Checks all of your boxes:

   - Backed up to cloud 
   - Has a mobile app that is easy to make small changes 
   - Simple file format like markdown 
   - Option to export data out as a backup  (it's just markdown files, you don't even need to export, just copy them. Or export to PDF)
   - Allow uploading arbitrary files like PDFs, images, receipts etc...  (https://obsidian.md/help/file-formats)
   - Nice to have: support for inline tables of data with simple calculations/sorting

Google Docs was built as a MS Word competitor and that's what it does best. I love Google Docs and I use it every day, but this is one thing I wouldn't use it for.

Software Engineers suddenly feel like they're fighting for their lives for employment, and time won't be "wasted" maintaining OSS for free.

We all need to eat.

You are not in defense contracting. You are in the business of war contracting.

Take from that what you will.

The harness on the other hand. Now that had problems.

*John Ternus to become Apple CEO*

Talk about burying the lede, lmao.

Among the dup stories submitted, this one has the best content but the worst title.

What if your AI uses an O(n) algorithm in a function when an O(log n) implementation exists? The output would still be "correct"

No, unfortunately. In a past life, in response to an uptime crisis, I drove a multi-quarter company-wide initiative to optimize performance and efficiency, and we still did not manage to change the company culture regarding performance.

If it does not move any metrics that execs care about, it doesn't matter.

The industry adage has been "engineer time is much more expensive than machine time," which has been used to excuse way too much bloated and non-performant code shipped to production. However, I think AI can actually change things for the better. Firstly, IME it tends to generate algorithmically efficient code by default, and generally only fails to do so if it lacks the necessary context (e.g. now knowing that an input is sorted.)

More importantly though, now engineer time is machine time. There is now very little excuse to avoid extensive refactoring to do things "the right way."

Performance can be a direct target in a feedback loop and optimised away. That's the easy part. Taking an idea and poof-ing a working implementation is the hard part.

Test what you care about. If you care about performance, then test your performance. Otherwise performance doesn't matter.

OpenAI is bleeding money faster than they can afford to and they are literally running out of people that they can go to for more. They need to stop the bleeding.