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.

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

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

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.

    The tests mock Jev. 

I think some people are missing the joke here

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

This needs an SBOM and a SonarQube Qualitygate pass to be considered production grade code

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.

Man-made horrors beyond my comprehension, neat.

> 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

Make sure to vendor this package if you want reliable operation.

    it('trusts Jev when it chooses the wrong amount', async () => {
    answer('space_2');
    await expect(leftPad('jev', 8)).resolves.toBe('  jev');
    });

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!

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

Next: jev-is-even

> MIT Licensed

Would have been funnier if it used the GLWTPL: https://spdx.org/licenses/GLWTPL.html

Is there a local model that can run this?

On the npm doomsday thing, the scarier version is someone vendoring it and the padding silently drifting between runs.

But is it Web Scale.

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/

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 already have Jev fatigue.

I’d be curious to see how reliable it is in practice, it sounds like a cheap and (hopefully) easy benchmark

Is this package vibe coded with Jev or just old school with an LLM?

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

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

Jev-in-the-Loop. Makes sense.

Can someone ELI5?

This makes about as much sense to me as AI-powered air traffic control

what if 11 spaces are needed

> 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 seems similar to BERT. Where would it be useful?

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.

Terrorist

why

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

github.com/f is more impressive to me than jev-leftpad lol

wait.. is it just me or are we going overboard? I am waiting for jev assistance to exit vim, no wait.. a decision if we should exit vim or not.

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/