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Open Source AI In 17 Minutes
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Open Source AI In 17 Minutes

What is Open Source AI and Why Should You Care?

Definition of Open Source AI

  • Open source AI refers to AI systems with publicly available core components, including model architecture, weights, training code, and licenses for use and modification.
  • In contrast, closed source AI has proprietary models where users access them only via APIs or enterprise platforms (e.g., GPT from OpenAI).

Evolution of Open Source vs. Closed Source

  • Since 2022, closed source AI dominated due to superior performance until January 2025 when DeepSync R1 emerged as a competitive open-source model.
  • This shift opened opportunities for more open-source models to appear on leaderboards.

Advantages of Open Source AI

  • Full control over deployment: Users can run models on-premises or in private clouds without vendor lock-in.
  • Cost-effectiveness: Open source solutions are significantly cheaper than their closed counterparts.
  • Auditable nature: Essential for regulated industries like healthcare and finance; promotes transparency.

Geopolitical Context and Market Trends

  • A16Z reports that around 80% of startups may be using Chinese open-source models due to rising quality and geopolitical factors.
  • The Western world is responding by investing more in open-source technologies.

Challenges of Using Open Source AI

Setup Complexity

  • Higher setup complexity compared to closed models; requires additional steps for access.

Hardware Requirements

  • Running these models demands robust hardware; limitations exist based on the size of the model one can operate.

Performance Limitations

  • Out-of-the-box capabilities may be weaker than those offered by established closed-source options like Claude or GPT.

Management Responsibilities

  • Users must manage security, scalability, and uptime themselves since no centralized management exists for open-source solutions.

The Future of Open Source AI

Rapid Improvements

  • Innovations such as inference quantization are making it possible to run advanced models on personal computers.

Community Contributions

  • Developers are creating frameworks that simplify setup and maintenance processes for users interested in leveraging open-source technology.

Call to Action

  • The speaker encourages viewers to explore building with open-source models now that accessibility has improved significantly.

AI Tools and Technologies for Building Open Source Models

Overview of AI Dating Apps and Open Source Models

  • Discusses the concept of an AI dating app that matches profiles using AI technology, highlighting the limitless possibilities in this domain.
  • Emphasizes the importance of open source models as foundational elements in building various AI applications, noting their evolving nature.

Leading Open Source Models

  • Mentions notable open source models to watch as of February 2026: Kimmy models from Moonshot AI, GLM models from Japu AI (now Z.AI), and Hunya models from Tencent for image processing.
  • Acknowledges that while model popularity may change over time, the usage methodology remains consistent across different platforms.

Utilizing Model Managers

  • Introduces OAM as a popular model manager necessary for local use of these open source models; installation is straightforward.
  • Highlights the significance of running powerful AI models locally on personal computers, enhancing user experience with advanced capabilities.

Building Agentic Systems

  • Advocates for creating agentic systems to fully leverage open source models' potential by integrating tools, memories, and knowledge into their functionality.
  • Clarifies that open source agents are not fundamentally different from closed-source agents; principles learned can be applied universally across both types.

Components of an AI Agent

  • Lists essential components required to build an AI agent: models, tools, knowledge and memory, audio/speech capabilities, guard rails, and orchestration methods.
  • Notes that while using open source agents requires specific software like Olama for model management, other components remain unchanged from traditional setups.

Tools and Frameworks for Open Source Agents

  • Presents various compatible tools and technologies available for developing AI agents; mentions NA10 and OpenAI's SDK as examples supporting open-source frameworks.
  • Points out that many popular orchestration tools can also work with open-source models effectively.

Dominance in Coding Arena

  • Observes a significant presence of open-source coding tools within the market; highlights a recent event hosted by Lonely Octopus showcasing innovative projects developed quickly using these resources.

New Resources for Productivity Enhancement

  • Introduces HubSpot's Claude Co-work Stack featuring 12 advanced prompts designed to streamline productivity tasks such as content analysis and competitive intelligence reporting tailored to user needs.
  • Emphasizes how these prompts facilitate actionable deliverables by working directly with users' files and data contextually relevant to their operations.( t = 528 s)

Demonstration Time: Building an Open Source Agentic Workflow

  • Begins a demo showcasing how to create an agentic workflow utilizing local financial statements without compromising privacy through closed-source solutions.( t = 592 s)

AI-Driven Financial Analysis and Email Management

Overview of Monthly Spending Insights

  • The AI agent analyzed financial statements for July and August, revealing that digital services and travel were the largest expenses.
  • Recommendations include cutting non-essential digital subscriptions and downgrading unused software, potentially saving up to $2,253 annually.
  • The workflow is hosted locally, ensuring privacy as no data leaves the machine; it operates completely free of charge.

