LLM Self Training Guide
A step by step guide to llm self training that covers memory-first workflows, embeddings, safety checks, and tools like OpenCrabs and Neura ACE.
Self Adapting LLMs
A clear guide on self adapting llms that explains how models learn at runtime, safety checks to add, and a step by step plan to run safe updates.
subquadratic models
A clear guide to subquadratic models, why they matter, and how to test and build systems that handle very long AI inputs.
Qwen 3.7-Max-Preview: 1M Token Context Window Explained
Qwen 3.7‑Max‑Preview introduces a 1M token context window, enabling large‑scale text processing and smarter AI workflows.
Fast‑Slow Training Explained
Fast‑Slow Training is a new training method that keeps AI models from forgetting old tasks while learning new ones. It uses slow weights for core knowledge and fast weights for quick adaptation.
Long Context Models
Long Context Models let AI handle much larger text windows. This guide explains SubQ, mobile Gemini, Claude Dreaming, benchmarks, and clear steps to adopt long memory.
How SEAL Lets LLMs Learn on Their Own
MIT’s SEAL framework lets large language models generate their own training data and edit themselves, making AI systems more autonomous and cost‑effective.
Self Adapting LLMs Explained
A simple, practical guide to Self Adapting LLMs, with examples, workflows, and safety checks for teams building smart apps.
OpenCrabs AI Agent: Self‑Hosted Bot That Learns and Fixes Itself
OpenCrabs AI agent is a self‑hosted bot that learns from its own actions and fixes mistakes without human help. It runs locally, keeping data private while improving over time.
Mixture of Experts Guide
A clear, simple guide to Mixture of Experts models, including how they work, training tips, and deployment advice for teams.