CodeInvasion

The shooter was a CIA agent

I remember how the meaning of words began to change. How unfamiliar words like "collateral" and "rendition" became frightening, while things like Norsefire and the Articles of Allegiance became powerful. I remember how "different" became dangerous. I still don't understand it, why they hate us so much.

I remember how the meaning of words began to change. How unfamiliar words like "collateral" and "rendition" became frightening, while things like Norsefire and the Articles of Allegiance became powerful. I remember how "different" became dangerous. I still don't understand it, why they hate us so much.

You can only win a gun fight if you have superior numbers and firepower. Otherwise it's just certain death for the assaulted.

We need to bring back the concept of a citizen militia.

All of my analysis comes from the US Bureau of Transportation Statistics, particularly information published in 2024. You are right--It is very, very difficult to normalize the data across different modes, however my analysis is specifically over passenger-miles, but I also did it over passenger-hour exposure, which is significantly worse because you can cover more distance in a shorter amount of time.

I wrote a section on the subject in a paper that is currently under academic review at CHI, but I ultimately cut the section. I should write it into a blog at some point.

Risk assessment is tricky business, and I've spent countless hours in discussion with colleagues over the topic. Humans, even highly attuned academics, are inherently terrible at assessing risk for low-frequency events. While we like to say things like one is 3x more likely than the other, it often lacks the context of scale. A lack of context often results in overly cautious recommendations that encourage people to live like a bubble-boy. I've advocated in the past that all academic journals should adopt a common risk metric, like the micromort when reporting on risk.

There are 800,000 pilots in the US, and an average of 300 deaths per year or 3.75 per 10,000 pilots. There are 243 million drivers in the US and 40,000 deaths per year, or 1.6 per 10,000 drivers. While one is higher than the other, they are still incredibly small frequency events, and our ape brains are not capable of adequately reasoning over that concept.

Riding a motorcycle or flying GA is only 3x more lethal than driving and just as lethal as walking (75% of walking fatalities occur at night and are entirely due to cars).

The big difference is on a motorcycle, the danger is other drivers, whereas for a pilot, the danger comes from themselves as 90% of fatal accidents are caused by pilot error.

Eliminating pilot error is entirely possible as demonstrated by the commercial airline industry, which ends of being the safest form of travel by multiple magnitudes over cars. If GA pilots can hold themselves to the same rigorous standards as commercial airline transport pilots, then GA can absolutely be much safer than driving.

X is from when airport codes were two letters.

The ICAO designator for all airports in the contiguous US starts with K, so it's full designator is KLAX. Same for KPDX.

There are over 5,000 public use airports in the US. While not all of them start with K (some very small airports start with a number instead), all moderately sized or larger airports do.

The tokenizer is capable of decoding spaceless tokens into compound words following a set of rules referred to as a grammar in Natural Language Processing (NLP). I do LLM research and have spent an uncomfortable amount of time staring at the encoded outputs of most tokenizers when debugging. Normally spaces are not included.

There is of course a token for spaces in special circumstances, but I don't know exactly how each tokenizer implements those spaces. So it does make sense that some models would be capable of the behavior you find in your tests, but that appears to be an emergent behavior, which is very interesting to see it work successfully.

I intended for my original comment to convey the idea that it's not surprising that LLMs might fail at following the instructions to include spaces since it normally doesn't see spaces except in special circumstances. Similar to how it's unsurprising that LLMs are bad at numerical operations because of how the use Markov Chain probability to each next token, one at a time.

This is because spaces typically are encoded by model tokenizers.

In many cases it would be redundant to show spaces, so tokenizers collapse them down to no spaces at all. Instead the model reads tokens as if the spaces never existed.

For example it might output: thequickbrownfoxjumpsoverthelazydog

Except it would actually be a list of numbers like: [1, 256, 6273, 7836, 1922, 2244, 3245, 256, 6734, 1176, 2]

Then the tokenizer decodes this and adds the spaces because they are assumed to be there. The tokenizer has no knowledge of your request, and the model output typically does not include spaces, hence your output sentence will not have double spaces.

Thanks, it's definitely a good escape from the code, that's for damn sure.

Old cars are work for sure, but if you are willing to learn it's not bad.

I have a 2007 Mustang. I've replaced the entire front suspension, rear differential, alternator, and paid an upholsterer to replace the convertible top. I upgraded the radio and put in a 10inch touch screen with Wireless carplay and integrated backup camera. Next up is dropping the trans to replace the clutch plate, throw-out bearing, resurface the flywheel, and replace the rear main seal on the engine while I'm down there because the flywheel is rusty and accumulates a thin layer of rust every morning that makes a grinding noise for 30 seconds until it grinds off.

It definitely doesn't just work like a new car, but since I do the work myself it also doesn't cost me much.

Nowhere in the bill text does it say that. Please cite your sources. The bill is less than a five minute read. https://legiscan.com/CA/text/AB1043/2025