Aside from it being "instructions for agents", I'm not sure I understand how this isn't just a markdown file that more or less reads like a readme that targets more junior engineers.
I am curious about how this compares to dataview. As a dataview user, I'm not immediately seeing something bases does that dataview doesn't, but I am not a power user.
Dataview can be used for queries that output tables, but its strength is letting you write essentially custom imperative Javascript code that renders stuff in notes dynamically (dataviewjs mode). Whereas "queries to tables" is more or less what Bases does, the dataviewjs mode will probably always be unique.
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I'll add to the conversation another interesting technique from Chris Voss, which is to use no-oriented questions.
People like to say no. (I'm not sure what this cognitive bias is, but anecdotally I agree.)
So, if you can frame your requests in a way that "no is permission", it will often get a red light a bit easier.
Example: replace "Is this a good idea?" with "is this a bad idea?"
Now, of course "not a bad idea" is not the same thing as "good idea", but it's a lot more likely. Even reading that, I imagine most people would respond more intuitively, because it helps us avoid a commitment we don't necessarily want to adopt.
I think there is some positive effect potential for Apple to let this slide. The broader this network is, the more adoption it receives. P2P as a super-structure has always been a bigger than vendor problem; adoption by any means is likely an allowable tradeoff, especially since Apple doesn't have to do the work here.
Eventually they will capitalize more on the mesh density, rather than crushing the adoption now.
Except that custom tags like these do not require an Apple device in order to use them, so the size of the network is not increased. They only increase the load on the network. FindMy is not a P2P/mesh network; all these tags do is broadcast keys which are picked up by iDevices, which then upload those reports to Apple.
Two master secrets are randomly generated when pairing the AirTag for the first time, which are then saved to the iCloud keychain. Those secrets are then used to generate a new keypair every 15 minutes (at most), and the public key is broadcasted by the tag. Not only does Apple not know what the master secrets are in the first place (because they're stored in the keychain), but that's also an insane number of keys to compare against, with no real possibility to precompute them. And that's a big win in terms of privacy.
I suspect that, given a reasonable prompt, it would absolutely discard certain phrases or concepts for others. I think it may find it difficult to cross check and synthesize, but "term families" are sort of a core idea of using multi-dimensional embedding. Related terms have low square distances in embeddings. I'm not super well versed on LLMs but I do believe this would be represented in the models.
