Want to check this out, partially as an inducement to get me to do more walking - talk to something like Google Gemini on my phone. See also Coachbot history.

Feeding a large amount of personal context into an AI for an ongoing chat, especially for use with voice in earbuds, presents some challenges due to current technological limitations in AI memory and data handling. However, there are several strategies and emerging approaches that can help you achieve this to varying degrees.

Here's a breakdown of how you can approach it, considering current capabilities and future possibilities:

Q: how can I feed lots of personal context into you, to be used in an ongoing chat (incl by voice in earbuds)?

Current and Near-Term Strategies

1. Initial Large Text Dumps (with Limitations)

• How it works: In a text-based chat interface, you can often paste a significant amount of information at the beginning of a conversation. Many models can process a large "context window" (the amount of text they can consider at one time).
• Pros: Simple to execute for static information.
• Cons:
  • Context Window Limits: Even the largest context windows have a finite size (e.g., tens of thousands to hundreds of thousands of "tokens," which roughly correspond to words). You can't dump an entire life story at once without exceeding it.
  • Drift/Forgetting: As the conversation progresses, older information within that initial dump might "fall out" of the active context window, and the AI might forget details.
  • Retrieval, not Absorption: The AI "sees" the text but doesn't necessarily "learn" it in a permanent, integrated way. It's more about retrieving relevant snippets from the context you provided.
• Voice Integration: This initial dump would need to happen via a keyboard/text interface before you switch to voice.

2. Segmented Context Provision (Manual/Semi-Automated)

• How it works: Break your personal context into logical chunks (e.g., family history, professional background, hobbies, medical info). You can then:
  • Reference on Demand: Tell the AI, "Remember when I told you about my job as a marine biologist? I'm referring to that now."
  • Upload Smaller Files: If the platform supports it, upload documents (e.g., PDFs, text files) containing specific information as needed.
• Pros: More manageable than one giant dump; can refresh context as needed.
• Cons: Still relies on manual intervention and the AI's ability to retain context from prior turns or uploaded files.

3. Using AI's Built-in Memory/Custom Instructions

• How it works: Many advanced AI models (like Gemini, ChatGPT, etc.) are developing features for "memory" or "custom instructions." You can typically access these in the settings. Here, you can enter persistent information that the AI is supposed to remember across all your conversations.
• Examples of what to put: Your name, key relationships, general preferences, significant life events you want it to always be aware of.
• Pros: Designed for persistence; the AI is explicitly told to consider this information.
• Cons:
  • Limited Capacity: The amount of information you can put here is usually quite limited compared to the "lots of personal context" you're envisioning. It's for high-level, frequently relevant facts.
  • Platform-Specific: Availability and functionality vary by AI platform.
• Voice Integration: Once set, this memory will automatically be considered in voice conversations.

4. External Knowledge Base with Retrieval-Augmented Generation (RAG)

• How it works (Advanced/Developer-Oriented): This is the most robust current solution for "lots of personal context."
  1. Create a Knowledge Base: Store your personal context in a structured way (e.g., a database, a collection of text files, Markdown files).
  2. Vectorize: Use an embedding model to convert all your text data into numerical vectors.
  3. Retrieve: When you ask a question, your query is also vectorized. A search is performed against your personal knowledge base to find the most relevant chunks of information (based on vector similarity).
  4. Augment: These retrieved chunks are then provided to the AI model as part of its prompt, along with your original question.
  5. Generate: The AI uses this augmented prompt to generate its response.
• Pros:
  • Scalability: Can handle vast amounts of information (gigabytes of text).
  • Accuracy: Reduces hallucination by grounding the AI in your specific data.
  • Always Fresh: The relevant context is retrieved and provided with each query.
• Cons:
  • Technical Complexity: Requires some technical setup (coding, setting up a vector database, integrating APIs). Not a simple out-of-the-box solution for most users.
  • Requires a Hosting/Development Environment: You'd be building a custom application around the AI.
• Voice Integration: This RAG system would sit behind your voice interface. Your spoken query would trigger the RAG process, and the AI's response would be spoken back. This is how many advanced enterprise AI solutions are built.

Future Possibilities & Emerging Trends

1. Truly Persistent, Evolving AI Memory

• Concept: AI models that genuinely "learn" and integrate information over long periods, without constant re-feeding. This would involve more sophisticated internal memory architectures that can update and refine their understanding based on ongoing interactions.
• Current Status: Research is ongoing, but not yet widely available in consumer-grade AI.

