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Technology Munch

Mechanism

How these things actually work

Almost every surprise these tools produce follows from the mechanism. Ten minutes here prevents a year of confusion about hallucination, forgetting, and why the same question gives two answers.

  1. What a Language Model Is Doing

    Not a database, not a search engine, not a reasoning machine in the way people assume. The mechanism explains almost every surprise these tools produce.

    Explainer

  2. Why Models Invent Things

    Fabrication is not a bug being fixed. It is the same process that produces correct answers, operating where the training data was thin.

    Explainer

  3. Context Windows and Memory

    The model has no memory between messages. What looks like conversation is the entire history being re-read every time, which explains several odd behaviours.

    Explainer

  4. Tokens, Cost and Length Limits

    Models process fragments rather than words or characters. This explains pricing, length limits, and why some languages cost several times more than others.

    Reference

  5. Training Cutoffs and Stale Answers

    Every model has a date after which it knows nothing, and it will answer questions about that period anyway, confidently and wrongly.

    Explainer

  6. Reasoning Modes: Cost and Benefit

    Models that work through steps before answering are genuinely better at some problems and no better at others. Knowing which is which saves time and money.

    Analysis

  7. Agents: What They Cannot Do

    An agent is a model given tools and permission to act in a loop. That is genuinely useful and it introduces failure modes a chat interface does not have.

    Analysis

  8. Open Weights vs Closed Models

    The distinction is about who controls the model file, and it determines your privacy position, your costs and your exposure to a vendor's decisions.

    Analysis

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