_1751731246795-BygAaJJK.png){kind=logo}
Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
How to Cut AI Inference Costs Without Touching Your Model
Most AI inference overspend is not a model-size problem — it's a request-routing problem. Here's the playbook for fixing it without touching your model or losing output quality.
Your AI Feature Doesn't Need More Data. It Needs a Harder Objective.
Most AI feature stagnation is not a data quantity problem. It's an objective mismatch — your model is perfectly optimizing a proxy metric that quietly diverged from the outcome users actually care about.
AI Prompt Versioning Cheatsheet: Track, Rollback, Deploy
A scannable reference for shipping prompts to production without breaking output quality. Covers versioning schemes, rollback patterns, regression testing, and the dev-staging-prod promotion pipeline most teams skip.
Questions to Ask Before Hiring an AI Fintech Dev Partner
Most AI fintech vendors demo well and use the right words. These 15 questions separate the ones who have actually shipped under regulatory and credit-risk constraints from the ones who haven't.
How GimBooks Served 3M Users Without a Broken Ledger
A teardown of the inflection point most accounting SaaS hit between 50K and 500K users — where ledger drift, reconciliation failures, and AI categorization errors look like three problems but are actually one. Here is what we learned shipping through it with GimBooks.
Batch vs. Real-Time AI Inference: A Decision Framework
Most teams default every AI feature to real-time inference and overpay for latency they don't need. The right question isn't how fast your model runs — it's whether a stale answer causes a worse user decision.
Stream LLM Tokens to a React UI Without Melting Your Server
Most LLM streaming tutorials skip the part that actually breaks under load: backpressure between OpenAI's ReadableStream, your Node response, and the browser. Here's the three-line fix and a working tutorial that survives concurrency.
How to Run a Shadow Deployment Before Your AI Feature Goes Live
Staging tests passed, but staging traffic looks nothing like production. Here's the shadow deployment playbook senior engineers use to validate an AI feature against real inputs before a single user sees an output.
Your AI Model Isn't the Product. Your Retraining Loop Is.
Most teams confuse deploying a model with building an AI product. The model you shipped is a depreciating asset — the retraining pipeline behind it is the only thing that compounds.
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.