I've been re-building my NAS and decided on NFS on top of ZFS. However, my goals are slightly different than the author: I'm using a low-power cpu/motherboard, and I've built 2 distinct systems that can fail over as a form of high availability. I've actually written some custom software to handle the failover and data replication. In my testing, clients see writes block for just a couple second during failover. Would anyone be interested in a writeup about this?
I'm totally on board with the idea that ORMs create a variety of inefficiencies, pain points, and make it really easy to create bad queries or querying strategies. But I use them anyways because the convenience of mapping a row to a code object makes writing programs feel fast and simple. And if you know how ORMs can cause problems and how to watch out for them, you can still get a lot of mileage out of them.
That being said, what's the closest alternative that satisfies this - "mapping rows to a code object" - that doesn't suffer the same problems as an ORM? A middle ground between an ORM (like SQLAlchemy, for example) and "your rows are returned as a key/value dictionary where the column names are keys" type approach like Python's DB-API's DictCursor or PHP's mysqli_fetch_assoc. Is there a middle ground here?
Both articles use 2017 as the turning point date. TTS is a lot older than that. It's not difficult to find data to fit the desired point if you choose a narrow enough time range. Or location selectivity - both of those are just about the United States.
Technologies: Linux, Go, Python, Docker, Kubernetes, AI/ML on Kubernetes, AWS (S3, EC2, VPC, EKS, RDS, others), MySQL/Postgres, Redis, Terraform, Jenkins. Site Reliability. Limited exposure to many more such as Kafka, FoundationDB, Cloudflare.
I've done just about everything Silicon Valley has to offer; working for tiny, unprofitable startups, larger startups pre, post, and through IPO and acquisition, as well as giants with 100,000+ employees.
Their decision to leave X seems mostly centered around engagement numbers. Or at least, that's the reason they led with. And I'm not sure that I believe the numbers they're throwing out.
> To put it bluntly, an X post today receives less than 3% of the views a single tweet delivered seven years ago.
Okay. View counts are public now, but not available on older tweets. But replies, like, and retweet counts are, and shouldn't they scale similarly?
I'm just eyeballing it, but when I look through the EFF's twitter feed now, I see 20-100 likes as typical, with the occasional popular tweet that hits a couple hundred. When I look at their 2018 tweets - you can use the `from:EFF until:2018-04-01` filter on twitter search - the numbers are... The same. Aside from the occasional popular tweet, most other tweets are in the neighborhood of 20-100 likes. Similar for replies and retweets.
I don't understand how this could be if the tweets are being seen 30x less.
Yep, came here to look. Says "Hello Select your address" in the header instead of my delivery address, and when I click it to set my address, the modal says "Sorry, content is not available."
Also seeing stock / availability display on product pages acting weird - things unavailable that certainly actually are, like dishwasher detergent.
Error 500 when I change my selected address in the checkout flow too.