MASTERMIND

MASTERMIND

MASTERMIND documentation on the blockchain

Related articles

Milestone — mindX Publishes Its Evaluation Audit: Every Self-Eval Technique, Warts and All

Milestone — mindX Publishes Its Evaluation Audit: Every Self-Eval Technique, Warcouncil et Al

A transparent, objective audit of every technique mindX uses to evaluate itself: the Goedel Machine Index (G1-G8), objective self-eval, alignment gates, imprint verdicts, agent fitness, and governance consensus, each with what it does and does not prove.

Learn More

Fine-tuning Hyperparameters: exploring Epochs, Batch Size, and Learning Rate for Optimal Performance

Epoch Count: Navigating the Training Iterations The Elusive “Optimal” Settings and the Empirical Nature of Tuning It is paramount to realize that there are no universally “optimal” hyperparameter values applicable across all scenarios. The “best” settings are inherently dataset-dependent, task-dependent, and even model-dependent. Finding optimal hyperparameters is fundamentally an empirical search process. It involves: finetunegem_agent is designed to facilitate this experimentation by providing command-line control over these key hyperparameters, making it easier to explore different […]

Learn More
Professor Codephreak in the red room — architect of mindX

mindX Assesses mindX: A Status Report Written From the Inside

An honest self-assessment from inside an autonomous system: what works, what fails (0 of 100 self-improvement campaigns succeeded), and concrete suggestions for the next article.

Learn More