That said, thanks for sharing the emails/headers.
It's curious that Amazon hasn't flagged you for purchasing & reviewing multiple similar items in such a short span of time. I would imagine it would be quite easy to spot someone who's bought and reviewed 12 vacuum cleaners in a 2 or 3 year window.
You've got stores that would include a $5-$20 coupon/gift card in the item in exchange for a positive review. Sure, this didn't 1:1 translate but if a user did it would look like a legitimate review.
You've got a plethora of LLMs out there just itching to GENERATE.
Then an expensive option I was suprised happened - I bought a Dyson clone vacuum cleaner off of Amazon. A few weeks later, the company emailed me and said 'We have a new model. Buy that one, leave a review, we'll refund the purchase'. So I did it. This happened about 10 more times in 2024. My outdoor shed is entirely stick vacuums.
Feel a bit dirty doing it but that's ok I've got 12 vacuums that can clean my conscience.
I think Fakespot would have difficulty with all 3 of these scenarios.
Dependency resolution is slow because it's computationally very expensive. Because uv is written in Rust the resolution is just much much faster. IIRC they actually reuse the same resolution package that Cargo (Rust's package manager) uses.
The dependency resolution computation is an interesting problem. I think poetry at some point switched to mypyc for compilation (although I can't find conclusive evidence for it now). From my experience, mypyc doesn't really improve performance much compared to say writing a c/c++ extension. Perhaps offloading dependency resolution in poetry to a native c library is a way to match uv.
no self awareness, no reflection. just impulse. me, me, me.
blasting music in public, talking at max volume, slamming doors. taking 20 mins to use an ATM when it takes me 30 seconds. and so on.
I'm not sure I trust this. A quick search finds a Psychology Today article about it along with a single reference. I lazily suspect the result is based on some type of questionnaire.
The way "chain of thought" is used in LLMs to improve reasoning demonstrates, to me at least, the value of capturing intermediate steps in some rich compressed structure. Nothing beats that than words and sentences (see them or hear them). A lot of ideas can't be captured with just photos alone imho.