor mapping on request — and seven other concrete tests that separate production shops from landing-page operations.
Best AI Development Companies in Australia for SMBs
Most Australian SMBs evaluating AI partners pick between agencies that bolt GPT-4 onto a dashboard and offshore shops with no accountability. Here's an honest landscape of the vendor categories that actually fit a sub-$300K AUD AI build.
How KarroFin Scored 250K Borrowers Without a Credit Bureau
KarroFin needed to underwrite borrowers the bureaus had never heard of. Here's the engineering call that turned UPI logs, GST cadence, and utility payments into a score that beat the bureau — and what we'd do differently.
LLM Prompt Failure Modes: A Diagnostic Cheatsheet
Most production LLM failures aren't model failures — they're prompt-contract violations with predictable symptoms. A diagnostic reference for engineers debugging summarization, extraction, and classification features that broke after launch.
Microservices vs. Monolith for Your First AI Feature
Most architecture advice for shipping AI assumes you're either Google or greenfield. Here's the actual decision framework for a Series A team adding the first inference feature to a Django or Rails monolith.
Shadow Mode Testing for AI Models Before You Cut Over
Shadow mode lets your new credit-scoring or fraud model see real production traffic without making any customer-facing decisions. Here's a runnable tutorial covering the wiring, the logging schema, the comparison metrics, and the failure modes offline evaluation will never show you.
Event Sourcing Explained: Why Your Audit Log Is Lying to You
Most fintech databases store only the latest state, which is why audit logs quietly lie when regulators ask what happened six months ago. Event sourcing fixes that by making the log the system itself — here's how it works and when it's worth the cost.
Celery vs. Kafka for AI Pipelines: Pick the Right One
Celery's broker-ack model makes it structurally incapable of replaying inference events, no matter how much you tune it. Here's how to decide whether your AI pipeline needs a queue or a log — and when Kafka is worth the operational cost.