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The following article originally appeared on the Agentic AI Foundation blog and is being reposted here with the author’s permission. Websites are designed for people. We can look at a page and quickly understand where to enter information or which control will complete an action.
The following article originally appeared on Vanishing Gradients and is being republished here with the authors’ permission When an AI agent can explore a dataset, choose a modeling approach, run the analysis, and explain its findings, what should the data scientist do?
Ask most engineers what MCP is and you’ll get the same answer: a way to plug tools into an LLM. Fair enough, as far as it goes. But that description treats MCP like plumbing, and after months building MCP-based integrations for large enterprise platforms, I don’t think plumbing is the right…
This is the third post in a four-part series about post-training. If you missed them, read part 1 and part 2. The final post, on implementing your own pipeline, will be coming October 7.
When considering the history of AI, Richard Sutton observed that brute force and compute scale has always trumped human expertise, and when you look for it, you can see this “bitter lesson” play out throughout tech history.
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