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Learn before you build
What software really costs, how long it takes, how to pick a partner, and how to put AI tools to work — for startups, SMBs and enterprises.
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.
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.
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.
How to Migrate a Live E-Commerce Catalog to AI Search
Your semantic search prototype beats keyword search in offline eval. That's not the hard part. The hard part is rolling it out on a live catalog with thin product data without watching conversion rate slide for two weeks before anyone catches it.
LLM Prompt Failure Modes: A Diagnostic Cheatsheet
Most production LLM failures aren't model failures — they're prompt-contract violations with predictable symptoms. A diagnostic reference for engineers debugging summarization, extraction, and classification features that broke after launch.
Shadow Mode Testing for AI Models Before You Cut Over
Shadow mode lets your new credit-scoring or fraud model see real production traffic without making any customer-facing decisions. Here's a runnable tutorial covering the wiring, the logging schema, the comparison metrics, and the failure modes offline evaluation will never show you.
Event Sourcing Explained: Why Your Audit Log Is Lying to You
Most fintech databases store only the latest state, which is why audit logs quietly lie when regulators ask what happened six months ago. Event sourcing fixes that by making the log the system itself — here's how it works and when it's worth the cost.
Detect Data Drift in a Scikit-learn Model Before Users Do
A runnable tutorial for adding Population Stability Index drift detection to a production scikit-learn classifier. Catch input shifts in a single interpretable number per feature, weeks before error rates move.
AI Retraining Triggers Cheatsheet: When and Why
A scannable reference for ML engineers running production models on calendar-based retraining schedules. Includes drift triggers, signal-type cadences, and a decision table for replacing your weekly cron.
How to Retire a Legacy System Without Killing the Business
Replacing a business-critical legacy system isn't a code problem — it's a behavioral contract problem. Here's the playbook we use to retire 10-year-old systems while live traffic keeps flowing.
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.
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