SUN
Personalized AI audio. Harvard CS, Stanford AI PhD, ex-Amazon Podcasts founding engineer.
About
Audio has always been static, limited, and the same for everyone. SUN makes it personal, dynamic, and always up to date. Create podcasts, audiobooks, or structured audio experiences on any topic. Customize everything, from length and voice to language. Ask questions at any moment and get answers in real time. Not sure what to create? SUN knows what to play. Connect the apps you already use, and it continuously adapts to your interests, delivering audio tailored to you. Built by Artin Bogdanov (Harvard CS), Mehmet Günhan Ertosun (Stanford PhD in AI), and Amy Hua (founding engineer at Amazon Podcasts), SUN combines deep technical expertise with real insight into how people consume audio.
From their website
sunapp.ai ↗SUN offers personalized AI-driven audio courses that let users generate, explore, and learn across topics. The platform emphasizes intelligent audio for learning and growth.
Users engage with AI-generated audio courses that cover any topic. The product supports creating and exploring audio content, enabling learning through personalized, intelligent audio experiences designed to help users learn anything daily.
Who it’s for: Individuals seeking self-directed, topic-varied learning through AI-generated audio content
- AI-generated audio courses
- Personalized learning experiences
- Content generation and exploration capabilities
- Topic coverage across any subject
- Daily learning growth prompts
Founders with strong academic and industry background; product-oriented focus on learning audio
Founders · 2
2x Founder | Co-founder @ SUN (a16z Speedrun SR006) | Stanford Eng. PhD | Former Venture Builder @ $2.5B VC | AI & Generative AI | Scientist | Inventor | Author | Meditation Enthusiast Entrepreneur, AI technologist, and Stanford-trained scientist (more than 400 scientific citations and multiple patents) working at the intersection of artificial intelligence, startups, and product innovation. Previously worked as a venture builder at a $2.5B venture capital fund, where I helped incubate and prototype multiple AI and frontier-technology startups: leading technical development from idea → prototype → MVP. Earlier in my career I led AI and data science initiatives in finance, healthcare and medical imaging, building AI & machine learning systems for next-generation technologies. My technical work spans: • Generative AI (LLMs, diffusion models, multimodal systems) • Machine learning, deep learning & data science • AI product development & startup incubation • Medical imaging and healthcare AI • Advanced modeling, simulation, and statistical learning Prior roles include Principal Scientist (AI/Data Science) at medical imaging companies and founding an AI healthcare startup applying deep learning to radiology. I received my PhD in Electrical Engineering from Stanford University, where my research focused on advanced device physics, modeling, and emerging technologies. My postdoc at Stanford School of Medicine involved applications of machine learning, AI and data science to medical domain. Later, I was a research fellow at Stanford School of Medicine at Stanford Cancer Imaging Training (SCIT) Program, supported by the National Cancer Institute, with my research focussed on applications of deep learning to medical imaging. Fun fact: I had published three papers at international prestigious journals while I was still an undergraduate student. I am also an author and researcher interested in the intersection of technology, philosophy, and human knowledge.
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