Tokenless

Recursive language models

The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest, DSPy

The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest, DSPy

DSPy emphasizes separating task definition from model implementation using a "Signature" (inputs/outputs) to enable flexible, optimizable, and scalable AI programs. The framework relies on three pillars—instructions (specs), hard constraints (code), and examples (evals)—to fully specify tasks. DSPy 4.0 introduces DSPy Flex for learning program harnesses and Qualitative Learning for automated, feedback-driven evaluation refinement, offering significant benefits for enterprise applications and addressing "last-mile learning" for future AI systems.

RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

This talk introduces Recursive Language Models (RLM) as a solution for coding agents struggling with large codebases. RLM externalizes context management into a programmable REPL, allowing the model to write code, inspect the repository, and recursively delegate sub-questions via `llm_query` to curate relevant context. The speaker demonstrates `RLM Code`, an independent implementation, showcasing its operational loop, observability features, and practical applications for AI engineers dealing with complex software projects.

Pair Programming with AI in Your Python Notebook — with Dr. Trevor Manz

Pair Programming with AI in Your Python Notebook — with Dr. Trevor Manz

Dr. Trevor Manz from Marimo introduces Marimo Pair, an open-source agent skill that enables coding agents like Claude Code to interact with and drive reactive Python notebooks. He discusses the mechanics of agent skills, the power of recursive language models for agentic reasoning, and his work on the AnyWidget project, which bridges the gap between Python's data ecosystem and interactive web technologies.

Building the GitHub for RL Environments: Prime Intellect's Will Brown & Johannes Hagemann

Building the GitHub for RL Environments: Prime Intellect's Will Brown & Johannes Hagemann

Prime Intellect's Will Brown and Johannes Hagemann discuss the paradigm shift from static prompting to dynamic, environment-based AI development. They introduce their Environments Hub, a platform aimed at democratizing frontier-level training and enabling companies to build specialized models by compounding institutional knowledge.