AFAIK bridging deductive and inductive AI has been understood as the trick for "AGI" for a long time, probably even before it was called AGI.

I really want this winter of deductive AI to be short. We need both sides and can't afford a long winter like the one inductive AI suffered.

Cost-wise the only viable private compute is local compute. It's more expensive than cloud, but true private compute in the cloud is definitely pricier.

I moved from high end to a "mid-range" Samsung phone that's way better than it needs to be given how boring phones have become. The only upgrade I wish on it was more open source software.

I'd be interesting to dive into bounds and good properties for sets of landmarks.

I imagine that if, - Every node is at least X cost/distance away from a landmark - Landmarks are no closer than Y cost/distance from each other

You can start promising a lot about the size of your open set on any execution.

A* on h* (perfect heuristic) takes O(l) where l is the length of the solution (could expand exactly l nodes, but solving/guessing ties incorrectly might bump this to a multiple around the avg edges per vertex). I imagine that having good bounds mean you'll take no longer than a certain amount of expansions/depth before you lock-into the railway that h* provides (and you need some extra work to get off it too).

It's the 2nd time I hear this argument and I'm already fed up with it

Is it the preferred way only because training is expensive? It's like saying binaries are the preferred way of modifying program because you can't afford to have a fast enough machine to compile it yourself.

Most people don't have resources to compile their own browser, but what makes some browsers open is access to the source.

Maybe it's the preferred way because the people sharing models are themselves working with weights and no data?

Do you want user-level services, or a multi-tenant system-level service? - Who can tweak the service configuration? - How do you ensure there's no data leaks between users? - What about misbehaviour? Quotas, crashes

I think lingering is great here

Sound like you don't like computers to begin with

I'm really not fond of having to `git rebase --interactive` just to drop all commits that are "already in master", especially when I forget this silly step and get a borked rebase.

- Keeps messages O(1) so I can focus on scaling in the amount of notifications

- Tells whoever runs into this that,

    - I didn't planned for arbitrarily large messages as they *might* otherwise grind performance to a halt.
    - They *might* be misusing my notification system