> For the complete documentation index, see [llms.txt](https://docs.hawky.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hawky.info/10.9.2024-hawky-launch-on-ethervista.md).

# 10.9.2024 Hawky launch on EtherVista

### Current state of Artificial Intelligence in Web3

Over the past year, there's been a notable increase in AI-based products driven by the rapid evolution of Web3 technologies. However, only a few of these products are developing use cases that genuinely benefit users today.

In reality, many projects are still primarily using these AI products for influence or experimentation, without achieving significant and sustainable results. This has prevented these products from becoming essential tools for projects.

### Why so many products aren't working

The main issue is that many businesses are not truly focused on creating functional solutions. Instead, they are primarily trying to capitalize on the hype surrounding AI for short-term gains. This trend leaves project owners and businesses struggling to find products that genuinely solve problems and harness the full potential of AI.

### How does Hawky solve this?

We adhere to a proven business philosophy that focuses on solving problems through straightforward yet impactful solutions. Our unwavering commitment is to deliver enhanced value for both our users and investors.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.hawky.info/10.9.2024-hawky-launch-on-ethervista.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
