Stability AI

ComfyUI Full Workshop — first workshop from ComfyAnonymous himself!

Jul 19, 2025 · 51:25

ComfyAnonymous and Yedrick Kosinski present ComfyUI, an open-source node-based canvas for generative AI that has become the top 150 most popular GitHub repos with 78,000 stars and 3 to 4 million active users. Started as a personal project in January 2023, it now supports image, video, audio, 3D, and text models, with 22,000 custom nodes from 3,000 developers. A key feature is embedding full workflows into generated files for easy sharing. The team discusses its story, including ComfyAnonymous's time at Stability AI and founding ComfyOrg, and addresses questions on CFG, VAEs, LoRAs, control nets, API nodes for remote generation, and future plans for cloud inference and improved onboarding.

From Software Developer to AI Engineer: Antje Barth

Jul 24, 2024 · 19:48

Antje Barth, a Principal Developer Advocate for generative AI at AWS, outlines five practical steps from software developer to AI engineer: understanding foundation models, getting hands-on with AI developer tools like Amazon Q Developer, prototyping with models via Amazon Bedrock, integrating agents, and staying up to date with community events. She demonstrates Amazon Q Developer's ability to generate, explain, and transform code, reducing unvaluable tasks from 70% to focus on creative work. Barth introduces Amazon Bedrock's unified Converse API for standardized model invocation across providers like Anthropic's Claude 3.5 Sonnet, and shows how agents can be built to control a Minecraft bot through reasoning and tool use. The episode concludes with the announcement of the AI Engineering Hub at the AWS loft in San Francisco for community workshops and events.

[Workshop] AI Engineering 101

Nov 6, 2023 · 3:02:24

In this hands-on workshop, AI engineer Noah Hein teaches the basics of AI engineering by building five projects with OpenAI's GPT-3, GPT-4, Dall-E, Whisper, and Telegram. Participants create a Telegram bot that uses GPT-3 for chat, implements retrieval-augmented generation (RAG) with embeddings and cosine similarity on MDN docs, generates code with GPT-4 using few-shot prompting, creates images via Dall-E 2, and transcribes voice with Whisper. The session explains core concepts like tokens, chunking, context windows, temperature, and top-p, emphasizing that embeddings and prompt engineering are key to performance. Hein demonstrates that these tools are cheap and accessible—the entire workshop costs about $0.05 in API fees—and shows how AI engineers can integrate multiple models into a single application.