Enhancements for Local AI Agents

  • To improve the AI agent's performance, integrating additional financial documents like bank statements can provide a more comprehensive overview.
  • Users are encouraged to download Oola Lama and NA10 to build local agents effectively; Oola manages open-source AI models while NA10 assists in orchestrating various components.

Setting Up Local AI Workflows

  • A self-hosted AI starter kit simplifies setup by bundling necessary tools like N8N and Oola together with PostgreSQL for data management.
  • After installation, users can create workflows or import existing ones from files provided in the description.

Demonstration of Email Agent Workflow

  • A demo showcases an email agent workflow that processes unread emails using Python with Olama and OpenAI agents SDK.
  • The agent drafts responses based on incoming emails; for instance, it created a reply for an interview invitation at Lonely Octopus.

Importance of Email Screening

  • The email agent flags potential security risks by identifying automated job listings or suspicious content within emails.
  • This tool significantly enhances productivity by managing high volumes of emails efficiently, especially beneficial for small teams handling numerous inquiries.

How to Build a Multi-Agent System with OpenAI's SDK

Overview of Data Privacy and Cost

  • The speaker discusses the challenges of using data for AI systems, emphasizing that privacy concerns prevent companies from effectively utilizing data without significant costs.
  • They highlight the financial burden of monitoring emails and drafting replies, which could be mitigated by local hosting solutions.

Introduction to Multi-Agentic Systems

  • The speaker introduces a multi-agent system built using OpenAI's agents SDK and Olama with the Quen 38B model, noting its flexibility to switch models easily.
  • They mention that this is not an AI coding tutorial but express willingness to create one if there is interest from viewers.

Resources for Learning

  • Additional resources such as videos, live streams, and a 28-day boot camp are offered for those interested in deeper learning about building agents.
  • The boot camp has limited spots (100 per cohort), ensuring personalized support and community engagement for participants.

Potential of Open Source AI

  • The speaker emphasizes the promise of open-source AI in addressing privacy and cost concerns while integrating AI into workflows.
  • They believe that open-source solutions represent a significant step forward in overcoming hesitations related to adopting AI technologies.
Video description

Free resource to unlock the Claude Cowork Stack and replace a week of work 👉 https://clickhubspot.com/129e64 🤖 Want to get ahead in your career using AI? Join the waitlist for my AI Agent Bootcamp: https://www.lonelyoctopus.com/ai-agent-bootcamp In this video I cover open source AI essentials, including what they are, the open source AI stack, and how to use the models and build agents with no code and code. 🤖 Want to get ahead in your career using AI? Join the waitlist for my AI Agent Bootcamp: https://www.lonelyoctopus.com/ai-agent-bootcamp 🤝 Business Inquiries: https://tally.so/r/mRDV99 🖱️Links mentioned in video ======================== Ollama: https://ollama.com/ N8n: n8n self hosting guide: https://docs.n8n.io/hosting/ N8n AI Starter Kit: https://github.com/n8n-io/self-hosted-ai-starter-kit Financial statements workflow: https://drive.google.com/file/d/1EQCUGjEloN2VLRaKFTyvS-iQJvoOHT-k/view?usp=sharing Email agent: https://github.com/hellotinah/email-agent-workflow Videos about Building AI Agents: https://youtu.be/qU3fmidNbJE?si=jABbSPW7GwKOyvaw https://youtu.be/DV0Ln7HRyJQ?si=1dbihPPRVF4jS5LY https://youtu.be/_Udb5NC6vTI?si=1lAQHDzC6WPd-I9W https://youtu.be/ftBWgcwvEk4?si=4fKhdVwjdqUHOSyw 🔗Affiliates ======================== My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ 365 Data Science: https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina 🎥 My filming setup ======================== 📷 camera: https://amzn.to/3LHbi7N 🎤 mic: https://amzn.to/3LqoFJb 🔭 tripod: https://amzn.to/3DkjGHe 💡 lights: https://amzn.to/3LmOhqk ⏰Timestamps ======================== 00:00 Intro 00:34 Defining Open Source AI 04:44 Quiz 1 04:48 Open Source AI Stack 09:44 No-Code Demo: Financial Document Analyzer (n8n + Ollama) 13:10 Code Demo: Email Agent Workflow (Python + Ollama + OpenAI Agent SDK) 17:26 Quiz 2 📲Socials ======================== instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ tiktok: https://www.tiktok.com/@hellotinahuang discord: https://discord.gg/5mMAtprshX 🎥Other videos you might be interested in ======================== How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s 🐈‍⬛🐈‍⬛About me ======================== Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person! 📧Contact ======================== youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: tina@smoothmedia.co ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)