AI explained in plain language
Most decisions about AI tools get easier once the ideas underneath are clear. These posts explain them in plain language, with sources you can check: how large language models differ from each other, what retrieval augmented generation (RAG) adds, how a neural network learns, and what people mean when they call a system agentic.
Each post sticks to what is established and links to the papers, the vendor documentation or the research behind it. Where something is a forecast rather than a fact, the post says so.
If you are deciding where AI fits in your own business, the AI agents for business posts are the practical next step.
What agentic AI is and how it differs from a chatbot
Agentic AI is a model that picks its own next step and uses tools in a loop. How it differs from a chatbot or a workflow, with a worked example.
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Brain chips and BCIs: what has happened and what is speculative
Brain computer interfaces today: peer reviewed speech results, Neuralink and Synchron trial milestones with dates, and the claims that are still plans.
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How large language models differ, and what to compare
Context window, reasoning effort, tool use, input types and price per token: how LLMs from Anthropic, OpenAI and Google differ, from their own pages.
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Where AI is heading: what the data shows and what is forecast
Where AI is heading, from dated reports: Stanford's AI Index, Epoch AI, METR and the IEA. What has been measured, what is forecast, and who said it.
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AI in robots: what is deployed today and what is still a demo
What AI robots actually do today, from IFR counts, Amazon, Waymo and dated humanoid pilots, which claims have evidence, and the limits the industry names.
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How a neural network learns, from weights to backpropagation
A neural network learns by measuring its error and adjusting its weights with gradient descent and backpropagation. The layers, the loss and the 1986 method.
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RAG explained: how retrieval works and when to use it
Retrieval augmented generation finds the right passages and puts them in the prompt. How it works, from the 2020 paper to vector search, and when to skip it.
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