xAI's Superintelligence Push Just Got a Co-Founder of Mistral AI and a Member of Mira Murati's Founding Team - FinTech Weekly
xAI's Superintelligence Push Just Got a Co-Founder of Mistral AI and a Member of Mira Murati's Founding Team FinTech Weekly
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I Compared Make.com and n8n Across 20+ Client Deployments. Here Is My Verdict.
A client came to me in January with a Make.com scenario that had started as a simple lead routing workflow and mutated into a 47-step monster. It was timing out. It was burning through their operations credits. And when they needed to add an AI agent that could make decisions based on their CRM data, Make had no good answer. Three weeks later, after rebuilding the whole thing in n8n, their monthly automation bill dropped by 71% and the AI agent actually worked. That project pushed me to do something I had been putting off: a real, systematic comparison of Make.com and n8n for AI agent workflows. Not a feature checklist review. A practitioner's assessment built on two years of deploying both platforms across more than 20 client environments. Here is what I found, and more importantly, here
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Tokens, training, context windows, and temperature — the four concepts that explain everything about large language models. You know how your phone suggests the next word when you’re texting? Type “I’m going to the” and it suggests “store” or “park.” Now imagine that autocomplete was trained on every book, every website, every conversation ever written — and instead of suggesting one word, it could write entire essays, solve math problems, and generate working code. That’s fundamentally what a Large Language Model does. And once you understand four concepts — tokens, training, context windows, and temperature — you’ll know more about how AI works than 95% of people who use it daily. No PhD required. Concept 1: Tokens — How AI Reads AI doesn’t read letters or words the way you do. It reads
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