Man-made horrors beyond my comprehension, neat.

I have no mouth but I must pad.

Now that's a rare GitHub Username!

Though, again, a yet another project for a yet another "AI" to make someone else more dependent on it...

Related:

    https://en.wikipedia.org/wiki/Jev_(AI_model)  
    https://typesafe.ai/

Can someone ELI5?

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.

why useless? it did one thing that js didn't natively do

It did one thing that you could write in one line of code. But it wasted space as a packaged dependency instead.

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.

why

funny

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 thought you were doing a PR to add an 11th space.

Nah, for ≥11 spaces we should fan out to a GPT-6 Astra agent. On light reasoning of course, lest we be wasteful.

im so confused about what i should bring up at the 10am today

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.

its confidence scores

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 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

Man we're going full 2015 ML, telling normies that confidence scores mean NOTHING to alleviate false negatives/positives.

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!

But is it Web Scale.

what if 11 spaces are needed

Would be fun to have a test runner that uses Jev for assertions.

Btw, this model also has very tiny inherent bias: https://jev-bias-analysis.stupidlabs.lol/

Flaky tests by design is a diabolical idea

    The tests mock Jev. 

10/10 no notes

How did this guy get a single-letter GitHub username?

His oldest repository was updated in 2011. Looks like he's been there a long time.

Jev mania at its peak? HN is full of Jev like today.

I already have Jev fatigue.

Can't believe it's already been 10 years...

https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/

What an eerie feeling reading this, or was it prescient?:

"""

    def fizz_buzz_encode(i):
        if   i % 15 == 0: return np.array([0, 0, 0, 1])
        elif i % 5  == 0: return np.array([0, 0, 1, 0])
        elif i % 3  == 0: return np.array([0, 1, 0, 0])
        else:             return np.array([1, 0, 0, 0])

interviewer: OK, that's probably enough.

me: That's enough setup, you're exactly right. [<--- !!] [...]

"""

Damn, Claude was there all along

This is funny, because I just implemented the same feature, but mine is calling OpenAI’s Astra on High (very capable for this kind of feature).

It works great, but maybe your implementation could save me some money. I’ll test it and report back.

I can't tell if this comment is satire or not lol

poor implementation

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

I think some people are missing the joke here

https://en.wikipedia.org/wiki/Npm_left-pad_incident

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.

It's fun so far seeing the new trend from the sidelines, never bothered and never will bother learning what JEV is, but I'll see the occasional meme.

I feel for the people whose 'AI strategy' is keeping up with every vibecoded vibecoding junk that releases

I'm very curious about the performance. Where's the quantitative analysis? Got any graphs or charts? How's the recall/precision? Show me the data!

This comes just in time. My app has been using Amazon Mechanical Turk for left-pad queries, and that service is shutting down in less than two weeks.