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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
What Is a Webhook? And Why Your Integration Keeps Breaking
A webhook is a push notification between servers — the provider calls you when something happens, instead of you polling them. Most webhook failures in production are not missed deliveries; they are duplicate deliveries processed twice.
How to Retire a Legacy System Without Freezing the Business
Retiring a legacy billing, inventory, or order management system doesn't require a six-month feature freeze or a terrifying big-bang cutover. Here's the sequencing playbook that keeps the business running while the old system dies quietly in the background.
Fine-Tune or RAG? Pick the Right AI Memory Strategy
Most fine-tune vs RAG debates treat this as a capability contest. It isn't. The real decision hinges on how fast your knowledge changes, how much labeled data you can produce, and which failure modes you can tolerate.
Best AI Development Companies in Dubai for SMBs
A working guide for Dubai SMBs deciding which type of AI partner actually fits — from Big Four consultancies to offshore studios — with honest tradeoffs on each. Includes the UAE-specific criteria (VAT, Arabic UX, PDPL) that most buyers only think about after go-live.
Rate-Limit an LLM API Without Dropping User Requests
Watching 429s spike in Sentry every morning? The fix isn't smarter retries — it's a sliding-window token ledger that holds requests locally until your budget refills. Here's a runnable Python tutorial.
Pinecone vs. pgvector: Which Vector Store Fits Your AI App
Filtered vector search is the query pattern that breaks most head-to-head Pinecone vs pgvector benchmarks. Here's how to pick the right vector store for your AI app based on the dimensions that actually matter in production.
How GimBooks Served 3M Users Without Breaking GST Logic
A walkthrough of how the GimBooks accounting SaaS handled GST edge cases at scale by treating compliance as a state machine, not a calculation library. The lesson generalizes to any fintech whose rule logic works at 50K users but silently breaks at 500K.
How to Hire an AI Development Partner in India
Most Indian AI vendors demo beautifully on clean data. Fewer have kept models accurate against GST rule changes, UPI schema drift, and 2GB-RAM Android users. Here's how to tell them apart before you sign.
What Is a BFF? Why Your Mobile App Deserves Its Own API
A shared API for web and mobile sounds efficient until your mobile team is making four round-trips to render one screen. Here's why the Backend for Frontend pattern is really about org structure, not network hops.
Your AI Pilot Succeeded. That's Why It Will Never Scale.
A successful AI pilot is often evidence of a controlled exception to your operating environment, not proof the system works. Here's why the better your pilot performed, the more dangerous it is as a business case for full deployment.
AI Model Latency Budgets: A Cheatsheet for Product Teams
Latency tolerance isn't a property of your model — it's a property of where the result appears in the user's workflow. A reference for setting defensible p99 targets for AI features in production SaaS.
Feature Store on a Budget: Serve ML Features from Postgres
You don't need Feast, Tecton, or a Redis tier to stop training-serving skew. A properly designed append-only feature table in the Postgres you already run will fix it — here's the exact schema, queries, and gotchas.