Save tokens and save money with this self-evolving beast (github.com)

Entroly has introduced a groundbreaking AI runtime that drastically reduces token consumption while enhancing code understanding and performance. Unlike existing solutions like Claude and Copilot, which only access a limited portion of the codebase, Entroly leverages a 2M-token brain to optimize context handling, enabling it to process an entire repository with substantially lower costs. This self-evolving daemon analyzes and synthesizes code without relying on large language models (LLMs), thus operating with a "provably token-negative" model—meaning it learns and improves without increasing expenses over time. The significance of Entroly for the AI/ML community lies in its innovative approach to token economy. It employs a deterministic synthesizer to read the abstract syntax tree (AST) of the code and create functional tools without incurring costs initially. Additionally, the "Dreaming Loop" feature allows the AI to generate synthetic queries and improve autonomously when idle, ensuring that users encounter a continually evolving and more efficient runtime each time they access it. By fundamentally changing how AI interacts with codebases, Entroly not only enhances productivity but also lowers operational costs for developers.