No fluff. No listicles. Writing from inside the work — for founders, product teams, and marketers who care about getting the details right. We publish what we wish someone had told us before we shipped.
RAG and fine-tuning are the two ways to make a general-purpose model work for your case, and teams treat the choice as a fight. It is not one. RAG gives the model the right information at answer time; fine-tuning changes how the model behaves. They solve different problems — and most features need one far more than the other.
Almost every production AI system you have used runs on Python. Not because Python is fast — it is not — but because the ecosystem, the model SDKs, and the data tooling all live there. The interesting question is what separates a Python notebook that works from a Python service you can run in production.
Node.js and Python are the two default choices for a backend, and the decision is usually made on what the team already knows. That instinct is not wrong, but it is incomplete — they have genuinely different strengths, and the workload should sometimes override the habit.
Legacy software can be modernised in place, incrementally, while it keeps running and earning the whole time. No big-bang switchover, no frozen roadmap — here is the approach, step by step.
A legacy CMS rarely fails outright — it ages, until every change is slow and risky and the security updates have stopped. How to replatform off it without losing the search traffic you spent years earning.
Adding AI to a product that works well is straightforward. Adding it to one that is not ready produces a demo that falls apart in front of real users. Five things have to be true first — and the model is not one of them.
Re-platforming a store is nerve-wracking because the store is the revenue. Why stores move to Shopify, what a migration really involves, and how a staged approach keeps SEO, data, and uptime safe.
An AI agent sounds like magic and is, underneath, a fairly simple loop: the model decides on an action, the action runs, the model sees the result, and it decides again. Understanding that loop tells you what agents are good at, what they cost, and where they break.
An AI product MVP has a different job from a normal MVP. A normal MVP tests whether people want the thing. An AI MVP also has to test whether the AI can actually do the thing reliably — and those are two separate risks.
Django and FastAPI are both Python web frameworks, and teams treat the choice as a fight. It is not one. Django is a batteries-included framework for building a whole product; FastAPI is a lean, async, typed framework for APIs and services. The honest question is which one fits the thing you are actually building.