Poor lighting is no laughing matter.

Python code:

results = {}
for ip0 in range(256):
  ip0_str = to_str( ip0 ) + '.'
  for ip1 in range(256):
    ip1_str = ip0_str + to_str( ip1 ) + '.'
    for ip2 in range(256):
      ip2_str = ip1_str + to_str( ip2 ) + '.'
      for ip3 in range(256):
        ip_vector = [ ip0, ip1, ip2, ip3 ]
        ip_str = ip2_str + to_str( ip3 )
        results[ ip_vector ] = ping( ip_str )

Might look a bit nicer using format strings instead. That map will contain on the order of 4 billion entries (one for each value of 2³²), and the actual size will depend on what format the ping function returns, 4 bytes for each key (optimized from my initial version that used the IPs as keys), plus all the internal structures for the map like key hashes and the hash table itself. Ie, this takes more memory to run than viewing OP’s full sized image and I wouldn’t suggest running it with less than 32GB of RAM. Though it would take less memory if it generated the image directly or at least making the keys implicit (which the image does, as they are encoded into the x, y coordinates rather than stored).

Edit: Let’s look at runtime, too, because why not. Assuming every single IP responds in 0.01 seconds (they’ll take longer, especially the ones that time out instead of respond), rounding the total to that nice 4 billion number get us 40 million seconds. An hour is less than 4000 seconds, so it would take over 100 hours to run this script.

Though it could be parallelized, since you can ping many targets at once. Not sure what the maximum number of pings you could have in flight is, but whatever it is, you’d be much better off using a script that did like 80% of that (to leave some margin for the rest of the system to use, also ISPs might not be happy with you maxing out your ICMP traffic).

Or more specifically, what is the sum of all latencies divided by the number of responses times the total numer of requests sent (to scale up for the ones that didn’t reply, assuming their average latency would have been similar to the total average)?

You have too many negatives there.

Should be “I didn’t say she did say they didn’t.”

Though… You also didn’t say that she didn’t say that they didn’t (that was the other commenter) so I guess you’re still accurate.

There was a head rush that felt kinda neat at first. Guessing because nicotine binds to the ACE2 receptors, which play a role in ATP reuse (iirc), ATP being the big energy molecule that powers the current multicellular life, so it’s similar to a bit of oxygen deprivation in that available ATP is reduced.

But then the body just expresses more ACE2 receptors to compensate, so you stop getting the head rushes. This happens fairly quickly, too, like you might get them for like half of your first pack of smokes before the “feels better after a smoke” is more about getting rid of nicotine withdrawal than any positive feelings (if you enjoy the altered feeling of a head rush).

There’s worse things to attention whore about. You could call this setting a good example, too.

The more I think about it, the more I don’t like the mindset that good deeds must be done in secret. People shouldn’t expect anything in return other than appreciation, but I think good deeds do deserve appreciation.

And I’d bet that bit in the bible was actually there to discourage competition and make it look like no one helps each other so the church is more necessary.

As long as people aren’t just making up shit, exaggerating wildly, twisting what happened, or humiliating the people they help.

Oh that’s one of the biggest low severity annoyances: timeouts set to stupid short times. When I push buttons navigating to a certain area, the device can fucking wait for me to be done in that area and tell it to go elsewhere, not assume that 10 seconds of inactivity means I’ve wandered off and forgotten about it or something.

I have a toaster oven like that, though that’s not even the worst part of the UI design. It’s a great oven but clearly the interface was designed by someone who lacked either care or competence because you have to scroll through a bunch of useless presets for if you have some specific portion of chicken or want to burn a slice of pizza, and of course it has no memory, even if you stopped it while trying to pause it to check your food. It would have been better with an analog timer and a mode knob ffs. And if you hesitate too long while setting the mode, time, and temperature, like if you need to grab the box and then do the math to convert to convection toaster oven time and take more than 12 seconds, it’s off by the time you go to enter it.

Oh and I can only guess at what some of the modes are because the icons they use aren’t very descriptive. Is that the warm setting or broil?

It’s a stupid design for a toaster oven but extra fucking dumb for a car.

