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For Startups
Playbooks, decision frameworks, and case studies written for startups.
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.
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.
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.
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.
FastAPI vs. Django for AI Model Serving: Pick the Right One
Your p95 latency isn't creeping past 800ms because Django is slow. It's creeping up because a synchronous, GIL-bound model call is blocking your event loop — and FastAPI won't fix that on its own. Here's how to actually choose.
Microservices vs. Monolith for Your First AI Feature
Most architecture advice for shipping AI assumes you're either Google or greenfield. Here's the actual decision framework for a Series A team adding the first inference feature to a Django or Rails monolith.
How GimBooks Kept AI Accurate Across 3M Downloads
When an AI bookkeeping feature works at 10K users but breaks at 500K, the instinct is to blame data volume. The GimBooks case study shows the real culprit is usually segment collapse — and the fix is architectural, not statistical.
Questions to Ask Before Hiring an AI SaaS Dev Partner
Most AI SaaS vendor pitches look identical until you ask the right questions. Here are the 15 a Series A founder should run through before signing — and the answers that separate operators from demo-builders.
Feature Stores Explained: Why Your AI Keeps Training on Lies
Your credit-scoring model passes every offline test, then degrades two weeks after deployment. The culprit isn't drift — it's that your training pipeline and your serving pipeline are computing features differently, and no one is enforcing they match.
AI Evaluation Metrics Cheatsheet: Pick the Right One
Most teams pick an AI evaluation metric because it was easy to instrument, then discover months later that the number looked fine while a key account churned. This cheatsheet maps the metrics to the business decisions they actually encode.