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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
Build vs. Buy Your AI Matching Engine: A Decision Framework
Most build-vs-buy frameworks for AI matching engines treat this as a cost decision. It isn't. It's a data-density question — and the answer usually surprises the people asking it.
5 Mistakes Teams Make When Automating Pharmacy Operations
Most pharmacy automation projects fail not because the software is bad, but because it was configured against an idealized workflow no pharmacist on the floor actually follows. Here are the five mistakes we see most often, and how to recover from each.
Your Automation ROI Is Real. Your Headcount Math Is Wrong.
Hours saved on a slide are not dollars removed from a P&L. Here's the honest math behind automation ROI — and why most business cases quietly overpromise the board.
Stream LLM Responses to a React Frontend Without Melting
Your ChatGPT-style feature stalls for 6 seconds before rendering a single token. Here is how to stream LLM responses to React properly — with auth, aborts, and partial JSON that does not double-render on flaky networks.
How to Cut Over a Live Database Schema Without Downtime
A step-by-step playbook for renaming columns, dropping tables, and restructuring core models on a live production database — without a maintenance window. Written for CTOs whose deployment pipeline isn't clean enough to ship app and DB changes atomically.
What Is Idempotency? Stop Charging Customers Twice
A double-charge after a mobile timeout is almost always a retry bug, not a payment gateway bug. Here's how idempotency keys work, and why the fix lives in your client — not your server.
Temporal Tables vs. Audit Logs: Pick the Right History Model
Audit logs and temporal tables solve different problems, and picking the wrong one leaves you writing ad-hoc reconstruction queries for years. Here's how to choose when compliance, point-in-time reporting, or both land on your desk.
Questions to Ask Before Hiring an AI Healthcare Dev Partner
Before you sign an AI healthcare development partner, ask them these 18 questions. They separate teams that have shipped against real clinical, HIPAA, and EMR constraints from vendors who dressed a generic build in healthcare language.
AI Observability Cheatsheet: What to Log, Alert, and Ignore
Standard APM misses the signals that actually break LLM features in production. This cheatsheet covers exactly what to log, what to alert on, and what to stop paging engineers about at 2am.
5 Mistakes Teams Make When Automating B2B Credit Onboarding
Your automated onboarding is hitting SLA targets but the manual-review queue keeps growing and bad approvals are creeping up. Here are the five failure modes we see most often in B2B credit decisioning, and how to recover from each.
How to Hire an AI Development Partner in the US
A practical filter for US buyers evaluating AI development partners after a failed vendor engagement. The criteria that separate demo shops from teams that have actually shipped production systems under compliance pressure.
How Vahak Onboarded 800K Trucks Without Breaking the Marketplace
Vahak crossed 800,000 trucks on its platform without watching match quality collapse — a rare outcome for two-sided logistics marketplaces. The unlock wasn't more onboarding data. It was redesigning the matching model to learn from behavior after signup.