Learn how large language models use tokens, transformers and learned parameters, what inference does, and what an LLM cannot guarantee.

AI inference uses a trained model to answer new requests. See how it differs from training and fine-tuning, and where model serving happens.

Understand LLM context windows, durable memory, RAG and KV cache—and why advertised context length does not guarantee useful recall.

Compare retrieval-augmented generation and fine-tuning for private documents, changing knowledge, repeatable behaviour and Canadian data control.

An offline LLM can run without cloud access after preparation. Learn what must be downloaded, what still connects, and what air-gapped means.

Compare AI chatbots, fixed workflows and agents by autonomy, tools, risk and control—and learn when the simpler architecture is the better choice.

Open weights, open source AI and API access are different. Learn what each permits and what Canadian businesses must verify before deployment.

The tariff crisis is not an AI tariff. It is evidence that Canadian businesses need a tested alternative to U.S.-controlled AI platforms.

CUSMA remains legally in force, but recent U.S. actions weaken it as a planning assumption. Canadian AI systems need portability and fallback.

Canada can operate suitable open-weight models on Canadian-governed infrastructure without waiting for one domestic model to lead every benchmark.

D-Central’s position: Canada cannot leave vital AI inference dependent on a foreign administration and platforms it cannot govern or replace.

OpenAI’s Tibo joins me to break down what’s next for Codex, ultra-fast AI, recursive self-improvement, and the future of personal AI agents. We also get into OpenAI vs. Anthropic, why Codex usage is exploding, and how AI could soon work faster than humans…

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LLM access to models by Anthropic, including the Claude series