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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
Questions to Ask Before Hiring an AI Development Partner for Healthcare
Every AI vendor claims healthcare experience. Here are 15 specific questions that separate teams who have actually shipped under HIPAA, HL7, and clinical scrutiny from those who built a wellness app and are overstating their credentials.
In-House AI Team vs. AI Development Partner: Pick One
You have 30 days to decide: hire two senior ML engineers or engage an AI development partner for your first core feature. Here's the decision framework that actually matters — and the one axis most founders get wrong.
5 Mistakes We See Teams Make Shipping AI to Thin-File Users
Most thin-file AI lending models don't fail because the architecture is wrong. They fail because the team never audited what happens after the first batch of rejections starts retraining the model. Here are the five failure modes we see most often.
Pinecone vs. Weaviate vs. pgvector: Pick One Without Regret
Every vector database benchmark was run by the vendor being benchmarked. Here's an honest head-to-head of Pinecone, Weaviate, and pgvector for production RAG — based on where your vectors actually need to live, not whose marketing landing page you read last.
Fine-Tune a Prescription NER Model on 500 Labeled Lines
Off-the-shelf medical NER models choke on regional brand names, OCR noise, and mixed-language prescriptions. Here's how to fine-tune your own with roughly 500 labeled lines and a free afternoon.
How Vahak Matched 800K Trucks Without a Recommendation Collapse
A deep dive into the architectural decisions behind Vahak's freight matching engine — and the counterintuitive reason match quality got worse as supply grew. How feedback loop pollution, not data sparsity, became the real enemy at marketplace scale.
Your AI Feature Isn't Slow. Your Data Contract Is.
Most production AI features that feel slow aren't bottlenecked by the model. They're bottlenecked by the undocumented assumptions between your product database and the AI layer — and no GPU upgrade fixes that.
LLM Latency Cheatsheet: Where 800ms Actually Goes
Most LLM latency budgets get spent before the model even sees the prompt. Here's a layer-by-layer cheatsheet for pinpointing where time disappears and what to fix first.
How to Cut SaaS Churn With Behavioral Signals Before the Cancel Click
Most B2B SaaS churn is predictable from usage logs 30-45 days before the cancel email arrives. Here's the operator's playbook for catching it — built around the one signal most founders miss: collapsing seat breadth.
Vector Search Is Not Semantic Search (And the Difference Costs You)
Your vector-powered drug lookup demos beautifully but returns clinically wrong matches in production. The gap between vector search and real semantic search is where health-tech features quietly break.
Stripe Radar vs. Custom ML Fraud Models: Which Wins?
Stripe Radar's false-positive problem in emerging markets isn't a model sophistication issue — it's a training data representation issue. Here's how to decide between Radar, a third-party ML layer, and a custom model trained on your own transaction graph.
Build vs Buy AI Route Optimization: A Decision Framework
The build-vs-buy decision for AI route optimization isn't really about fleet size — it's about whether your constraints are standard or proprietary. Here's a framework that scores both paths honestly, including where each one quietly fails.