Full Open AI playground Complete Guide to OpenAI Playground: Multi-Agents, SDKs & No-Code Devop

Описание: Unlock the Power of OpenAI Playground & Multi-Agent Systems!
In this deep dive, we move beyond simple API keys to explore the full potential of OpenAI Playground and the OpenAI SDK. Whether you are a traditional coder or part of the "new breed" of low-code developers, this video breaks down how to build powerful AI apps—from fitness trackers to tax collectors—using multi-agent workflows.
We explain the crucial difference between standard APIs and the "Cloud Console" approach of the Playground. You'll learn how to structure your AI’s "brain" using different storage types, including Vector Stores, Fine-Tuning, and Batch Processing for handling massive datasets like Malayalam or Mandarin dictionaries.
Key Takeaways:
• OpenAI Playground vs. API: Why you need a centralized platform for your agents.
• Building AI Apps: How to wrap AI models (like NanoBanana) into consumer apps using tools like NextJS.
• Storage Masterclass: A breakdown of the 6 storage types—Assistant, Batch, Fine-Tune, Vision, User Data, and Events.
• Agent Workflows: Creating "Tax Collector" and "Accountant" agents that talk to each other.
• Debugging AI: Understanding Logs, Threads, and Traces (explained simply with a school attendance analogy!).
• MCP Servers: Solving the structured output problem when API versions change.
🌏 Global & Local AI Development: Whether you are building for a global audience or training models on specific regional languages (like Malayalam or Mandarin), this tutorial covers the async training and media capabilities you need to succeed.


00:00 Intro: OpenAI Playground vs. API Keys
02:15 The Google Cloud Analogy: Console vs. Docs
04:30 Architecture of an AI App: Fitness Tracker Example
07:10 The "New Breed" of Low-Code AI Developers
10:00 Storage 1: Assistant Storage (The Central Brain)
12:30 Storage 2: Batch Storage & Offline Learning
15:45 Storage 3: Fine-Tuning (DeepSeek & Neural Networks)
19:30 Storage 4: Vision (Crop Disease Scanner Example)
21:00 Storage 5: User Data (YouTube History Example)
22:30 Storage 6: Events (Evaluating Model Progress)
24:15 Workflow: Building Tax Collector & Accountant Agents
27:00 Understanding Logs: The School Attendance Analogy
30:00 Conversations & JSON Structured Outputs
31:45 Chat Kit & Threads: Using NotebookLM for Predictions
34:30 App Info & MCP Servers: The API "Translator"
39:00 Asynchronous vs. Synchronous Training Explained
41:30 Media Tabs: Training on Video, Audio & Images
44:00 Conclusion
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