Do you know what's completely missing from all of these products like anything LLM and Onyx...
a mobile application that has parity on the same features that ChatGPT and Claude does...
Readit NewsTypo fix: ynab.com
Fixed thanks!
For those interested in this type of single entry accounting (and by extension double entry)
Here are some other ones I've tried and used in the past:
I wonder if this type of work can be applied towards translating kernels between GPU vendors, e.g. CUDA → AMD. Does anyone know if that's possible or whether that kind of problem is AGI-complete?
There's a higher level of abstraction
Chris Latner of Apple's Swift and Tesla fame is running a company entirely predicated on this, but at the deterministic language design level rather than the inference level.
If a beam search, initiative plan and execute phase is more effective than having better tooling in a deterministic programming language then this will clearly take the lead.
Calling beam search 'AI' is doing a lot of heavy lifting here. This is just superoptimization with a very expensive heuristic function.
That's correct - however as other commenters have noted. Doing this by hand is extremely challenging for human engineers working on tensor kernels.
The expense calculation might be
expense of improvement = (time taken per optimization step * cost of unit time ) / ( speedup - 1)
The expensive heuristic function is saving wall time well also being cheaper in cost of unit time. And as the paper shows the speed up provided for each unit time multiplied by unit cost of time is large.
I think there's a very important nugget here unrelated to agents: Kagi as a search engine is a higher signal source
of information than Google page rank and ad sense funded model. Primarily because google as it is today includes a massive amount of noise and suffered from blowback/cross-contamination as more LLM generated content pollute information truth.
> We found many, many examples of benchmark tasks where the same model using Kagi Search as a backend outperformed other search engines, simply because Kagi Search either returned the relevant Wikipedia page higher, or because the other results were not polluting the model’s context window with more irrelevant data.
> This benchmark unwittingly showed us that Kagi Search is a better backend for LLM-based search than Google/Bing because we filter out the noise that confuses other models.
Why would lefthook not be a more reliable tool (in design)
The ability to rapidly shard everything can be extremely valuable. The difference between "we can shard on a dime" and "sharding will take a bunch of careful work" can be expensive If the company has poor margins, this can be the difference between "can scale easily" and "we're not getting this investment".
I would argue that if your folks have the technical chops to be able to shard while avoiding surrogate guaranteed unique keys, great. But if they don't.... a UUID on every table can be a massive get-out-of-jail free card and for many companies this is much, much important than some minor space and time optimizations on the DB.
Worth thinking about.
Definitely an issue, rarely the main one. You can work around integer PKs with composite keys or offset-based sharding schemes. What you can't easily fix is a schema with cross-tenant foreign keys, shared lookup tables, or a tenancy model that wasn't designed for data isolation from day one. Those are architectural decisions that require months of migration work.
UUIDs buy you flexibility, sure. But if your data model assumes everything lives in one database, the PK type is a sub-probem of your problems.