2. AI Agents with Personalized Profiles

• Concept: AI systems that act more like personalized agents, maintaining a rich, constantly updated profile of your preferences, history, and goals. They would proactively use this information to tailor interactions.
• Current Status: Early stages of development. Some AI platforms offer limited "agent" capabilities, but comprehensive, self-updating personal profiles are still largely conceptual.

3. Seamless Integration with Personal Data Sources

• Concept: Direct, secure, and privacy-preserving integration with your personal data (calendar, email, health apps, smart home devices, photo library, etc.). The AI would be able to pull relevant context directly from these sources in real-time.
• Current Status: Limited integrations exist (e.g., AI assistants connecting to calendars), but broad, user-controlled integration across diverse personal data is a significant privacy and technical challenge.

Practical Steps for Voice in Earbuds

For voice interaction via earbuds, the key is that the AI processing happens in the cloud (or on a connected device), and the audio is merely the input/output interface.

1. Choose an AI platform that supports voice input/output: Most major AI models now have good speech-to-text and text-to-speech capabilities.
2. Use a compatible app/device: This could be:
   • The AI's official mobile app (e.g., ChatGPT app, Google Gemini app).
   • A third-party app that integrates with an AI API.
   • Smart earbuds with integrated AI assistants (e.g., Google Pixel Buds Pro with Google Assistant, Apple AirPods with Siri, though their "memory" might be less robust for deep personal context).
3. Feed context via text first: For current solutions, you will likely need to provide the initial "lots of personal context" through a keyboard interface on a computer or phone, either by pasting text, using custom instructions, or uploading files if supported.
4. Engage in voice chat: Once the context is set up in the backend, your voice interactions should leverage that information.

In summary, for "lots of personal context," the most effective current method is often a custom RAG solution if you have the technical skills. For a more user-friendly, off-the-shelf approach, leverage the AI's "custom instructions" or "memory" features for critical high-level facts, and be prepared to occasionally re-introduce specific context within a conversation if the AI seems to forget details from a large initial dump. The voice aspect is typically handled by the platform's standard speech-to-text/text-to-speech capabilities once the context is established.

Q: does Gemini have access to any context saved in NotebookLM?

Currently, Gemini (the general conversational AI) and NotebookLM (the AI-powered research and note-taking tool) are distinct products, though they are both powered by Gemini models and share some underlying technology.

Here's a breakdown of their relationship regarding context:

• NotebookLM uses Gemini models internally: NotebookLM leverages the power of Gemini models (like Gemini 2.0 Pro) to process your uploaded sources (Docs, PDFs, web pages, YouTube videos, audio files, etc.), summarize them, answer questions, and generate "Audio Overviews." It acts as a specialized AI grounded in your specific documents.
• Limited Direct Two-Way Integration (as of now): As a user, Gemini (the chatbot) does not automatically have direct access to the specific context saved within your NotebookLM notebooks. They are separate user interfaces and data silos, designed for different primary purposes.
  • Gemini is for broad conversational AI, general knowledge, creative writing, etc., drawing from its vast training data and, if you enable it, your Google Search history.
  • NotebookLM is your "personalized AI expert" on your uploaded sources.

However, there are ways they can interact or for you to leverage them together:

1. Manual Transfer: You can copy-paste information or summaries generated by NotebookLM into a Gemini chat session to provide context for Gemini. Conversely, you could copy text from a Gemini conversation and use it as a source in NotebookLM.
2. Shared Technology/Features: Features developed for NotebookLM, like "Audio Overviews" (generating podcast-style conversations from documents), have begun to "trickle down" into the broader Gemini experience, allowing Gemini users to generate similar audio summaries from uploaded documents.
3. Google AI Pro Subscription: Access to NotebookLM and enhanced Gemini features are often bundled under a "Google AI Pro" (formerly Google One AI Premium) subscription, indicating they are part of a larger AI ecosystem, even if not fully integrated for seamless context transfer.
4. Enterprise/Education Context: For Google Workspace business and education users, there are often more robust data protection and integration features where Gemini and NotebookLM operate within the organization's existing data controls. In these environments, while still distinct, the underlying data handling might be more unified.

In essence: While both are Google AI products and use Gemini technology, Gemini the chatbot doesn't inherently "see" your NotebookLM content unless you explicitly move it between the two platforms or use features that have been specifically integrated from NotebookLM into the Gemini app. NotebookLM's strength lies in its ability to become an expert on your specific, uploaded knowledge base, with citations, which is a more controlled and grounded experience than a general AI chat.