My 2021 Model Y LR says 297 Wh/mi over 42.7k miles.
* I live in southern Wisconsin (temps swing from -20F in February to 100F in August)
* Gemini snow tires November through April, Inductor wheels otherwise
* Source: The odometer matches Trip B, never reset.
I'm much happier with the screen reporting % state of charge instead of EPA miles remaining.
The world is so dependent upon ARM’s designs, yet their revenue is relatively small. Public companies are expected to grow year after year after year. It will be interesting to see how they do that without becoming a competitor to their customers. I wish them good luck!
Not a Rust dev, but I thought this tutorial was great the way it stepped from the obvious, through the nitty-gritty performance enhancements, before ending up using an elegant feature that you probably wouldn't appreciate very much without knowing what it's doing for you under the hood.
21 for us, same joint accounts. It probably helps that we both have financial niches we like to pay attention to. I’m the retirement and investments person while she’s the paychecks and credit card person (we don’t have a budget per se, but CC is a nice way for us to get a spending report without much effort).
"Our" radio is decidedly more modern, circa early 1970's, but it has played thousands of hours of St. Louis Cardinals games when KMOX 1120 kHz would fade in about 7 PM up here in Western Wisconsin. Every time I consider ripping out the guts to make it a bluetooth streaming speaker, I can't go through with it.
By the time I switched to Mac it was already MacOS X Jaguar and it had vi which I was used to. I didn’t buy my first BBEdit license until 2 years ago (!!) thanks to a blog post I read. It’s a great editor. I’ve always liked macOS because it felt like the Linux I use at work, but BBEdit is a great reason to not run vim fullscreen.
There’s a number of indie devs who rely on https://github.com/ccgus/fmdb for fast persistence. The rebirth of NetNewsWire came with FMDB at its core (https://inessential.com/2020/05/18/why_netnewswire_is_fast). What’s not clear is if a project starts with, say, SwiftData, and finds it to be a bottleneck, how easy is it to remove unless one goes to great lengths to make a clean API between the 2?
I think I’ve been paying close to the same price for computers for 25 years. Moose’s Law and relentless miniaturization and cost optimization are the only reason why.
This is what the industry calls 2.5D technology. Intel has EMIB and TSMC has CoWoS. It’s one way the industry has been able to keep Moore’s Law limping along with chiplets.
As long as their scalar and vector units still support packed 8/16/32-bit data, this seems fine? Too bad there’s no OS telemetry on the number of legacy apps still in operation.
I have 4 HDDs in NAS and one 12 TB drive for Time Machine. It’s great to have 4 TB in your MacBook, but it’s so expensive and I personally don’t need to actively have more than 1 TB at any one time (and maybe not even half that), meaning the rest can be offloaded to a NAS.
Ruby's Queue structure to push work, and Thread for spinning up workers based on the number of cores on the machine. Main thread would push all commands to run to the Queue, followed by ${N} shutdown hints, and ${N} Threads would pick them off in a while loop that would only stop when it saw a shutdown command. Once the last thread consumed the last shutdown hint, all threads were done and the script would exit. This was barely one step beyond a bash script that backgrounded all tasks at once and swamped a host until it slowly finished up.
Before GNU Parallel I used to use Ruby's workers and job queue to keep ${N} cores busy with work. It sorta worked like GNU parallel but was quite basic. I've since switched to using GNU Parallel. Stable code I don't have to write doesn't have to be maintained... not to mention it has more features than I normally supported.
Rexx was my first foray into writing code using OS/2 around 1994. I then tried my hand at Pascal before getting into college and taking a formal C++ class.
“In our case, we developed a custom chip to transcode video, as well as software to coordinate these chips. And we put it all together to form our transcoding special brain – the Video (trans)Coding Unit (VCU). We’ve seen up to 20-33x improvements in compute efficiency compared to our previous optimized system, which was running software on traditional servers.”