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Vector databases can be an important component for building reliable AI agents and scalable semantic search applications. In this episode, Arjun Patel from Pinecone breaks down how to optimize your RAG pipeline, choose the right embedding models (sparse vs. dense), and implement effective chunking strategies for better data retrieval. We also explore the new Pinecone plugin for Claude Code, demonstrating how to build a recommendation system and chat with your documents using Pinecone Assistant without writing complex code.

https://www.pinecone.io/

https://www.linkedin.com/in/arjunkirtipatel/

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00:00:00 - Intro

00:01:11 - What Vector Databases Unlock

00:04:40 - Optimal Chunking Strategies for RAG

00:09:07 - How Embedding Models Work

00:17:25 - Improving Search with Re-ranking

00:26:52 - SQL vs Vector Database Architecture

00:35:48 - Claude Code & Pinecone Assistant Demo

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Join the Tool Use Discord: https://discord.gg/PnEGyXpjaX

Unlock the power of AI fine-tuning for image and video models with Greg Schoeninger, CEO of Oxen.ai. In this episode, we explore how to move beyond simple prompt engineering to training custom open-source models for as little as $1. Discover the technical strategies for building high-quality datasets, the trade-offs between LoRAs and full fine-tunes, and how to achieve consistent characters and styles in generative video.

We dive into real-world examples, including how to generate massive product catalogs for a fraction of the cost of enterprise APIs and the story behind the viral "Isometric NYC" project built with Claude Code. Greg breaks down the entire AI lifecycle—from data curation and labeling with Vision Language Models (VLMs) like Qwen and Gemini to deploying efficient, specialized models that outperform general-purpose giants. Whether you are looking to optimize GPU costs, automate video workflows, or build your own AI tools, this conversation provides the blueprint for scaling your AI capabilities.

Links from the episode:

Oxen AI: https://www.oxen.ai/

Fine Tuning Fridays: https://luma.com/oxen

Isometric NYC Project: https://cannoneyed.com/projects/isometric-nyc

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https://x.com/gregschoeninger

00:00:00 - Intro

00:01:47 - When to Switch from Prompt Engineering to Fine-Tuning

00:11:05 - How to Build a Dataset for AI Fine-Tuning

00:21:52 - LoRA vs. Full Fine-Tuning Explained

00:27:53 - Case Study: Fine-Tuned Qwen 3 VL vs. Google Gemini

00:36:33 - Case Study: Isometric NYC with Claude Code

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Tool Use is a weekly conversation with the top AI experts.

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Unlock the ability to build AI tools without coding experience as Justin Borge joins Tool Use to demonstrate how he built Helios, a personal management system, using only LLMs like Gemini and Claude. Justin breaks down his transition from marketer to growth engineer, sharing his exact workflows for using JSON documentation to give AI a perfect memory of his business and goals. He reveals his unique "bullying" strategy for debugging code using persona-based prompts and explains how he created a human-in-the-loop system to safely edit local files without hallucinations. This conversation covers everything from writing your first Python script to mastering meta-prompting for maximum productivity.

https://www.linkedin.com/in/justinborge/

https://justinborge.com

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00:00:00 - Intro

00:03:49 - Top AI Tools & JSON Workflows

00:10:06 - Transitioning from Marketing to Engineering

00:12:23 - How to Debug Code Without Coding Knowledge

00:13:56 - Persona Prompting & "Bullying" LLMs

00:18:09 - Live Demo: Helios AI Management System

00:31:29 - How to Start Building AI Tools Today

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Tool Use is a weekly conversation with the top AI experts.