Deep Siic V3: A Revolutionary AI Tool

Introduction to Deep Siic V3

• The speaker introduces Deep Siic V3 as a groundbreaking tool in artificial intelligence, claiming it surpasses industry giants like GPT-4 and ChatGPT 3.5.

• Emphasizes the historical significance of this moment in AI development and promises to demonstrate how to maximize the use of this open-source model.

Performance Comparison with Other Models

• Highlights that Deep Siic V3 excels in key benchmarks, outperforming competitors while being significantly cheaper at $0.014 per million tokens.

• Explains the architecture of Deep Siic V3, which utilizes a "mixture of experts" approach, consisting of multiple smaller models specialized for different tasks (e.g., math, chemistry).

Benchmark Achievements

• Notes that Deep Siic V3 shows superior performance in various benchmarks such as MLU and GPQ Diamond, even leading in ethical AI assessments.

• Mentions its recognition as the most ethical model on the market and its strong performance in competitive programming evaluations.

Handling Complex Problems

• Discusses how Deep Siic V3 achieves perfect scores on complex benchmarks designed to evaluate long prompts without losing information.

Considerations Regarding Data Privacy

• Warns about potential government oversight due to its Chinese origins, indicating that user data may be accessible by authorities if requested.

• Concludes that users should assume their data will be used for training purposes and could be subject to governmental access.

Open Source Benefits

• Advocates for open-source models like Deep Siic V3, emphasizing transparency and accessibility amidst rapid advancements in AI technology.

• Argues that decentralizing advanced AI models can mitigate risks associated with single entities controlling powerful technologies.

Practical Applications of Deep Siic V3

Automation Example Using Make

• Introduces a simple automation scenario where emails are automatically forwarded to an AI model for response generation.

Setting Up Automation

Open Router: Enhancing Automation with Language Models

Introduction to Open Router

• Open Router allows the use of various language models in automation scenarios, providing a fallback mechanism if the chosen model fails due to token limits or saturation.

• Users can create chat completions and connect their accounts easily, with access to numerous free and paid models, enhancing flexibility for beginners.

Selecting the Right Model

• The recommended model is Dipsi v3, which features automatic foldback. This ensures that if Dipsi fails, Open Router will find a similar performing model to continue tasks seamlessly.

• Dipsi v3 excels in handling long contexts, making it ideal for coding tasks and integration with no-code tools like Zapier.

Cost Efficiency and Use Cases

• Utilizing Dipsi can significantly reduce costs compared to other models while enabling various automations such as text correction, landing page creation, and social media copy generation.

• To connect with Open Router, users must generate an API key. This process involves creating a key named "Dipsi prueba" for automation purposes.

Setting Up Webhooks

• Users may need to deposit funds into Open Router; however, initial credits are often sufficient for experimentation.

• A custom webhook is created to capture emails sent to a specific address. This setup allows real-time information processing from another application.

Testing Email Automation

• After configuring the webhook, users can test it by sending a sample email. The subject line "prueba" and body text "testeando dipsi" should reflect correctly in the scenario setup.

• Each email received at this address can trigger automated responses generated by Dipsi based on user-defined parameters.

Configuring Dipsi's Response Style

• Users can instruct Dipsi on response tone—professional or casual—by defining prompts that specify desired styles for replies.

• Organizing workflow elements with clear naming conventions enhances management efficiency within automation setups.

Final Steps in Automation Setup

• A well-defined prompt helps ensure responses align perfectly with user expectations regarding formality and clarity.

Creating Draft Emails with AI Automation

Setting Up the Outlook Module

• The process begins by adding the Outlook module to the scenario, selecting the option to create a draft message.

• Users must connect their Microsoft account and set up the email subject (e.g., "Response generated by Deeps") and content.

• Additional settings include adjusting email priority (low, medium, high) and entering recipient details.

Automating Email Responses

• Once configured, any forwarded email to a specific webhook will trigger an automated response saved as a draft in Outlook.

• An example scenario is provided where a user receives an email requesting collaboration for YouTube videos but prefers not to respond manually.

Testing the Automation

• The user forwards a hypothetical sponsorship request email to the designated webhook address with specific instructions for response.

• After setting up user information and confirming content generation from the assistant, they execute the scenario.

Reviewing Generated Drafts

• Upon sending a test message, users can observe how requests are processed in real-time within their setup.

• The system successfully generates drafts based on received emails; however, it notes that some fields may be empty if not filled out initially.

Finalizing Responses

• To respond effectively using automation, users simply need to forward emails to the configured webhook address.