We invented noise cancellation technology that became a significant breakthrough in mobile audio, which we initially stumbled upon while trying to solve a problem.

We started as a company focusing on text-to-speech, then expanded to speech-to-text, creating an orchestration that combines both technologies.

We're currently working on a project called Henson, which aims to get deep learning models to generate new sounds, focusing on improving the quality and usability of the generated audio.

We decided to stop working on our initial business model and pivot to something else, which involved a lot of difficult decisions, including letting go of our team.

Using background noise in voice applications can make users more accepting of latency, improving their overall experience.

We initially explored automating mechanical engineering but pivoted to focus on programming, recognizing the potential for AI in that space.

The journey to discovering the potential of language models was not straightforward; we initially explored various other technologies.

Initially, we attempted to create a co-pilot for mechanical engineering, but we pivoted to focus on coding because we were more passionate about it and recognized the potential for AI to transform programming.

We realized that the existing AI models were not sufficient for our needs, prompting us to develop our own models to enhance product quality.