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open.substack.com/pub/frontend...
I think I might have found one of the many paths to happiness.
The generalist’s role is to help translate and connect these deep insights across disciplines.
www.figma.com/blog/the-ris...
The Rise of the Generalist
1. Complex problems rarely fit into single discipline.
2. As AI continues to excel at specialized tasks within defined boundaries, human value increasingly comes from connecting multiple domains of expertise
3. There always be need for deep expertise.
The investment that truly guarantees the highest returns:
Fake: U.S. stocks
Fact: Investing in yourself.
Fewer decisions mean faster development
supabase
vs
neondatabase+DrizzleORM
Since when did “launch weeks” become so popular? Isn’t a launch supposed to be just one day? 🤣
In the past, there were already qualitative metrics, like whether the ecosystem was robust enough. Is “AI-friendly” simply a rephrasing of that same concept?
I’m not sure if having enough examples can solve this problem. It makes me wonder if “AI-friendly” should become a new criterion when choosing a library.
These days, new frameworks and libraries come equipped with well-designed developer experiences, including documentation and CLI tools. But now, do they also need to optimize for AI learning? Even with technologies like RAG or AI agents that can browse the web, there seem to be clear limitations.
On the other hand, with widely adopted libraries like Tailwind, there was no need to consult the docs at all. This made me wonder if it’s becoming even harder for new libraries to break into the existing market. No matter how great the interface is, reading documentation can be a hassle.
One of the things I’ve noticed while using TanStack Form is how important being AI-friendly has become. Since it’s not as widely used as RHF, it’s harder to get help from AI agents, and I had to rely on the documentation.
