After 3 years out of production, a product page showed an offer too good to be true. By applying for another amazon credit card, they'd reduce it to what it was 3 years ago: $100.
It was a scam. You're not getting that kind of confusing power for $150 anymore. There was another updated listing in China.
Of course, a lion who is part monkey can't let go of the fruit. Now a 3 week delay wasn't too much to find out if the Chinese listing was also a scam. The justification was manely to stop using the junk laptop lions had used daily since 2018 for rep counting. Pay now & maybe you'll get your order in a month.
Bezos's $50 discount was refunded as a gift card balance so the total cost was still $100. Lions had other Bezos credit cards in the past. It seems they eventually expire & don't get renewed, at which time you can apply for another one & get another discount.
To be fair, the $100 4GB Jetson nano never shipped until 2023. They only had a $50 2GB version 3 years ago.
2 weeks later, it was real. The mane trick is the SDK image had to be written to a 32 gig card. It didn't work with a 64 gig card & it's too big for a 16 gig card. It might need a manually created partition table to use a 64 gig card. The mount command works with the raw image when providing -o offset= but not when using the partition table.
Setup has to be done with the ttyACM device. It's a pain. 1st, you have to view the output from UART RXD, UART TXD to find out when it has booted. Then you have to connect to ttyACM0 & hit enter a few times.
No terminal program could render the text. It was a matter of reading a terminal capture file to read the prompts & planning on fixing any errors from the command line.
Once setup, logins are provided on all the UARTs & ttyACM, but only UART RXD, UART TXD allows a root login.
Most animals connect a keyboard, mouse, & monitor to use the graphical setup.
Then came disabling a bunch of programs by renaming them .bak.
The mane thing is verifying it really has 4GB. By default, it has some kind of compressed swap space in RAM by using a /dev/zram device. Having the CPU & GPU share the same RAM doesn't leave as much room as you thought. Sadly, it's only useful for running neural networks but not training them. It shows how insanely expensive AI has been & probably always will be.