Recent submissions

Comment · 22 days ago
And finally, we need those clip-on Game Boy lights again.
Comment · 28 days ago
On why we shouldn't:

- bacterial populations are being regulated by bacteriophages (viruses) or predators. They have spent millions of years evolving to hunt standard chiral life and wouldn't be physically able to digest or hunt mirror bacteria.

- the same is true of our immune system. Our pattern-recognition receptors only work for non-mirror molecules and wouldn't recognize a reversed molecular fingerprint. You may be able to engineer antibiotics and phages, but a vaccine would require a creative approach.

- the claim "yes but mirror bacteria would starve since they wouldn't find mirror food to eat in nature or in our bodies" doesn't hold up, because there are enough achiral (non-handed) nutrients in the soil, our blood and our gut that mirror bacteria could eat.

Comment · 1 month ago
Here is an example where you can see it in action:

https://navignine.com/c/g4PDSWbLgy2HPLROeDfz#key=1f84bcb51d7...

Or just start a new chat and try it out for yourself.

This is a link to the mentioned research tool, but it's not used commercially anymore. As long as the rate limits allow it, you can play around with it a bit as a demo without a login.

Comment · 1 month ago
I recently measured something similar on whether a quote supports the claim it's cited for. I tested on 240 examples from ALCE (labeled by human annotators).

Jev 1.13 flagged 87.5% of the citations that the annotators marked as not supported. The best regular LLM judge I tried was GLM-5.3-flash with reasoning which flagged 83.9% (without reasoning it was 75.6%).

The same also works with open models though. Give Qwen3.5-397B the three possible answers, let it output a single token, and read off the probability of each answer. No reasoning, one token per check. That flagged 89.3%.

There were only 56 unsupported cases, so these numbers are within each other's error bars. But my conclusion was that the speed and the confidence score come from reading out a single token, not from Jev itself.

Comment · 1 month ago
Thanks :) I'm also building a tool for spoonerisms that might be useful for rhyming: https://wordlab.rickinto.place/spoonerize

It uses the same data as the misheard tool and produces cross-pairs like these: Be known Knee bone

No guts Go nuts

And there is a rude word filter for SFW that can also be inverted for extra "edginess"

Comment · 1 month ago
Comment · 1 month ago
That's actually fun to watch. Did you experiment with nicknaming before you turned it off? I'd be a little curious to see how it behaves.
Comment · 1 month ago
Looks great :) It would be nice to be able to manually tweak the plotting algorithm or some underlying metrics.

Live from the official Hacker News API

A modern reader for Hacker News · SvelteKit + Skeleton