Anonymous wrote:Anonymous wrote:Anonymous wrote:Anonymous wrote:Anonymous wrote:Macbooks work perfectly fine for Engineering/CS. We have kids in our extended family that are/were in Engineering programs at Georgia Tech, UIUC, CWRU and Stanford that use Apple laptops (Macbook Pros). Two of these kids are in CS.
I am not impugning Macs. I am just saying that there are some university engineering programs that tell students not to buy a Mac.
I call BS. Citation or it doesn’t exist.
If you really need to run something on Windows you run VirtualBox.
Apple M1 is better for
* creative stuff
* research and paper writing
* software development
* running Jupyter for simple data and stats stuff (any of conda macports or home brew will work here, since you don’t need the optimized linear algebra libraries for student work)
* machine learning (M1 is scarily close to GPU performance)
The power efficiency is also just a killer feature for someone who may not plug in much during the day.
good luck getting virtual box to run on an m1. just google it, the results aren't great
Right VBox it won't work. But QEMU already lets you run ARM Linux on it, and since this isn't an issue that's going to affect a freshman (and probably, not a sophomore either), it will be working by the time it's relevant.
If DC really needs Windows access, you can always set up an AWS Lightsail account (dev service, so limited from spendy stuff). Leaving a personal Windows machine running 24/7 is $8/mo (https://aws.amazon.com/lightsail/pricing/). I've used this service (not under Lightsail) and it's really snappy. But he won't need Windows access unless he's in a cybersecurity program (eg, UMBC).
Anonymous wrote:Anonymous wrote:Anonymous wrote:Anonymous wrote:Anonymous wrote:Macbooks work perfectly fine for Engineering/CS. We have kids in our extended family that are/were in Engineering programs at Georgia Tech, UIUC, CWRU and Stanford that use Apple laptops (Macbook Pros). Two of these kids are in CS.
I am not impugning Macs. I am just saying that there are some university engineering programs that tell students not to buy a Mac.
I call BS. Citation or it doesn’t exist.
If you really need to run something on Windows you run VirtualBox.
Apple M1 is better for
* creative stuff
* research and paper writing
* software development
* running Jupyter for simple data and stats stuff (any of conda macports or home brew will work here, since you don’t need the optimized linear algebra libraries for student work)
* machine learning (M1 is scarily close to GPU performance)
The power efficiency is also just a killer feature for someone who may not plug in much during the day.
good luck getting virtual box to run on an m1. just google it, the results aren't great
Right VBox it won't work. But QEMU already lets you run ARM Linux on it, and since this isn't an issue that's going to affect a freshman (and probably, not a sophomore either), it will be working by the time it's relevant.
If DC really needs Windows access, you can always set up an AWS Lightsail account (dev service, so limited from spendy stuff). Leaving a personal Windows machine running 24/7 is $8/mo (https://aws.amazon.com/lightsail/pricing/). I've used this service (not under Lightsail) and it's really snappy. But he won't need Windows access unless he's in a cybersecurity program (eg, UMBC).
Anonymous wrote:Anonymous wrote:Anonymous wrote:Anonymous wrote:Macbooks work perfectly fine for Engineering/CS. We have kids in our extended family that are/were in Engineering programs at Georgia Tech, UIUC, CWRU and Stanford that use Apple laptops (Macbook Pros). Two of these kids are in CS.
I am not impugning Macs. I am just saying that there are some university engineering programs that tell students not to buy a Mac.
I call BS. Citation or it doesn’t exist.
If you really need to run something on Windows you run VirtualBox.
Apple M1 is better for
* creative stuff
* research and paper writing
* software development
* running Jupyter for simple data and stats stuff (any of conda macports or home brew will work here, since you don’t need the optimized linear algebra libraries for student work)
* machine learning (M1 is scarily close to GPU performance)
The power efficiency is also just a killer feature for someone who may not plug in much during the day.
good luck getting virtual box to run on an m1. just google it, the results aren't great