Gincker AI

A Playground for AI and Machine Learning

Bringing Developers and Users Together

Providing the Same Development Environment to Developers and Users

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Create and Test Machine-Learning Algorithims without Code

Train Machine-Learning Models with Your Own Data, Visualize Machine-Learning Results in Realtime

Gincker AI makes machine learning model trainining as easy as using a calculator, and ML result delivery and sharing as simple as posting a Tweet. With Gincker AI, you can create and test a variety of machine learning algorithms, ranging from simple to sophisticated, just by typing in a mathematic formula, pasting a dataset, or uploading a data file; you don’t need to write a single line of code or rely on any special software package.

  • Simple Interface: Gincker AI converts different machine learning models into templates. It implements a common interface and standardized input/output format by encapsulating and centralizing all the programming and implementation details internally. As long as you work through one template, you will be able to use all the other templates to create and test your machine learning algorithms. This is because all templates on Gincker share the same user interface and input/output formats. You can simply go to, select a proper template, and create and test your desired machine learning algorithm out of the box.
  • Software as a Template: Over 40 templates on the current release of Gincker AI not only show the power and capability of the platform, but also illustrate the procedure and format used in creating those machine learning models. These templates cover a broad range of machine learning applications, including classifications, regressions, supervised and unsupervised machine learning, neural networks for classification and regression, convolutional neural netwroks for handwriting recognition, self-organization maps for color clustering, traveling salesman problem, etc. In addition, more templates will be added to Gincker over time.
  • Customization: Gincker AI provides rich configuration options that allows you to customize your output from trained machine learning models. For example, Gincker provides 13 different categorical color schemes that can be used to specify colors for different chart series. Gincker also consists of over 100 different colormaps that define the color schemes for various types of visualizations, such as surface and heatmap charts. You can also customize the other elements for your result charts, such as chart title, axis, gridline, background color, number of grid points, etc.

Output from Gincker AI - A Unique URL Link

Delivering Dynamic Content to Users

Gincker AI allows you to save your work to gincker - a unique URL link. The saved gincker can not only provide output results, but also bring the development environment directly to users. Users can use the gincker to regenerate, manipulate, modify, and customize the machine learning algorithms

  • Dynamic Results: The output from Gincker is a URL link called gincker. This gincker can be embedded in websites, incorporated into applications, or integrated into research papers. Users can also bookmark the gincker for later use or share it with friends, colleagues, or other users. The advantage of ginckers over the static output results from traditional software packages is that a gincker can bring the development environment to users and deliver dynamic content - users can use gincker to regenerate, manipulate, modify, or customize the results to meet their requirements.
  • Effective Communication Tool: The gincker has an extremely small file size - it is simply a single line of text with the format: "{template}#{xxxxxxxxxx}", which is shorter than a Tweet. It consists of three parts:, which is our website name, the template name, and ten randomly generated characters that are used to identify the gincker. Therefore, exchanging ginckers over the Internet will be much faster and more efficient than thansmitting large graphics or image files. The gincker links will become an effective communication tool in our daily life.

Gincker's Future

Can be Any App, Will Be Everywhere, and for Everyone

Gincker key features: distributed platform, easy-to-use, and dynamic output. These features make Gincker suitable not only for graphics and quantitative finance but also for other fields.

  • Any App: Gincker is not limited to graphics and technical analysis, it can have wide applications in other fields. In future releases, we plan to add templates to Gincker for the following fields: numerical methods, education, mathematical and physics modeling, artificial intelligence (AI), and other scientific and engineering fields. In fact, we can convert any software package, business application, or software service into templates and integrate them into Gincker. This means any application is simply a template in Gincker.
  • For Everyone: In the future, We will keep Gincker as a SaaT (software as a template)-based web-distributed platform that does not need to install anything on a user’s local machine. We will continue to improve Gincker’s user interface and put the easy-to-use feature as our top priority. Gincker will further encapsulate and centralize all the programming complexities and implementation details internally, while exposing a simple and common interface to users. Gincker is for everyone - using Gincker is as simple as using a calculator.
  • Everywhere: Due to Gincker's ability to create and deliver dynamic content, we will gradually make Gincker an effective communication tool in our daily life. For example, when communicating with colleagues to discuss computation results or a stock trading strategy, you do not need to send an email with a large attachment anymore; simply include a gincker in your email instead.
  • Smart Gincker As the number of templates and ginckers on the platform increases, we will gradually make Gincker become a true expert system. It will be able to pick and recommend the right templates to users according to user’s background and other attributes. Gincker will be smart enough to generate the output results automatically by following the user’s specifications or simple descriptions without the need to provide a mathematic formula or input data.

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Support Gincker Development

Developing and maintaining the Gincker platform take a lot of time and effort. If you find this platform useful and would like to help the project, you can contribute funding the project development by donating through Paypal: