How to use AI to work with arbitrage ?

Greetings on behalf of the XHive team.
How can you use artificial intelligence to increase your arbitrage profits?
AI is not only the future, but also the present in arbitrage.
Neural networks are constantly evolving and updating. In 2023, AI capabilities have grown by leaps and bounds.

Text Tasks:

  • Writing SEO-text, product card text and comments.
  • Creating a portrait of CA.
  • Communicating with customers.
  • Building conversion forecasts and analyzing behavioral metrics.
  • Maintaining documentation and creating email templates.
  • Writing simple code and building websites.

Graphing Tasks:

  • Creating and editing pictures, photos and videos.
  • Analyzing competitors, trends and audience requests.
  • Conducting A/B testing and selecting the best marketing strategies.
  • Artificial intelligence saves arbitrageur’s time, helping to increase conversion rate, sales volume and average check.

All AI services can be categorized into groups based on what they can do:

For word processing:

  • NeuralText
  • SEOwind
  • LanguageTool
  • Anyword
  • CopyMonkey

To work with pictures:

  • Midjourney
  • Wepik
  • DALL-E 2

To work with websites:

  • 10Web
  • Webullar
  • Hostinger

To work with chatbots:

  • Chatfuel
  • MobileMonkey

Unique ChatGPT:

  • Preparation of texts for creatives, lendings and prelendings.
  • Search for information and ideas for the site: design, structure, headings.
  • Generating images according to specified parameters.
  • Creating, editing and optimizing codes in different programming languages.

ChatGPT can easily handle any of these tasks, which will help to relieve the arbitrageur’s time.
Note: Each service is customized for specific tasks, choose one according to your needs.

Case 1: How to create prelending for a gut-offer using AI

In order to create a gut-officer landing page, the web determined which services would be suitable for the task at hand. He settled on:

  • GPT-3.5 for code generation, as it handles this task better than the next model;
  • GPT-4 for content generation;
  • DALL-E for working with images.

There is no difference between using GPT through the official website or through a bot in Telegram. Here it’s as convenient as you like. In the case study, all requests were sent via Telegram.

Let’s start by developing a structure

The web recommends starting prelending development with structure. Here you can take the simple or the complex path.

Simple option: send GPT-3.5 a link to a template and ask it to generate something similar. Web request and neural network response may look like this:

A complicated variant: come up with the structure yourself, describe to the neural network the idea of future prelending and ask for ideas. This is the request of the web and this is the response of the neural network:

Design work

This is how you can work out the design of the whole site, make colors, fonts, places for photos, sections and other parts of the page. To see what the result is, change the format of the text file to “.html” and open it with a browser.

Content preparation

After the basic web page preparation, I started preparing the content. For image development I used a neural network based on DALL-E. Example of query and result:

When preparing the text, rely on the developed structure of the site. AI can be used to create content for any section:

The main thing in working with a neural network is to check the result. The system is not perfect yet, and there may be logical errors in the texts. After editing the text, paste it into the prepared structure code. Customize the markup – send pieces of code to the neural network and ask to edit it to add lists or, for example, bold. When the content is ready, it remains to add links to the main landing page and lay out the page.

Results

Neural networks are not standing still. Of course, they don’t work perfectly, and often the web has to clean up after the AI, but it’s a matter of time. Already now, webs are actively integrating neural networks into their work and getting good results. The most pleasing result is the time savings. Neural networks with subscriptions can produce more content in less time, but for the web this is still a big plus. No need to spend budget on designers and raters. I gave the task to ChatGPT – he put together the structure of the landing page, gave the ready code, filled the site with the necessary text. I gave Midjourney the task – the visual content is ready.

The XHive team wishes you a Happy New Year!

We wish you happiness, health, success and prosperity in the New Year! May it bring you much joy, pleasure and profit!

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