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About
We work in industry, but come from academia.
Our founder Eric loves learning and sharing new ideas. He has degrees in computer science, mathematics, and physics. While in academia, Eric spent a great deal of time helping other students via tutoring and TAing for many STEM courses both through university and privately.
But, there was a problem. Although great resources exist for people learning maths for standard courses (e.g. calculus and ODEs), such material for proof-based classes is void. This shortcoming is, in part, due to the different nature of proof-based courses. Eric had a brutal transition when he started his first real analysis course. It felt like proofs were just obscure paragraphs about quoting theorems and too much was “left as an exercise to the reader.” The struggle to overcome this barrier and see the patterns took Eric an extraordinary amount of time. Sadly, too often undergrads are dissuaded from mathematics for this same situation: not being able to interact with resources (e.g. tutors) that can help put abstract ideas in forms they understand (e.g. intuitive diagrams).
Eric knows this struggle personally and believes one important pain can be alleviated: confusion about how to write proofs. The majority of problems in standard real analysis classes have common patterns that can be replicated to provide templates for thinking about new problems. Undoubtedly, a great amount of effort and failed attempts are required to succeed in math studies. However, students can be empowered right from the start with simple explanations; Typal was founded for this purpose. Since the ability to ask questions is also essential, a discussion platform for peer math students was created. Students can post inquiries or read through prior posts and know you are not alone in your math struggles.
Road Map. Our plan is to focus on applied, proof-based subjects with great utility in industry. We begin with analysis as this is a core tool. All topics on our road map are as follows.
  1. 1.
    Real Analysis (2022)
  2. 2.
    Optimization (Late 2022)
  3. 3.
    Algorithmic Game Theory (2023)
  4. 4.
    Zero Knowledge Proofs (Late 2023)
Last modified 2mo ago
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