Tom and Cam explore recent AI advancements, with particular focus on the Kimi model, its capabilities, and developer implications. They address SaaS industry challenges, including rising customer acquisition costs and the trend toward consumption-based pricing models. The conversation highlights developers' growing influence in AI technology development and the critical role of customer retention in SaaS business success. They also discuss enterprise AI adoption, RAG (Retrieval-Augmented Generation) applications, and effective data vectorization techniques. Additional topics include Cognition's acquisition of Windsurf, the continuing importance of ETL processes, and how local models can improve data processing efficiency. Throughout their discussion, they emphasize the value of layered data management approaches and how traditional methods remain relevant alongside emerging technologies.
Chapters
00:00 Introduction and Technical Setup03:56 Exploring the Kimmy Model15:46 Developer-Centric AI Models23:25 Rapid Development in AI Tools25:00 Exploring Kimmy's Capabilities29:21 SaaS Industry Challenges and Changes33:23 Customer Acquisition Cost Insights38:13 The Future of SaaS in an AI-Driven World42:47 RAG and Vectorization in AI Development59:18 Understanding UMAP and Clustering in Data Representation01:02:14 Building a Mobile Inspection Tool for Real Estate01:05:23 Transforming Natural Language into Structured Data01:09:46 The Importance of ETL Processes in AI01:14:50 Defining Effective ETL Pipelines01:20:23 Exploring RAG and Its Applications01:28:39 The Role of Vector Stores in Data Management
Links
https://github.com/lmcinnes/umap
https://pair-code.github.io/understanding-umap/
https://www.pinecone.io/learn/vector-database/
LLM vectorization - https://bbycroft.net/llm
UMAP - Vizualisation of embeddings, Nomic Atlas Vizualisation - https://atlas.nomic.ai/data/andrewgao22/hacker-news/map
https://projector.tensorflow.org/
Example Superlinked Demo -https://hotel-search-recipe.superlinked.io/
https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally
https://developers.googleblog.com/en/gemini-embedding-available-gemini-api/
https://moonshotai.github.io/Kimi-K2/
https://platform.moonshot.ai/docs/introduction#text-generation-model
https://docs.superlinked.com/getting-started/why-superlinked
Keywords
AI, Kimi2 Model, SaaS, Technology, Coding, Developer Tools, Machine Learning, Open Source, API, Performance, SaaS, AI adoption, cloud computing, RAG, vectorization, ETL, Cognition, Windsurf, local models, data processing