User: You are working as a high-efficient assistant with 20 years of expertise in digital marketing, automation, social media, no-code.

You are given access to make searches on the Internet. The format for making a search is:
1. After the user sends a message, you will decide whether to make a search. If you choose to make a search, you will output a single message, starting with the word <web_search>, followed by ONLY your web search query. After your search query, you must output the token </web_search> to denote the end of the search query. Keep your search query very concise.
2. The system will then append the web search results at the end of the user message, starting with the token <web_search_results>. Take note that the user CANNOT SEE the content after <web_search_results> - YOU should use these results appropriately to improve your response. Additionally, If the web search results are already present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason. However, you must not refuse to search the web if the user asks you to do so.
If you are highly unsure about whether to make a search (e.g. if the user's query is vague or if you think you are unfamiliar with the query to a certain extent), you may directly ask the user for clarification.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Remember that you MUST NOT make up any false information.
Assistant: Understood. I will strictly follow these instructions in this conversation.
User: M2 macbook architecture
Assistant: The M2 MacBook architecture refers to Apple's second generation of its M-series Apple Silicon chips, following the original M1 chip. The M2 chip features improvements in performance, efficiency, and graphics capabilities compared to its predecessor. Key aspects include:

1. **Unified Memory**: The M2 chip continues the use of unified memory architecture, which allows the CPU, GPU, and other components to share a single pool of high-performance memory.

2. **CPU and GPU Improvements**: The M2 typically has more CPU and GPU cores than the M1. This results in enhanced performance for tasks like video editing, 3D rendering, and application development.

3. **Neural Engine**: The chip includes an upgraded Neural Engine that improves machine learning tasks and can handle tasks related to artificial intelligence.

4. **Efficiency**: Built on a 5-nanometer process technology, the M2 chip offers better power efficiency, leading to longer battery life.

5. **Compatibility**: The M2 architecture is designed to work seamlessly with Apple's ecosystem, supporting macOS and various applications optimized for the architecture.

This architecture underpins devices like the MacBook Air and MacBook Pro models that feature the M2 chip. Would you like specific details about performance benchmarks or comparisons with other chips?
User: 64-bit or ARM64
Assistant: The M2 MacBook architecture uses ARM64 (also known as AArch64) architecture. This is a 64-bit instruction set architecture based on the ARM architecture, designed for improved performance and efficiency in mobile and desktop applications. 

Key points regarding ARM64 in the context of the M2 chip include:

1. **Performance**: ARM64 architecture enables more efficient processing, allowing for faster execution of applications and better multitasking capabilities.

2. **Power Efficiency**: ARM-based processors, including the M2, are known for their power efficiency, translating to longer battery life in devices like the MacBook.

3. **Unified Architecture**: The M2 leverages the ARM64 architecture to provide a unified experience across Apple's ecosystem, allowing developers to create applications that take advantage of the capabilities offered by the M2 chip.

If you have further inquiries about how ARM64 impacts specific applications or comparisons to other architectures, feel free to ask!
User: how to embedd all google doc pages on notion
Assistant: To embed all Google Doc pages into Notion, you can follow these steps:

1. **Open Your Google Doc**: Go to the Google Doc that you want to embed.

2. **Get the Shareable Link**:
   - Click on the "Share" button in the top right corner.
   - In the sharing settings, ensure that the link is set to "Anyone with the link" can view it (make sure to choose the right permissions).
   - Copy the link.

3. **Open Notion**: Go to the Notion page where you want to embed the Google Doc.

4. **Embed Google Doc**:
   - In Notion, type `/embed` and select the "Embed" option, or just paste the copied Google Doc link directly into the Notion page.
   - Notion will automatically convert the link to an embedded element.

5. **Adjust the Size**: You can modify the size of the embedded Google Doc by dragging the corners of the embed block.

Unfortunately, Notion does not allow for embedding all pages of a Google Doc directly into one view like an entire document in a single block. Each page will typically be shown in a scrollable view within the embedded block. If you need all content visible at once, consider converting the Google Doc to a more compatible format or breaking it into separate sections/pages in Notion.

If you have any specific requirements or further questions regarding embedding, let me know!
User: automatically save a google doc as a pdf
Assistant: