I have to be honest. While this is obviously a smart and useful idea, it misses one of the core features of Jev: its confidence scores. Partial confidence could easily be mapped to fractional spaces, using unicode characters like U+2009: THIN SPACE. As it stands, this package is not harnessing the full power of Jev.
important to note that the "confidence" score is... maybe not what people think it is - kind of useless, and just a convenience step from the probabilities.
from the docs: "confidence is a statistic computed from the probability distribution the answer already gives you." [0] I actually encourage people to visit the docs because it has a specific page on this with a little applet to really make this clear.
What else do people think it is? If Typesafe had found a way to measure arbitrary AI results against objective reality (past, future and present) they'd either be making a killing on the stock market or working for the NRO, not publishing that as a confidence value on their API
Yeah I keep getting this weird sense that Jev is kinda poorly reinventing ML. I guess the graphs don't lie and theoretically I can replace luna with it, but I don't really use luna anyway.
What is the use case for a classifier that works 90% of the time...? I feel like if I'm classifying something, I probably care enough that 90% ain't gonna cut it...
I guess the answer is just agential stuff that effectively gets double checked by the LLM in the driver seat, anyway? That tracks, though it means that jev is mostly just for the people making harnesses. Which is all of us but still!
I think the argument would be that the classifiers of classic ML can be very useful and that Jav is a geenral purpose classifier you can just use that doesn't need to be trained per-task.
I mean when you get your bloodwork done to check for an illness, the test you get will give the right result 90% of the time - and depending on the result, you doc might order more tests, which could be more expensive but no mrpe reliable than the first - but they are going to be statistically independent, and after 2 more, he can be 99.9% sure.
Which begs the question, can Jev retest until it gets the right result? Can it tell how corellated two of its results are? 90% correct makes for a wonderful iterator, but a poor oracle.
I should have know that beforehand, I have terraform ready for quickly moving off of mechanical turk into agentic AI left padding, this load-bearing workload has to be clowd powered
Instead of using Choice with criteria "space_0", "space_1"..., it could be even more elegant to use criteria names like "", " "... which could be directly inserted into response.
People who were in high school in 2016 and therefore not being aware of leftpad are currently in their 6th year of their career if they went directly to college and then a job.
Tech doesn't teach it's own history in a useful way, so we keep repeating it too.
> This means the package can add between 0 and 10 spaces. If more than 10 spaces are needed, Jev has no correct option. Which feels appropriate for this project.
I'll raise a PR which uses Jev to check if the target length is beyond this range
i maintained a system where for .. reasons (like other systems) from early days have users with id: null "null" "" and some i do not even know how to write here
It's funny, i actually worked with this guy (same small startup, not closely), and recognized him from that unique username on this post. It's been so long i wouldn't have had any idea if not for the oddness of the single letter username.
> Please don't use this in production. Or anything important.
5 to 10 years from now, after this has worked itself deep into the npm dependency chain, we'll be lamenting how Jev-Leftpad is causing outages in critical services.
Jev is a new type of model that just makes decisions based on given options. It's small and really really fast.
Leftpad is a npm package that chooses if it should or shouldn't pad the left side of a string. It was famous for bringing down everyone's npm installs a few years ago.
This combination is a double joke. Put something stupid in something stupid.
Uses JEV to do something that's one line of code. Also, "leftpad" was a useless package from years ago that many important packages used instead of writing the code themselves. Its outage at some point broke a lot of packages.
important to note that the "confidence" score is... maybe not what people think it is - kind of useless, and just a convenience step from the probabilities.
from the docs: "confidence is a statistic computed from the probability distribution the answer already gives you." [0] I actually encourage people to visit the docs because it has a specific page on this with a little applet to really make this clear.
[0] https://docs.typesafe.ai/confidence
What is the use case for a classifier that works 90% of the time...? I feel like if I'm classifying something, I probably care enough that 90% ain't gonna cut it...
I guess the answer is just agential stuff that effectively gets double checked by the LLM in the driver seat, anyway? That tracks, though it means that jev is mostly just for the people making harnesses. Which is all of us but still!
Which begs the question, can Jev retest until it gets the right result? Can it tell how corellated two of its results are? 90% correct makes for a wonderful iterator, but a poor oracle.
https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/
"""
interviewer: OK, that's probably enough.me: That's enough setup, you're exactly right. [<--- !!] [...]
"""
Damn, Claude was there all along
It works great, but maybe your implementation could save me some money. I’ll test it and report back.
https://en.wikipedia.org/wiki/Npm_left-pad_incident
Tech doesn't teach it's own history in a useful way, so we keep repeating it too.
I'll raise a PR which uses Jev to check if the target length is beyond this range
You pad the text.
Oh, my god.
so probably early bird
jev should only have the choice of space_0 or space_1, then recurse on n-1
this extends the implementation to infinite padding and is cleaner code
Would have been funnier if it used the GLWTPL: https://spdx.org/licenses/GLWTPL.html
Btw, this model also has very tiny inherent bias: https://jev-bias-analysis.stupidlabs.lol/
5 to 10 years from now, after this has worked itself deep into the npm dependency chain, we'll be lamenting how Jev-Leftpad is causing outages in critical services.
Leftpad is a npm package that chooses if it should or shouldn't pad the left side of a string. It was famous for bringing down everyone's npm installs a few years ago.
This combination is a double joke. Put something stupid in something stupid.
And yet we learned nothing from it and all the same problems keep popping up, only this time in the form of supply chain attacks.
I feel for the people whose 'AI strategy' is keeping up with every vibecoded vibecoding junk that releases
I use it mainly to check whether this code fits the rules I defined, just a yes or no. But I'm not sure if that's the right way to use it.
Though, again, a yet another project for a yet another "AI" to make someone else more dependent on it...
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