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For Startups
Playbooks, decision frameworks, and case studies written for startups.
Vector DB vs. Postgres pgvector: Pick One for Your AI Product
Your infra lead says pgvector won't scale and you need Pinecone or Weaviate. They might be right. They're also probably wrong for the reasons they think. Here's the framework that actually matters.
Fine-Tune an Embedding Model on Your Own Docs in 6 Steps
Your RAG pipeline keeps returning confidently wrong passages, and you've already exhausted chunking and re-ranking tricks. The defect is in the embedding model itself — here's how to fix it with 500 pairs from your query logs.
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.
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.