Entrepreneurship

When you want to look for user pain points, go to a bar in the middle of the week, look for the most desparate looking person and buy them a beer. They’d probably have something substantial to talk about. While I don’t usually go to bars during weekdays, I’ve found some similar ways of identifying user needs, mostly through conversations or online forums. This is a running document of the user needs or pain points that I’ve found to be interesting. Perhaps some of them will turn out to be viable business oppportunities. ...

Content recommendation system leveraging embedding similarity for personalized content recommendation.

Interactive chat interface with multiple AI agents, enabling dynamic conversation flows and specialized problem-solving capabilities.

Content recommendation system based on dynamic user profiles.

Interactive learning aids for reading comprehension and engagement.

Every now and then, I come across products or features, where the user action is probably not what the designer has envisioned. Some demonstrate the ingenuity of users, while others are less appropriate. Here’s a few that I’ve come across: 2024-11-26 Related search queries: For contrast, here’s a valid case:

A trip planning tool for generating itinearies based on article url input and content extraction. (Prototype: what2do-51224.web.app)

Insight extraction from popular opinion. Multi-modal ai content understanding for opinion and insight extraction from shortform videos. (crowdlistening.com) Credits: Madison Bratley, Violet Liu

Content understanding, generative agentic workflows, voice ai.

Reflecting on my list of failed projects, very few failed due to lack of innovation. Since I began working with LLMs in fall 2022, there has been an abundance of interesting GenAI technologies to experiment with. It started with “domain specific prompting/finetuning” and data flywheels (thou not even now does anyone know what this looks like in action). By spring 2023, the focus shifted to LLMs as agents, exemplified by the Generative Agents paper, Microsoft AutoGen, and a few opensource projects like MetaGPT. At Cogno, we also built multi-agent systems, integrating various function calling features and agent collaboration for complex task reasoning. Everyone built, few created value (Glean focused on enterprise search, while Moveworks created value through api actions, neither of which I believe agents to have mattered). Founders encouraged each other’s enthusiasm, while investors rushed to learn the latest buzzwords in LLM technology (‘prompt engineering’ and ‘function calls’ sounded less sexy compared to’agents’). ...