'Pierce Brown, the author of the *"Red Rising"* series, is a celebrated `American` science fiction writer known for his captivating storytelling and intricate world-building. Born on January 28, 1988, in Denver, Colorado, Brown developed a passion for writing at a young age, inspired by the works of **J.R.R. Tolkien** and **George R.R. Martin**. He pursued his `education` at *Pepperdine University*, where he honed his skills in political science and economics, which later influenced the complex socio-political landscapes in his novels. Brown\'s debut novel, *"Red Rising,"* published in 2014, quickly gained a dedicated following, praised for its gripping narrative and richly developed characters. The series, set in a dystopian future where society is divided by color-coded castes, explores themes of power, rebellion, and identity. Brown\'s ability to weave intense action with profound philosophical questions has earned him critical acclaim and a loyal fanbase. Beyond writing, Brown is known for his engaging presence on social media, where he interacts with fans and shares insights into his creative process. His work continues to resonate with readers worldwide, solidifying his place as a prominent voice in contemporary science fiction literature.',
token:'1. **Determine how a "customer" is identified**\n',
delayMs: 500
},
{
token:
"Looking at the database context, I can see there's a `customer` model that serves as the comprehensive customer model for customer relationship management and purchase behavior analysis. ",
token:'The customer is identified by `customerid` which is a unique identifier. ',
delayMs: 600
},
{
token:
'The customer model also has relationships to `person` (for individual customers) and `store` (for store customers), as well as connections to `sales_order_header` for tracking customer orders. ',
token:'2. **Determine metric for "top customer"**\n',
delayMs: 500
},
{
token:
'The user is asking for their "top customer" which typically means the customer who has generated the most value for the business. ',
delayMs: 700
},
{
token:'Looking at the available precomputed metrics, I can see several relevant options:\n',
delayMs: 500
},
{
token:
'- `customer_all_time_clv` - calculates total revenue generated by each customer over their entire history\n',
delayMs: 600
},
{
token:
'- `customer_lifetime_value` - estimates total revenue a business can expect from a customer throughout their relationship\n',
delayMs: 600
},
{
token:'- `customer_lifetime_orders` - tracks total lifetime orders\n\n',
delayMs: 500
},
{
token:
'The most appropriate metric would be total revenue generated, which aligns with the `customer_all_time_clv` model that calculates "the total revenue generated by each customer over their entire history." ',
delayMs: 900
},
{
token:
'This is the most direct measure of who the "top customer" would be from a business value perspective.\n\n',
delayMs: 600
},
{
token:'3. **Determine the visualization type and axes**\n',
delayMs: 500
},
{
token:
'Since the user is asking for "my top customer" (singular), this indicates they want to identify a single customer. ',
delayMs: 700
},
{
token:
'According to the visualization guidelines, when a user requests a single item based on a metric (like "the top customer"), I should use a number card that displays both the item\'s name and metric value. ',
delayMs: 900
},
{
token:
'This would show something like "Top Customer: [Customer Name] - $[Revenue Amount]".\n\n',
delayMs: 600
},
{
token:
"Now that I've considered the TODO items, I need to determine if further reasoning or validation is needed. ",
delayMs: 700
},
{
token:'All three TODO items appear to be well-supported by the available documentation:\n',
delayMs: 500
},
{
token:'- Customer identification is clear through the customer model\n',
delayMs: 400
},
{
token:'- The metric is available through the precomputed customer_all_time_clv model\n',
delayMs: 400
},
{
token:
'- The visualization type is clearly defined by the guidelines for singular requests\n\n',
delayMs: 500
},
{
token:'The documentation appears sufficient to conclude my prep work. ',
delayMs: 600
},
{
token:
'I have a clear path forward: use the customer_all_time_clv precomputed metric to identify the customer with the highest total lifetime value, and display this in a number card format showing both the customer identifier and their total revenue value.',
'```python\ndef fibonacci(n):\n """Generate Fibonacci sequence"""\n a, b = 0, 1\n for _ in range(n):\n yield a\n a, b = b, a + b\n\n# Print first 10 numbers\nfor num in fibonacci(10):\n print(num)\n```\n\n',
{token:'| Data A | Data B | Data C |\n',delayMs: 200},
{token:'| Info X | Info Y | Info Z |\n\n',delayMs: 200},
// Horizontal rule
{token:'## Horizontal Rule\n\nAbove the line\n\n---\n\nBelow the line\n\n',delayMs: 300},
// Mixed content - large chunk
{
token:
"## Complex Example\n\nHere's a paragraph with **bold**, *italic*, and `code` mixed together. We can reference [links](https://example.com) and use math expressions like $E = mc^2$.\n\n",
delayMs: 400
},
// Nested structures
{token:'### Nested Structures\n\n',delayMs: 150},
{token:'1. **First item** with *emphasis*\n',delayMs: 200},
{token:' - Sub-point with `code`\n',delayMs: 150},
