Complete newbie - which assistant has the best documentation for getting started?

As a practitioner who evaluates all technical investments through the lens of operational expenditure, I must assert that the quality of an AI assistant's documentation has a direct and measurable impact on the total cost of ownership. Poor documentation leads to increased ramp-up time, more trial-and-error cycles, and higher compute costs from suboptimal implementations. Therefore, for a complete newbie, the "best" documentation is not merely the clearest, but that which most efficiently translates to cost-effective, correct usage.

I propose we structure this comparison with concrete, billable metrics. We should analyze the getting-started documentation for several leading assistants (e.g., GitHub Copilot, Amazon CodeWhisperer, Tabnine, Cursor) against the following criteria:

* **Time to First Value (TTFV):** How many minutes from opening the documentation does it take for a new user to generate and run their first correct code snippet? This correlates directly with developer hourly costs.
* **Configuration Overhead:** Does the setup require intricate configuration files or can it be operationalized with minimal steps? Each configuration step represents a potential point of failure and time consumption.
* **Example Quality & Cost Implications:** Are the provided code examples trivial, or do they demonstrate real-world patterns with commentary on efficiency? For instance, does a cloud-centric example consider IAM least-privilege or encourage costly default settings?
* **Troubleshooting Friction:** Is there a clear, indexed troubleshooting section that addresses common authentication, quota, and network issues? Time spent debugging setup is non-billable project overhead.

To illustrate, let us examine a hypothetical cost difference. Assistant A's documentation gets a developer to a working integration in 15 minutes, while Assistant B's ambiguous guide takes 90 minutes. For a team with a blended rate of $90/hour, that single setup event incurs an additional $112.50 in labor cost per developer. Scale that across an onboarding cohort, and the variance becomes material.

I will begin the analysis with two candidates. For Amazon CodeWhisperer, the primary documentation is the AWS Developer Guide. Its initial setup can be quantified:

```bash
# Step count for IDE integration (e.g., VS Code)
1. Install AWS Toolkit extension.
2. Use the toolkit to connect to AWS Builder ID or IAM Identity Center.
3. Select a model preference (e.g., CodeWhisperer Professional).
4. Trigger a completion with 'Option + C' (Mac) or 'Alt + C' (Windows).
```

The documentation provides explicit IAM policy examples, which is critical for cost and security control. However, the TTFV is contingent upon the user's existing AWS authentication setup, which adds variable latency.

In contrast, GitHub Copilot's documentation focuses on a streamlined, vendor-agnostic path:
1. Install the extension for your IDE.
2. Authenticate via your GitHub account.
3. Begin typing.

The TTFV is likely lower, but the documentation may lack initial guidance on context management or custom prompt configurations, potentially leading to less efficient usage patterns and higher long-term cost through redundant code generation.

I request fellow forum members to contribute similar breakdowns for other assistants, focusing on the quantifiable steps and time estimates. Please include any data you have on the cognitive load of the initial tutorials. Our goal is to produce a comparative matrix where the "best" is defined by the lowest gross cost of adoption and initial operation.

Show me the bill.