Yeah, that's a fair point at first glance. 50GB might not sound like a huge burden for a modern SSD.
However, the 50GB figure was just a starting point for emails. A true "local Jarvis," would need to index everything: all your code repositories, documents, notes, and chat histories. That raw data can easily be hundreds of gigabytes.
For a 200GB text corpus, a traditional vector index can swell to >500GB. At that point, it's no longer a "meager" requirement. It becomes a heavy "tax" on your primary drive, which is often non-upgradable on modern laptops.
The goal for practical local AI shouldn't just be that it's possible, but that it's also lightweight and sustainable. That's the problem we focused on: making a comprehensive local knowledge base feasible without forcing users to dedicate half their SSD to a single index.
You already need very high end hardware to run useful local LLMs, I don't know if a 200gb vector database will be the dealbreaker in that scenario. But I wonder how small you could get it with compression and quantization on top
I know I’m just a single data point but I’ve had a lot of success migrating old node projects to bun (in fact I haven’t used node itself since Bun was made public)
Again, I might be saying something terribly stupid because JS/TS isn’t really my turf so please let me know if I’m missing something.
I'm waiting patiently for bun to catch up because I would love to switch but I don't think its ready for production use in larger projects yet. Even when things work, a lot of the bun-specific functionality sounds nice at first but feels like an afterthought in practice, and the documentation is far from the quality of node.js