Open Source AI Fundamentals In 17 Minutes | Tina Huang | Tina Huang

The podcast elucidates the burgeoning realm of Open Source AI, highlighting its increasing relevance and accessibility. It contrasts Open Source AI with its closed-source counterparts, emphasizing advantages like full control, customization, cost-effectiveness, auditability, and fostering innovation. The discussion points out that Chinese models are leading in the open-source arena. Despite drawbacks such as setup complexity and hardware requirements, advancements are rapidly mitigating these issues. The podcast also explores the Open Source AI stack, focusing on models, model managers like OLAMA, and agentic systems, providing practical demonstrations of building open-source AI agents with and without code, utilizing tools like NA10 and OpenAI's Agents SDK.

Outlines

Part 1: Introduction, Context

Part 2: Analysis, Challenges

Part 3: Tools, Frameworks

Part 4: Practical Demos, Implementation

Part 5: Conclusion, Outlook

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Open Source AI Fundamentals In 17 Minutes | Tina Huang

Tina Huang

Part 1: Introduction, Context

McDonald's Big Gouda and Big Tasty Red Steakhouse Football Snack

Defining Open Source AI: Advantages Over Closed Source Systems

The Rise of Chinese Open Source AI Models and Data Privacy

Part 2: Analysis, Challenges

Open Source AI Cons: Complexity, Hardware Needs, and Shrinking Drawbacks

Open Source AI Stack: Building AI Agents and Applications

Part 3: Tools, Frameworks

Key Open Source Models, Model Managers, and Agentic Systems

Building Open Source Agents: Components, Tools, and AI Coding

Open Source AI Coding Tools and HubSpot's Cloud Co-Works Stack

Part 4: Practical Demos, Implementation

No-Code Demo: Building an Open Source Agentic Workflow with OLAMA and NA10

Setting Up Local Agents: OLAMA, NA10, and the Self-Hosted AI Starter Kit

Code Demo: Building an Email Agent Workflow with Python, OLAMA, and OpenAI Agents SDK

Part 5: Conclusion, Outlook

Benefits of Open Source AI: Privacy, Cost, and Future Potential

Open Source AI Fundamentals In 17 Minutes | Tina Huang

Tina Huang

Part 1: Introduction, Context

00:00McDonald's Big Gouda and Big Tasty Red Steakhouse Football Snack

McDonald's Big Gouda and Big Tasty Red Steakhouse Football Snack

00:59Defining Open Source AI: Advantages Over Closed Source Systems

Defining Open Source AI: Advantages Over Closed Source Systems

03:07The Rise of Chinese Open Source AI Models and Data Privacy

The Rise of Chinese Open Source AI Models and Data Privacy

Part 2: Analysis, Challenges

04:11Open Source AI Cons: Complexity, Hardware Needs, and Shrinking Drawbacks

Open Source AI Cons: Complexity, Hardware Needs, and Shrinking Drawbacks

05:26Open Source AI Stack: Building AI Agents and Applications

Open Source AI Stack: Building AI Agents and Applications

Part 3: Tools, Frameworks

06:20Key Open Source Models, Model Managers, and Agentic Systems

Key Open Source Models, Model Managers, and Agentic Systems

07:39Building Open Source Agents: Components, Tools, and AI Coding

Building Open Source Agents: Components, Tools, and AI Coding

10:03Open Source AI Coding Tools and HubSpot's Cloud Co-Works Stack

Open Source AI Coding Tools and HubSpot's Cloud Co-Works Stack

Part 4: Practical Demos, Implementation

11:37No-Code Demo: Building an Open Source Agentic Workflow with OLAMA and NA10

No-Code Demo: Building an Open Source Agentic Workflow with OLAMA and NA10

13:24Setting Up Local Agents: OLAMA, NA10, and the Self-Hosted AI Starter Kit

Setting Up Local Agents: OLAMA, NA10, and the Self-Hosted AI Starter Kit

15:07Code Demo: Building an Email Agent Workflow with Python, OLAMA, and OpenAI Agents SDK

Code Demo: Building an Email Agent Workflow with Python, OLAMA, and OpenAI Agents SDK

Part 5: Conclusion, Outlook

17:20Benefits of Open Source AI: Privacy, Cost, and Future Potential

Benefits of Open Source AI: Privacy, Cost, and Future Potential