AutoDidact: Self-Teaching Framework for LLM Improvement

Fri, 01 May 2026 00:00:00 +0000

The most expensive part of improving AI models has always been data: collecting, cleaning, and annotating millions of examples requires enormous human effort. AutoDidact explores a tantalizing alternative: what if language models could teach themselves? Created by researcher dCaples, this open-source framework implements iterative self-improvement loops where LLMs generate their own training data, evaluate their own outputs, and fine-tune themselves – all without human intervention.

The concept draws inspiration from a rich body of research on self-supervised learning, self-play in games (like AlphaGo), and more recent work on constitutional AI and self-rewarding language models. AutoDidact packages these ideas into a practical framework that researchers and practitioners can apply to their own models and tasks.

Hermes Agent: Nous Research's Self-Improving AI Agent with 17 Platform Support

Fri, 01 May 2026 00:00:00 +0000

Most AI agents are static – their behavior is fixed at deployment time by their system prompt and model weights. What happens when they encounter a novel situation they were not designed for? They fail, and a developer must manually update the agent. Hermes Agent from Nous Research takes a fundamentally different approach: it learns from its experiences and improves its own behavior over time, without human intervention.

Hermes Agent, available at github.com/NousResearch/hermes-agent, is a self-improving AI agent framework with support for 17 different platforms including Discord, Slack, Telegram, Twitter, and more. It uses a built-in learning loop that captures task outcomes, identifies failure patterns, and updates its own instruction set to avoid repeating mistakes. This creates an agent that gets better at its job the longer it runs.

Self-Improving on SoloSoft

AutoDidact: Self-Teaching Framework for LLM Improvement

Hermes Agent: Nous Research's Self-Improving AI Agent with 17 Platform Support