Browse Comments — Clean (de-noised)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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Luís Rodrigues - This is both funny and useful for layperson explanations.
love the way in which this - without even mentioning it - acknowledges that AI is so much more than just generating images and videos
This is a clean mental model- what usually confuses people is not the acronyms, but how they actually stack into one working system. The real challenge isn’t understanding each layer, it’s making sure they’re connected in a way that turns intelligence into reliable action. Luís Rodrigues
This is one of the simplest and smartest explanations of modern AI architecture I’ve seen. Understanding the difference between LLMs, RAG, AI Agents, and MCP is becoming essential in today’s AI-driven world. The comparison with the human body makes it much easier to understand. Great post!
Well represented
Luís, I’ve seen this "human anatomy" analogy all over LinkedIn lately. It’s a clean framework, but it dangerously oversimplifies the reality of high-stakes environments. Bridging bedside medicine and AI architecture, I see a major flaw: this body is missing an immune system. In a hospital, "agents with hands" operating without strict, fail-closed deterministic gates is a recipe for fatal never-events. The clean logic of enterprise AI always shatters against the chaotic, noisy reality of clinical workflows. We need less hype about "brains" and more focus on "immune responses." I'd value your thoughts: 1) Technically, how do we architect this deterministic "immune system" into the MCP layer to intercept hallucinated agent actions? 2) Clinically, how do we stop this "brain + hands" hype from pushing leadership to deploy autonomous systems before EHR data is actually clean?
Love the analogy! Luís Rodrigues 👏🙌
The weakest layer in many companies is usually not the LLM. It is the connection layer: permissions, tool access, data boundaries, and reliable integrations.
Really clean way to map the stack. The “body” analogy actually makes it easy to explain beyond engineering teams.