Results for “automatic-differentiation”

Implements finite automata, regular expressions, parsers, and lexers for building text processors and pattern matchers.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Automates the full lifecycle of agent skills: extracts reusable constraints from user feedback, merges preferences with version bumps, and retrieves relevant skills for new tasks.

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

Orchestrates three frontier models to debate a question and synthesizes their best insights into a single superior answer.