It won’t be no one but clearly this platform isn’t (yet) a cesspool for it like other platforms have become.

There’s different levels of broken. Like a drawer that doesn’t close all the way still works as a drawer, and while I would call a drawer with its bottom busted out more broken than one that just won’t close, both could use some repairs.

How about something other than Microsoft’s computers?

Yeah, personally, I’ve noticed that I notice and appreciate very high quality streams when they are there but don’t notice lower quality ones in a bad way (where “lower quality” is still like 1080p, 720p is more noticeable).

Like 4k looks great but 1080p still looks normal.

What an entitled and dumb twat. I guess he didn’t think it through that the guy maintaining it now doesn’t have some magical ability to tell who would be a good new owner and “handing over the mainline” to any one person could screw people more than any AI changes because that new person might be malicious or neglectful.

Forks need to earn trust and it’s best that that step isn’t skipped by some inheritance. Not that there even is any obligation to hand it off no matter how many people rely on it or complain.

And the comparison with ms or Google was dumb because I didn’t expect them to do it the way I wanted and stopped using what I could to get away from where they were going and any expectation that they hand over their projects to someone else would be ridiculous (and Google open source projects have been forked).

Have you mentioned yet that anyone can do that second one by making their own fork? And that no one is required to give a fuck about how they think it should be done?

Or just do it like reddit did, where you can delete your post content and remove your username from it, but the thread and comments remain.

Though with how the fediverse works, it’s possible to spin up a custom instance that highlights deleted content instead of deleting it, meaning the attempt to get rid of it can be what brings it more attention if anyone has decided to do it. Just like with vote identities, they aren’t anonymous and there are instances/sites that just show who voted for what.

I’m still undecided about their own text (deleting comments or post text disrupts the conversation but there’s valid reasons), but I don’t think the OP of a thread should have any control over the existence of that full comment thread.

Yeah, while I can agree that plastic straws weren’t the worst offender for gestures around, I still think reducing that plastic use was a good thing and have noticed it takes more than hours for a paper straw to lose structure, though even then it doesn’t stop being a straw unless you bend it.

Though I think the claims that they are also filled with plastics are plausible. I use metal straws at home but maybe it’s time to start carrying some in the car for use with takeout.

Frankly, I don’t think users should have that power.

Any specific issues though? Yeah, it’s a large change and I’d be more surprised if it didn’t have issues, but are there any specific issues with the updates that have been found so far?

Yeah, reinventing the wheel can be fun.

You can train it on all the source code, meta data for that source code, and documentation you want but it will never understand programming. It’s a text predictor that was trained on both sides of a bunch of debates. Contradictions mean nothing to it, but it usually only predicts what one side of the debate will say to champion its side, which means it will use confident and absolute language to “sell” whatever side of the debate it looks like the previous tokens are headed towards.

It is impressive what it can output sometimes and it makes a decent debate/exploration partner, but it will always have a chance at predicting a useless series of tokens or contradicting the previous thing it just said because a) its training data only trains it to predict tokens from statistics, and b) its training data includes some of those contradictions directly.

I have lost count of the times I’ve been “thinking out loud” about something with an LLM and realize something about what I’m thinking about that contradicts what it is currently saying, then I’ll add my new perspective and it agrees entirely, despite the contradiction. Sometimes it tries to resolve the contradiction, sometimes it just abandons what it said previously entirely, sometimes it adds more to the perspective that I hadn’t considered.

That’s fine for just shooting the shit about some random topic but horrible for a tool intended to provide expertise and reliability, when the response matters because it feeds into something else and you want to automate it. Should a tool just inject “are you sure?” after each response? What if it makes it second guess something that was correct? What if it’s one of those debates and it will endlessly switch sides when it faces any opposition? That’s a waste of resources and time.

Funny thing is I’m expecting this to eventually go back to scripting for automation. An LLM has a higher chance of outputting a script that does what you want (depending on the task) while you hold its hand than it does of consistently giving the correct output when it is thrown into an automated system directly. But you get “goodish” results much quicker just trying putting the LLMs everywhere, even if there’s some selection bias on the results (“didn’t work, didn’t work, oh it worked, great!”).