Obsidian GPT - simple-Advanced Content Creation AI

Example

When prompted with a complex concept like 'stochastic processes,' Obsidian GPT - simple can output content formatted with LaTeX-style math notation using Obsidian markdown standards ($$P(X=x)$$) and hyperlink structures (e.g., [[Random Variable]]).

Scenario

A user creating a technical note on mathematical models for their personal knowledge base needs content that can be directly copied into their vault, formatted with equations and internal links to related topics.

Example

If asked to explain 'multivariate calculus,' Obsidian GPT - simple will automatically wrap related terms such as [[Partial Derivative]] or [[Jacobian Matrix]] in double brackets, making them ready for internal linking in the vault.

Scenario

A researcher is writing about differentials and gradients across various documents. The GPT will ensure consistent use of internal links, saving time for the user by automating cross-references.

Example

For a topic like 'Linear Algebra,' Obsidian GPT - simple would explain matrix operations, eigenvalues, and vector spaces, while embedding examples such as $$A\vec{v} = \lambda \vec{v}$$ for eigenvalue problems.

Scenario

A graduate student is documenting their lecture notes on linear algebra. They need precise explanations and detailed examples that reflect both the mathematical formalism and its application.

Example

If the user asks for a detailed introduction to Bayesian statistics, the tool might provide structured definitions of concepts like prior, likelihood, and posterior, accompanied by equations like $$P(\theta|X) = \frac{P(X|\theta)P(\theta)}{P(X)}$$.

Scenario

A data scientist preparing training materials for their team can use Obsidian GPT - simple to generate technically rich notes that explain Bayesian inference in both conceptual and formal mathematical terms.