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Explain what an AI model is, how it differs from the app interface, what training parameters and foundation models mean, how language image speech audio classification and prediction models differ, why modern assistants combine model types, and why larger models are often slower and costlier but more capable.

Explain what ChatGPT is and how it works, including the difference between ChatGPT and the underlying GPT model, how prompts become tokens, how the model predicts output, how safety training and tools affect answers, why web search matters for current facts, why hallucinations happen, and why model choice affects speed and capability.

Explain AI wrapper apps, including API-based products built on OpenAI Google Anthropic or other models, why wrappers are not automatically bad, how to judge whether the app adds workflow automation integrations prompts data or convenience, how outages reveal dependencies, and why pricing may exceed direct chatbot use.

Explain AI agents in plain language, including the difference between answering and acting, autonomous multi-step tool use, research agents browser agents coding agents task agents, why agents should pause before risky actions, and how to try built-in agent or deep research features safely.

Explain AI tokens with examples, including why tokens are not exactly words or letters, how token limits context windows and API pricing work, why input and output tokens matter, how file uploads and long chats consume tokens, a rough word-count estimate, tokenizer tools, and why non-English text can use more tokens.

Explain large language models in plain language, including how LLMs power ChatGPT Gemini Claude and Copilot, how they predict tokens, what parameters and training mean, why they are useful for language tasks, where they struggle, why they hallucinate, what knowledge cutoffs mean, and how small and large models differ.

Explain neural networks without math, including nodes layers training connection weights pattern recognition deep learning, how image recognition speech transcription chatbots recommendations spam filters and fraud detection use them, why they are not artificial brains, and why large networks require GPUs data and energy.

Understand what a Copilot+ PC means, which chips qualify, why the NPU and 40 TOPS requirement matter, which Windows 11 AI features need Copilot+ hardware, when Snapdragon X Arm compatibility matters, and whether better battery life or local AI features justify choosing one.

Explain Perplexity Spaces as project folders for searches files and custom instructions, use Spaces for multi-step research, create a Space with name description instructions and uploads, run searches inside the Space, share Spaces with view or contribute permissions, use source citations as a running bibliography, understand free versus Pro upload caps, and archive or rename finished Spaces.

Explain Gemini Gems as custom Gemini assistants with saved instructions, use built-in Gems such as Writing Editor Coding Partner and Learning Coach, create a new Gem with a name and instructions, optionally upload reference files when supported, edit delete or share Gems in Workspace, understand Gems are not separate models and have normal usage limits, and manage Google app connections separately.