Practical AI: Machine Learning, Data Science
Technology
Recently a16z released a diagram showing the “Emerging Architectures for LLM Applications.” In this episode, we expand on things covered in that diagram to a more general mental model for the new AI app stack. We cover a variety of things from model “middleware” for caching and control to app orchestration.
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Show Notes:
Emerging Architectures for LLM Applications
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Timestamps:
(00:07) - Welcome to Practical AI
(00:43) - Deep dive into LLMs
(02:25) - Emerging LLM app stack
(04:35) - Playgrounds
(08:07) - App Hosting
(10:46) - Stack orchestration
(15:50) - Maintenance breakdown
(19:08) - Sponsor: Changelog News
(20:43) - Vector databases
(22:36) - Embedding models
(24:27) - Benchmarks and measurements
(26:59) - Data & poor architecture
(29:42) - LLM logging
(33:01) - Middleware Caching
(37:32) - Validation
(40:53) - Key takeaways
(42:36) - Closing thoughts
(44:23) - Outro
AI in the U.S. Congress
First impressions of GPT-4o
Full-stack approach for effective AI agents
Autonomous fighter jets?!
Private, open source chat UIs
Mamba & Jamba
Udio & the age of multi-modal AI
RAG continues to rise
Should kids still learn to code?
AI vs software devs
Prompting the future
Generating the future of art & entertainment
YOLOv9: Computer vision is alive and well
Representation Engineering (Activation Hacking)
Leading the charge on AI in National Security
Gemini vs OpenAI
Data synthesis for SOTA LLMs
Large Action Models (LAMs) & Rabbits 🐇
Collaboration & evaluation for LLM apps
Advent of GenAI Hackathon recap
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