I have a first responder friend who helped a guy who wanted to have sex with his cousin, but she wouldn’t unless he got a circumcision, so he chopped his dick off. I personally think he was not using it the way it was meant to be used at two distinct levels:
- you shouldn’t have sex with relatives
- you shouldn’t chop your dick off
The emergency room doctors sewed it back on. Not sure what to think about that.
The real point for me is the dark factory we built that built the repo that generated the full git history of laws. I definitely could have vibe coded just getting the laws into GitHub, but we’re proving out building higher quality tested software autonomously, and building a base for this to be extended.
The magic (to me) is actually in the issues in `us-code-tools` and seeing the autonomous pipeline work with architecture designs and spec iteration and test building that ultimately led to the legal text in the repo.
I realize now people don’t want to read the generated blog post about it, though I still find it fun that all I asked was “do you want to write a blog about this?”
If you have something interesting to say then use your own words. The reason why your robot wrote a blog post or whatever is not insightful or meaningful.
Oleophobic coating is standard on phones and tablets, which is part of why they don’t pick up fingerprints as easily.
Some brands offer coating you can DIY yourself (eg ProofTech OLEOPEL) but these seem mostly designed for phone screens. I don’t know whether they’d be as effective on laptop screens
Similarly, it's possible to take the derivative of a song. You can use a Fourier transform to express the song's waveform as a series of sin and cosine functions, then take the derivative.
Imagine, for the sake of simplicity, you could express the song's waveform with the function 13 * sin(41x).
The derivative of this function is 533 * cos(41x).
Cosine, of course, is just a phase shifted sine, and the constant coefficient inside the function stays the same. So you're not changing anything about the shape of the wave, just stretching it vertically.
This has the effect of mimicking a "high pass filter," amplifying the volume of the highs.
Well, you get the frequency domain derivative. This is the same as scaling the time domain by a linear ramp. Not exactly hugely useful, unless you happen to be in radar.
You can take the finite difference with eg np.diff(waveform) though.
Google and lots of other firms use a "leap smear" to hide the leap second from end users, essentially "smearing" the second across the hours before and after each leap.
I guess it shouldn’t be surprising for this post to be LLM-written when the author’s point is that they use LLMs to write a bunch of social media posts, but it still makes me a little sad.
I get why it reads that way, but this post was written by me. I actually spent more time on this Show HN than on most client deliverables this week. The irony isn't lost on me though — when you work with LLMs all day, your own writing starts picking up the patterns.
FWIW, I'm a solo dev in Taiwan trying to make AI tools more accessible here. Mobile penetration is nearly universal but AI adoption is still very early. I'm learning as I build.
We're not meant to do anything. It's not like we were consciously designed with a purpose in mind. Do whatever you want.
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