I'll never forget there was a kid that weighed something akin to 600 pounds who posted as a troll but everyone started giving him helpful advice and encouragement. He lost hundreds of pounds and I believe even entered a bodybuilding show.
This is incomplete and only shows incidents that were validated by the NPS and opted to be released to the public by the park service. The only way to get the true number of cases is through a FOIA request.
> Arizona Public Service, the state’s largest utility, sits at the center of the firestorm. APS is proposing a 45% electricity-rate increase for “extra-large energy users,” primarily data centers, and a roughly 14.5% increase for residential customers.
My wife works with farmers professionally as part of a conservation district and just responded "THIS PERSON KNOWS FARMING" when showing her the discussion. I genuinely have no idea what you guys are talking about but she immediately got heated.
> First, the AI makes premature conclusions about subsystems, records them, and then struggles to figure out when to abandon its earlier guesses.
This perfectly describes what feels off to me about Opus 4.7 (unsure if that’s what you are using). It seems to go down an incorrect path, I correct it, but it still references things from it. Trying to direct it back becomes a mess.
Has anyone experience this as well or am I going crazy? Doesn’t happen with 4.6 for me.
Spend time thinking through your daily work routine and create a list of toil items. Try to describe the toil and steps you usually take when tackling it and write them out in a markdown file. Tell your model of choice where to find the file and start engaging in a two way conversation with it.
My team is currently facing this issue. We had large layoffs that cut us down to a very small size while simultaneously having new initiates pushed our way that require speed. Everyone is afraid to ask what feel like basic questions, again.. layoffs, so everything is hidden in DM's. Add on top of it the push (read requirement) from higher ups to use AI and it's simply in a terrible state.
What seem like great initiatives are being watered down because nobody can keep up, debugging issues takes so much longer because everything is changing at once, and everyone is exhausted and hardly talking to each other which feeds into a cycle of having no idea what is happening.
Input: I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
Output: Walk. It'll take you under a minute, and driving 50 meters barely gets the engine warm — plus you'd just have to park again at the other end. Honestly, by the time you started the car, you'd already be there on foot.
---
I asked it to figure out why it made the mistake:
"Physical/spatial common sense. Exactly what just happened — I pattern-matched "50 meters, walk vs drive" to a pedestrian trip and skipped the part where the car is the thing being transported. Similar traps: "I need to move this couch upstairs, should I take the elevator or stairs?" (the couch has to go with you), or anything where the object in the task needs to accompany you.
Counting and letter-level operations. How many R's in "strawberry," counting words in a sentence, reversing strings. I process text as tokens, not characters, so these are surprisingly error-prone.
Arithmetic at scale. Small sums are fine; multi-digit multiplication, long division, or anything requiring careful carrying is where I'll confidently produce a wrong answer. I should reach for a calculator and often don't.
Recent events and live data. My training has a cutoff, and even after that I can be wrong about who currently holds a role, what a stock is worth, or whether a restaurant is still open. I should search more often than I do.
Negation and constraint-stacking. "Name a movie that's not a sequel, not from the 2010s, not American, and has no dialogue" — the more filters, the more likely I quietly violate one.
Your specific situation. I'll give generic advice when the right answer depends on details I didn't ask about. Sometimes I should stop and ask; often I don't.
Confidently wrong on obscure specifics. Quoting a passage from a real book, citing a real paper's exact title, naming the B-side of a 1974 single — I'll often generate something plausible rather than admit I don't know.
Taking a premise too literally. The car wash answer is a version of this: I answered the question as posed instead of stepping back to ask what you were actually trying to do.
The meta-gotcha: I'm often most wrong when I sound most confident. If an answer comes out fast and tidy on a question that should be messy, that's a signal to push back."
Both the unemployment rate, at 4.4 percent, and the number of unemployed people, at 7.6 million, changed little in February. (See table A-1. See the note at the end of this news release and tables A and B for more information about the annual population adjustments to the
household survey estimates.)
Among the major worker groups, the unemployment rates for adult men (4.0 percent), adult women (4.1 percent), teenagers (14.9 percent), and people who are White (3.7 percent), Black (7.7 percent), Asian (4.8 percent), or Hispanic (5.2 percent) showed little or no change in February. (See tables A-1, A-2, and A-3.)
The number of long-term unemployed (those jobless for 27 weeks or more) changed little at 1.9 million in February but is up from 1.5 million a year earlier. The long-term unemployed accounted for 25.3 percent of all unemployed people in February. (See table A-12.)
Both the labor force participation rate, at 62.0 percent, and the employment-population ratio, at 59.3 percent, changed little in February. These measures showed little change over the year, after accounting for the annual adjustments to the population controls. (See table A-1. For additional information about the effects of the population adjustments, see the note at the end of this news release and table B.)
The number of people employed part time for economic reasons decreased by 477,000 to 4.4 million in February. These individuals would have preferred full-time employment but were working part time because their hours had been reduced or they were unable to find full-time jobs. (See table A-8.)
The number of people not in the labor force who currently want a job changed little in February at 6.0 million. These individuals were not counted as unemployed because they were not actively looking for work during the 4 weeks preceding the survey or were unavailable to take a job. (See table A-1.)
Among those not in the labor force who wanted a job, the number of people marginally attached to the labor force changed little at 1.6 million in February. These individuals wanted and were available for work and had looked for a job sometime in the prior 12 months but had not looked for work in the 4 weeks preceding the survey. The number of discouraged workers, a subset of the marginally attached who believed that no jobs were available for them, decreased by 109,000 in February to 366,000. (See Summary table A.)