Shepherding Electric Minds: My AI Dream Stack
When I started building my first AI-powered product in 2022 (back in the days of GPT-3, non-turbo), it felt like a fresh start—a universe of possibilities for how to build products. Now, two years into building generative AI applications, I'm seeing ideas converge into common patterns across most projects. It reminds me of my early web development days, when the LAMP stack (Linux, Apache, MySQL, PHP) became the go-to setup for launching projects quickly. In my role as a core engineer at PSL, responsible for rapidly spinning up proof-of-concepts and evolving them into launched products, I've been craving an equivalent AI stack—something to streamline the process and avoid starting from scratch every time.
This post isn't quite a repeatable tech stack yet—I'm not ready to settle on a single architecture while AI technology is evolving so rapidly. Instead, it's a dive into what my dream AI stack could be, combining the best patterns and components I've encountered or helped create with my PSL colleagues over the past few years.
The journey to this dream stack has been shaped by five distinct AI startups I've worked on. Each of these projects built on the concepts from the last, adding new insights and pushing boundaries:
