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1 America’s Sweethearts: Dallas Cowboys Cheerleaders Season 2 - Tryouts, Tears, & Texas 32:48
#004 AI with Supabase, Postgres Configuration, Real-Time Processing, and more
Manage episode 428522580 series 3585930
Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:
- Core components and how they power real-time AI solutions
- Optimizing Postgres for AI workloads
- The magic of PG Vector and other key extensions
- Supabase’s future and exciting new features
Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:
- Core components and how they power real-time AI solutions
- Optimizing Postgres for AI workloads
- The magic of PG Vector and other key extensions
- Supabase’s future and exciting new features
59 פרקים
Manage episode 428522580 series 3585930
Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:
- Core components and how they power real-time AI solutions
- Optimizing Postgres for AI workloads
- The magic of PG Vector and other key extensions
- Supabase’s future and exciting new features
Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:
- Core components and how they power real-time AI solutions
- Optimizing Postgres for AI workloads
- The magic of PG Vector and other key extensions
- Supabase’s future and exciting new features
59 פרקים
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1 #052 Don't Build Models, Build Systems That Build Models 59:22

1 #051 Build systems that can be debugged at 4am by tired humans with no context 1:05:51

1 #050 Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 1:06:57

1 #050 TAKEAWAYS Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 11:00

1 #049 BAML: The Programming Language That Turns LLMs into Predictable Functions 1:02:38

1 #049 TAKEAWAYS BAML: The Programming Language That Turns LLMs into Predictable Functions 1:12:34

1 #048 Why Your AI Agents Need Permission to Act, Not Just Read 57:02

1 #047 Architecting Information for Search, Humans, and Artificial Intelligence 57:21

1 #046 Building a Search Database From First Principles 53:28

1 #045 RAG As Two Things - Prompt Engineering and Search 1:02:43

1 #044 Graphs Aren't Just For Specialists Anymore 1:03:34

1 #043 Knowledge Graphs Won't Fix Bad Data 1:10:58

1 #042 Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs 1:33:43

1 #041 Context Engineering, How Knowledge Graphs Help LLMs Reason 1:33:34

1 #025 Data Models to Remove Ambiguity from AI and Search 58:39

1 #024 How ColPali is Changing Information Retrieval 54:56

1 #023 The Power of Rerankers in Modern Search 42:28

1 #022 The Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It) 46:05

1 #021 The Problems You Will Encounter With RAG At Scale And How To Prevent (or fix) Them 50:08

1 #020 The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval 52:15

1 #019 Data-driven Search Optimization, Analysing Relevance 51:13

1 #018 Query Understanding: Doing The Work Before The Query Hits The Database 53:01


1 #017 Unlocking Value from Unstructured Data, Real-World Applications of Generative AI 36:27

1 #016 Data Processing for AI, Integrating AI into Data Pipelines, Spark 46:25

1 #015 Building AI Agents for the Enterprise, Agent Cost Controls, Seamless UX 35:11

1 #014 Building Predictable Agents through Prompting, Compression, and Memory Strategies 32:13

1 Data Integration and Ingestion for AI & LLMs, Architecting Data Flows | changelog 3 14:52

1 #013 ETL for LLMs, Integrating and Normalizing Unstructured Data 36:47

1 #040 Vector Database Quantization, Product, Binary, and Scalar 52:11

1 #039 Local-First Search, How to Push Search To End-Devices 53:08

1 #038 AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It 1:14:23

1 #037 Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces 49:12

1 #036 How AI Can Start Teaching Itself - Synthetic Data Deep Dive 48:10

1 #035 A Search System That Learns As You Use It (Agentic RAG) 45:29

1 #034 Rethinking Search Inside Postgres, From Lexemes to BM25 47:15

1 #033 RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) 51:25

1 #032 Improving Documentation Quality for RAG Systems 46:36

1 #031 BM25 As The Workhorse Of Search; Vectors Are Its Visionary Cousin 54:04

1 #030 Vector Search at Scale, Why One Size Doesn't Fit All 36:25

1 #029 Search Systems at Scale, Avoiding Local Maxima and Other Engineering Lessons 54:46

1 #028 Training Multi-Modal AI, Inside the Jina CLIP Embedding Model 49:21

1 #027 Building the database for AI, Multi-modal AI, Multi-modal Storage 44:53

1 #026 Embedding Numbers, Categories, Locations, Images, Text, and The World 46:43
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