714 karma · joined September 2, 2022
We have a TON of ebikes i Norway and the cops regularly do checkpoints of suspected chippers in the main commute routes.
They have a little threadmill thing similar to what chiptuners use when tuning car engines that measure torque, watt and top speed.
That said, I agree that is pretty stupid.
The only connection to marginal tax rates is that the pay bands line up though?
It is funny you say these are well known and documented, yet provide no links or sources.
Its the parents job to curate access to the inernet. I say that as a father of three.
If no? Then congrats, you are now in a position where your software development lifecycle needs to handle non-determinism.
This fanatical vibing movement is ridicolous, but this luddite stance that LLMs cannot contribute to software dev because they are «non deterministic» is almost as ludicrus.
First of all, its not "macro based", as that implies dark magic and sacrificed goats. The spark dsl underlying all this is just structs all the way down in a nested manner. Just like you would see if you look under the covers of a Absinthe Blueprint produced by that dsl.
The dsl is declarative and allows to express a lot of stuff with less code, but I would say that saying its "macro based" is a bit misleading, although "technically correct". You could achieve the same by just having functions returning structs.
I have replaced a biiiiig nestjs app that exposed graphql with an ash app exposing graphql, and the boilerplate ratio for resolvers etc is bordering on 1:999. Like literally, across a 90 table large application I have maybe 600 lines of "specifically graphql related code" (5-10 lines of code to expose select actions as mutations and queries per resource). As opposed to the nestjs codebase that was using an annotation driven approach and had a gazillion lines of glue code for resolvers and data loading.
Also the authorization logic through the policies is so extremely composable and easy to do when combined with matching on resources it is fantastic. Each resource "owns" its own authorization, so there is no song and dance about figuring out acl from the entry point and then downwards a tree. You just let the resolver resolve its way down the graphql tree or just feed a long ass loader path into Ash.load and each resource is responsible to implement its own policies and you don't have to worry about accidentally leaking data because you access the data from a new entry path that was not locked down because you added a new resolver.
I kept reimplementing the same boring boiler plate every damn time I started a new project and that pain is almost 100% gone.
It is a harsh learning curve for sure, because the one downside of Ash is that you have to do it the "ash way" for stuff to compose as beautifully as it does. Once you really get into the groove making "expression calculations" (basically projections that reach into other resources or columns to make some kind of computed data, but is done in the database layer since you expose it as an expression) that you can compose and make depend on eachother etc it becomes so incredibly fast to make new functionality.
You think about one and one thing and let the framework take care of how to compose the loading and usage of what you make. A much simpler model than "making it yourself" in ecto which I have been doing for 10 years prior.
Albeit that is possible in typescript these days too tho.
With regards from a hardcore european leftist
The Ukrainians certainly does not want the war to end this way.
I drove 3600 km Norway to Spain in 2018 with only adaptive cruise. Then again in 2023 with autonomous highway driving (the kind where you keep a hand on the wheel for failure mode) and it was amaaaazing how big the difference was.
If they don’t sacrifice their life for their job they get deported after all.
It is funny that this article is what we ended up from that beginning tho.
LLMs seems to be ok'ish at solving trivial boilerplate stuff. 20 attempts deep I have not yet seen it able to even remotely solve anything I have been stuck enough on to have to sit down and think hard.
Bandit state.
Coupled with the fact that, as you yourself pointed out, there is a literal endless amount of work to do, forever. This is also due to the nature of the company being so big.
All companies always has work to do, and no one is ever «done», but in a giga-enterprise all meaningful deadlines and deliveries sort of tangentially rounds down to zero in terms of impact.
I almost burned out from this myself working in Microsoft. I was succeeding in my work by most metrics, but I am motivated more by my work being MEANINGFUL and having impact more than anything. That is almost impossible to achieve in any large enough company.
Jumped off to be a startup CTO and life started smiling again instantly.
Take time away from work, but not too much time. Comments such as «it takes years» can be true if you have ground yourself down to a nub, but trying (and being ok with failing) to do some work that lets you feel like you mean something and contribute back to society is an understated and important part of the healing process.
Good luck!
I have not done any kind of research into if the Wuhan lableak story is true or not, but the admitted supression by the govt has made sure I cannot trust any kind of conclusion around it.
So then I ask: What, in qualitative terms, makes you think AI in the current form will be capable of this in 5 or 10 years? Other than seeing the middle of what seems to be an S-curve and going «ooooh shiny exponential!»