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Provides a curated collection of hands-on tutorials, workflows and auxiliary files for training and using generative-model tooling (Stable Diffusion, Flux, WAN). Key items include a WAN 2.1 LoRA training tutorial and an articles collection covering DreamBooth, LoRA, LyCORIS and SDXL.
Visual, example-driven guide for using Claude Code: structured learning path, copy‑paste templates, and diagrams that show how to combine slash commands, hooks, subagents and MCP into production workflows.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.
Collection of hands-on workshop materials and sample code from Anthropic's "Code with Claude" series, covering Claude Managed Agents, memory (Dreaming Service), eval-driven agent development, and multi-agent patterns. Not maintained and not accepting contributions.
Frames AI research as a trainable practice of reading, building, debugging, and fast feedback. The essay is most useful for researchers learning how to avoid hype-chasing, benchmark tunnel vision, and agent-induced blind spots.