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First principles

LearningInference

Mechanisms

RepresentationsContext & memoryArchitecturesDiffusion

Advanced research

Reasoning & agentsWorld modelsAGI & ASIEvaluation

Explore each space independently. More detail is always within reach.

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AuraUnderstand AIReasoning & agents

From a model to a system that acts

Understand the harness around a model and its tools.

EssentialsExperimentGo deeper
Scientific sources

How it works

Reasoning & agents

A harness organises calls, supplies context, manages task state and runs tools within permissions. Returned observations can trigger another step or a check. MCP specifies standardised exchanges; it is not a complete execution harness.

Check my understanding

Before answering, form your own explanation.

Installing MCP guarantees that an assistant will complete every action autonomously.

Takeaway

A persuasive explanation does not prove a result correct. Autonomy depends on access, success criteria, stopping conditions and recovery. Evaluate the complete system on the intended task.

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From concept to practicePut it into practice

Scientific sources

  • Model Context ProtocolModel Context Protocol — Introduction
    Original publication
  • Chollet · 2019On the Measure of Intelligence
    Original publication

Teaching synthesis of the cited sources. Published results remain tied to their tasks, protocols and budgets.

Explore nextWorld modelsInference

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18 results

LearnLearning, then using a modelSeparate learned parameters from information supplied with a request.
LearnFrom context to the next tokenSeparate the model’s probabilities from the rule that selects an output.
LearnFrom text to representationsUnderstand why words become vectors without equating a vector with human meaning.
LearnWhat kind of memory?Separate working context, recurrent state, learned parameters and persistent storage.
LearnTransformers: understanding attentionCompare architectures, training methods and complete systems at the right level.
LearnGenerating by denoisingDistinguish corruption during learning from progressive generation.
LearnFrom a model to a system that actsUnderstand the harness around a model and its tools.
LearnPredicting to choose an actionExplore longer horizons and the risk of inaccurate predictions.
LearnDefinitions, not a single verdictExamine generality, performance and adaptation before applying a label.
LearnWhat can a result establish?Move from a convincing impression to a reproducible comparison.
ConfigurePrepare a ChatGPT projectA clear objective, useful context and instructions you can actually use.
ConfigureChoose for your taskStart with your needs. Compare access and outcomes without a universal model ranking.
ConfigureMake your request verifiableTurn an intention into an objective, context and success criteria.
ConfigurePut the right information in the right placeDistinguish working rules, task resources and memory managed by the product.
ConfigurePrepare a development environmentDefine the model’s role, harness capabilities and the access you actually need.
ConfigureCompose a development workflowOrganise preparation, changes, verification and reporting. Reuse the steps that help.
ConfigurePrepare a mission, then check the resultA reproducible example, a checklist and a record of your observations.
Public programsTentacularPublicly available uses, features and resources.