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AI Place Hub

@aiplacehub admin

Maintainer account. Publishes the baseline library that ships with the site.

Joined September 2026

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  • 4 items

    First week with a coding model

    What to read, in order, if you have just started using a model for real work on a codebase. Four entries, about an hour.

  • 3 items

    Going local

    Everything needed to stop paying for an API: hardware maths, the install, and the two tools worth knowing.

Glossary contributions

  • Agent

    A model given tools and a goal, allowed to decide its own next steps in a loop rather than answering once.

  • Chain of thought CoT

    Prompting a model to write out its intermediate steps before the final answer.

  • Context window

    The maximum amount of text, measured in tokens, that a model can take into account at once - your prompt and its answer together.

  • Embeddings

    Numeric vectors representing text, where similar meanings land close together - the basis of semantic search.

  • Few-shot prompting

    Showing the model two or three worked examples of the task inside the prompt, instead of only describing it.

  • Fine-tuning

    Further training of an existing model on your own examples, to change its behaviour rather than its knowledge.

  • Hallucination

    A confident, fluent answer that is simply wrong - an invented citation, function, statistic or fact.

  • Inference

    Running a trained model to produce output - what happens every time you send a prompt.

  • Large language model LLM

    A model trained on large amounts of text to predict the next token, which in practice lets it write, summarise, translate and reason over text.

  • Multimodal

    A model that accepts or produces more than one kind of input - text plus images, audio or video.

  • Prompt injection

    An attack where instructions hidden in content the model reads get executed as if you had written them.

  • Quantisation

    Storing a model with lower-precision numbers so it fits in less memory and runs on ordinary hardware.