Inspiration
As software gets more sophisticated, the price generally goes higher. As students, especially in college, many do not have enough funds to buy said software, and often times the trial period is too short to truly be able to learn how to use said skill. To find these free alternatives, one would have to spend hours searching on Google and sketchy websites. We wanted to streamline this process and help other students get to these resources so that they may be able to learn new and interesting skills.
What it does
Free Engine helps you find free alternatives to paid services like adobe photoshop, lightroom, etc.(many many more). We also provide you with the ability to expand our dataset by providing recommendations to add more services. When you are confident about your interest in a certain hobby, they can invest in more professional and paid options.
How we built it
We used a react framework that used Node JS, HTML, and Styling to build the UI. We used React specifically to make the website scalable as React automatically "generates" code for all elements in the data set. Finally, we used the Google API to search through our existing dataset.
Challenges we ran into
Embedding the programmable search engine into our website was challenging, however, a mentor guided us in the right direction. We also tried to put a dark mode/light mode switch feature, but that did not end up happening as we realized it takes much more time.
Accomplishments that we're proud of
We finally got the web page running!! Integrating the programmable search engine was a tedious task and we're glad that we finally got it.
What we learned
We expanded upon our skills in Javascript, Node JS/React, CSS, HTML, and Git. We also learned the basics of integrating an API into our project.
What's next for Free Engine
Further integrating Google search dataset with the React Set. Additionally, we want to enhance the styling and filtering of the search results by creating a custom and unique search. Finally, we can always refine the UI further, expand the dataset, and test for bugs - essentially maintaining code.
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