Let's fix ChatGPT's greatest design sins. We'll design and build a working app that makes ChatGPT multi-modal and multi-model. And no, you don't need to know what those words mean to use it.

Download the source code: https://github.com/bholmesdev/fixgpt

References from this video: - Try https://warp.dev to vibe code your own solution - Watch Scott and Mark's podcast episode, "how to not ship the org chart:" https://www.youtube.com/watch?v=Z1yYcUFzH2A - Read "Why is AI marketing so, so bad?" by Evan Armstrong at The Leverage: https://www.gettheleverage.com/p/why-is-ai-marketing-so-so-bad

Forget the hype of AI automation replacing jobs. The future lies in human augmentation — revealing blind spots, sparking creativity, and amplifying thoughtful decision-making. In this talk, we’ll explore the principles that distinguish augmentation from automation in AI UX design, covering interaction patterns, design principles, and trust-building feedback loops. Drawing from real-world experiences building AI-powered tools and beyond, we’ll dive into concepts for crafting interfaces that empower users to think smarter, not just work faster. Expect practical insights and a fresh perspective on AI’s role as a collaborative partner.

AI Augmentation: https://jytan.net/blog/2025/ai-augmentation/ Tusk: https://www.usetusk.ai/

Building effective AI agents isn’t about using the next biggest LLMs in the market - it’s about creating self-improving systems with data flywheels. By continuously learning from real-world data and agent interactions, these flywheels help evaluate, retrain, and optimize smaller, faster models that match the performance of large LLMs - at a fraction of the cost and compute.

In this video, learn how NVIDIA uses data flywheels and NeMo microservices to run efficient AI agents with lower TCO and faster inference. Explore a thoughtful framework on building a data flywheel for your own AI agent systems.

aiagents #dataflywheel #generativeai #modeldistillation #nvidia

Open-RAG-Eval is an open-source framework that revolutionizes RAG evaluation by harnessing the power of LLM judges for scalable, automated evaluation without the need for golden answers or golden chunks. Building on pioneering research from the University of Waterloo, this framework integrates innovative tools like UMBRELA for reference-free relevance scoring and AutoNuggetizer for automated fact-checking. Designed with a flexible connectors architecture, it seamlessly plugs into any RAG pipeline while delivering fast, transparent, and interpretable metrics on retrieval, generation, and hallucination in RAG.

Since Alibaba launched the Qwen series of large models in 2023, the Qwen series of large language models and multimodal large models have been continuously updated and improved. This presentation will introduce the latest developments in the Qwen series of models, including the large language model Qwen3, vision-language large model Qwen2.5-VL, omni model Qwen2.5-Omni, etc. Additionally, this presentation will also cover the future development directions of the Qwen series.