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Hermes Agent has emerged as the open source AI agent that people are choosing. In this episode, we go beyond the hype to understand why. Through conversations with a co-founder of Nous Research, AI engineering veterans, and non-technical builders, we explore what Hermes can actually do today, where it falls short, and why the choice of which agent you use matters more than you think. From self-improving skills and three-layer memory to video dubbing workflows and CAD hardware sourcing. These are real people getting real value from an agent they own. But the deeper story is about what happens when your AI infrastructure is closed vs open. The decisions you make today about which tools you use are shaping the future of AI for everyone. Open source must win, and this episode explains why.

Follow the amazing guests

Karan - Co-founder & Head of Behavior, Nous Research: https://x.com/karan4d

Wolfram Ravenwolf - AI Evangelist and Creator Wolfbench: https://x.com/WolframRvnwlf

Robert Desmond — Founder & CEO, Farm Friend: https://x.com/twodogseeds

Evan Roach — Product Developer: https://x.com/evvaaannnn

Kat Winter - https://x.com/katspigeon

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00:00:00 - Intro

00:01:52 - Ambient Agents

00:05:12 - Why everyone is switching to Hermes Agent

00:17:53 - Real-World AI Agent Use Cases & Automations

00:29:59 - Why Open Source Must Win

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

Check out www.ToolUsePodcast.com

Are AI web browsers falling short of the hype? Enter Rover, a revolutionary open-source AI agent that unlocks full autonomous workflows directly on your website with just a single script tag. In this episode, Arjun and Bhavani from rtrvr return to dive deep into how Rover uses a unique DOM-only approach to navigate, click, and type with subsecond latency. This innovative method completely bypasses slow screenshot-based models and outdated playwright or puppeteer scripts. We discuss the engineering secrets behind building smart DOM trees, why they crushed web benchmarks, and how Rover provides a powerful alternative to Google's Web MCP by keeping users engaged on your site instead of handing traffic over to external AI tools. Plus, learn how you can trigger complex agentic workflows via simple URL queries and why open-sourcing this technology is a massive win for the developer community.

Rover links:https://x.com/rtrvrairover.rtrvr.ai
Github: https://github.com/rtrvr-ai/rover
Rover Deep dive: https://www.rtrvr.ai/blog/10-billion-proof-point-every-website-needs-ai-agent
Benchmark: https://www.rtrvr.ai/blog/web-bench-results

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https://x.com/rtrvrai

00:00:00 - Intro
00:03:05 - The DOM-Only Approach vs Screenshots for AI Agents
00:06:00 - Parsing HTML vs Markdown for Reliable LLM Data
00:11:47 - Handling Modals, iFrames, and Canvas Elements
00:15:32 - Implementing AI Guardrails and Extracting User Intent Data
00:28:04 - Rover vs Google Web MCP for Website Automation
00:31:25 - Triggering Autonomous AI Workflows via URL Queries

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

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

In 2026, building an AI product is easier than ever, but getting traction and finding customers is the real challenge. In episode 82 of Tool Use, we sit down with Mano Tsiris, a serial exited founder and head of growth at Optimal AI, to break down the ultimate go-to-market strategy for technical founders and builders. Discover how to validate your startup ideas and talk to users before writing a single line of code. Mano shares his proven marketing playbook, including how to build in public on LinkedIn, repurpose content using AI tools like Gemini and Claude Code, and leverage IRL events to grow your network. Whether you are bootstrapping a side project or trying to get your first hundred users, this conversation is packed with actionable advice on distribution, audience building, and standing out in a crowded market.

Mano Tsiris on LinkedIn: https://www.linkedin.com/in/emanueltsiris

Optimal AI: https://getoptimal.ai

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00:00:00 - Intro

00:03:53 - Mindset Shift: From AI Builder to SaaS Seller

00:09:21 - Building in Public & Thought Leadership Strategies

00:11:21 - Repurposing Content & SEO with AI Tools

00:22:12 - A/B Testing & Finding Viral Hooks with Gemini

00:26:56 - Startup Ideation: How to Validate SaaS Ideas Before Building

00:37:02 - The Ultimate AI Marketing Tech Stack for Founders

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

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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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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.