Tool Use - AI Conversations

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Are you looking to optimize your AI applications with the best open source Large Language Models? This week, we explore open source AI, rapid model switching, and local self-hosting with Nathan Break, a Machine Learning Engineer at Mozilla AI and the creator of Any-LLM. Any-LLM is an open-source Python SDK that allows developers to seamlessly talk to any LLM provider through a single, unified interface.

Nathan demonstrates how developers can instantly swap between cloud-hosted AI providers and local models like Llamafile by changing just a single line of code. We dive into the growing capabilities of smaller, cost-effective models, how to reduce your API expenses, and the security benefits of running local AI on your own hardware. Nathan also introduces the Any-LLM Platform, a secure vault for managing API keys that tracks your token usage and enforces budget limits without ever accessing your prompt data. Finally, we check out Porch Songs, Nathan's open-source application that leverages LLMs to rewrite the lyrics of popular songs while maintaining their original rhyme schemes and chord charts.

Check out the projects mentioned in this episode:
Any-LLM GitHub: https://github.com/mozilla-ai/any-llm
Any-LLM Platform: https://any-llm.ai
Mozilla AI Blog Post: https://blog.mozilla.ai/introducing-any-llm-a-unified-api-to-access-any-llm-provider
Porch Songs GitHub: https://github.com/njbrake/porchsongs

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00:00:00 - Intro
00:01:25 - What is Any-LLM? Unified API for Open Source AI
00:05:32 - Benefits of Rapid Model Switching & Open Weight Models
00:11:06 - Any-LLM Python SDK Demo: Mistral Image Recognition
00:19:51 - Best Local AI Models: Llamafile, LM Studio & Local LLMs
00:24:38 - Any-LLM Platform Vault & Porch Songs App Demo
00:34:45 - AI Coding Agents, Claude Code & LLM Evaluations

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

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Are you putting your business at risk by relying on big AI companies? In this episode of the Tool Use podcast, we dive deep into Sovereign AI with Max McCrea, founder of Greyhaven. We explore how businesses can maintain control of their private data while still benefiting from AI. Max breaks down the importance of self-hosting and building architectures that abstract away the model provider so you can easily swap them out. We also discuss practical AI architecture decisions, the dangers of un-sandboxed AI agents, and how to set up software-defined sandbox environments to reduce the attack surface. Whether you are curious about running open source models or replacing proprietary tools, this episode provides a guide to taking back control of your systems.

Guest Links:
https://greyhaven.co
https://maxmccrea.com
https://github.com/GreyhavenHQ

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00:00:00 - Intro
00:12:17 - AI Context Management & Personal Data Indexing
00:20:40 - Open Source Replacements vs Proprietary SaaS Tools
00:29:14 - Corporate AI Data Harvesting & Vendor Lock-in
00:49:18 - LLM Orchestration & AI System Architecture
00:53:24 - Best Open Source AI Models for Self-Hosting

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

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

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.

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

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

Subscribe for more insights on AI tools, productivity, and growth engineering.

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