There was a third ruling in November 2022 for the case, so I wouldn’t say hiQ won. Based on a quick scan, the ruling ended up being that hiQ did violate TOS but that no action ended up being taken for either side.
Your description of always feeling hungry after keeping it off is rather worrying to me, since I’m getting close to my target weight after a 1 year+ journey of diet and exercise.
I do remember reading before that the fat cells that get created when you gain weight never really go away, they just “deflate”. So after you lose weight, you are more likely to gain the weight back than someone who is at the same weight now but who has never been bigger than that.
I also recall seeing a study that your body sends you extra hunger pangs to gain the weight that you lost back, because it assumes that losing weight means you’re in fight-or-flight mode, so it wants to make sure you survive. That’s why a weight loss drug where an extra 300-400 calories per day were excreted via urine did not show significant results.
I couldn’t find the exact study for the second part. So I’ll just leave a general reference from Northwestern alluding to the same results.
Kinesis has a 60-day money back guarantee if you buy from them, minus the cost of shipping. These are also sold on Amazon, which has a really good return policy so you can return the item with no hassle if you don’t end up liking it.
Of course, this is all presuming that
A. You have enough capital to buy the keyboard and are ok with not having $300+ for a month or two
B. you can get used to the keyboard within a 30 day period and get a feel for how you’d like to use it full time
I’m still a little confused on what dbt does after reading the article. Is it like Trino that generates materialized views as output, with built-in version control and other features?
I work in one of the data platform teams at a social media company. Between our 3 HDFS clusters, we're storing more than an exabyte of data. At our scale, we have to tune our workloads carefully to make sure that problems of scale are not noticeable to internal customers (data scientists, analysts, etc.).
We basically have an entire org of highly paid engineers focused on making sure people can use that data efficiently. So we have a team of people working on storage, on Spark, on Presto/Trino, on data ingestion, and so on.
So my understanding is that we're investing in engineers to improve data science productivity, so that they can do analysis without having to understand the internals of all our systems, so that executives can make informed decisions backed by data to continue printing money. Or something like that...
https://www.natlawreview.com/article/court-finds-hiq-breache...