Readit News> No clicking. No undo. Just recompile.
> That's our mascot. Entirely CSG.
> No garbage collection pauses. No floating point surprises from a scripting layer.
And worst of all, the dreaded "and/but honestly":
> But honestly, the main reason is the toolchain.
Am I misreading things?
Once CBDCs become a thing, citizens should have the ability to have direct credit relationships with the central bank.
We can then transition from a cash based monetary system to an accrual based one (similar to how businesses do their accounting.)
Public benefits, then, rather than being given out like it is currently (e.g. you get $200 for food stamps) will instead be based on allowing you to draw credit.
So, the eGovCreditCard would for example always allow any citizen to draw $200 per month for food expenses.
Potentially, if we want to do more generous policies a la "UBI," we could add e.g. $1000 always being allowed per month for rent.
Health care similarly, instead of if the archaic and very inefficient system we have now where those on the dole often go to emergency rooms, money is funneled through "insurance", etc... would allow you to draw money for regular doctor care. Maybe at a set maxiumim limit per citizen, e.g. $1M.
I could imagine an LLM inference pipeline where the next token ranking includes its similarity to the target embedding, or perhaps instead the change in direction towards/away from the desired embedding that adding it would introduce.
Put another way, the author gives the example:
> embedding("king") - embedding("man") + embedding("woman") ≈ embedding("queen")
What if you could do that but for whole bodies of text?
I'm imagining being able to do "semantic algebra" with whole paragraphs/articles/books. Instead of just prompting an LLM to "adjust the tone to be more friendly", you could have the core concept of "friendly" (or some more nuanced variant thereof) and "add" it to your existing text, etc.
This is an interesting problem both for public datasets like the ones you're showing, and for internal datasets created and exposed by teams within an org. There are a lot of moving pieces to consider over and above the basics of getting data into and out of systems:
* How do you communicate the data schema in a way that provides both strong guarantees (data you see WILL match the advertised schema), while still being adaptable to change and unexpected circumstances (schemas WILL change)
* How do you deal with transformations/data cleanup in a non-hacky way? Then how do you scale them?
* How do you deal with data ownership? What if one data product consumes another in a nontrivial way -- who owns what?
I'm working on a team building a product to solve these problems! We recently opened beta signups so if you're interested, check us out: https://www.estuary.dev/
I'm happy to answer any questions :)
Let's say I'm the creator of Alacritty, would I have more problems adding libghostty than it's generically named identical counterpart libtermengine?
Just that it's a specific "product"-y sounding name? Would you also be concerned about "libwayland" vs "libcompositor"? Genuinely curious: this seems like an insightful question, I just don't follow the reasoning.