I'm really curious about the profile of who is still working there and their reasons for being there. Not speculating. Just the facts.
fubarx
joined 1 year ago
Before podcasts, there used to be RSS readers. They let you quickly subscribe and scan a lot of blogs and catch up on latest posts. Flipboard started as a fancy mobile app version, with access to articles from mainstream news providers.
It's grown and changed a lot, but it's essentially a quick way to scan for interesting daily news.
They've now added ways to subscribe and catch up on Fediverse sources like Mastodon (and soon, Lemmy).
The CEO has a podcast called Dot Social where he talks to people about how this Fediverse stuff all fits together: https://dot-social.simplecast.com/
I just spent the weekend installing 24.04 on a Lenovo laptop as a self-hosted server. The only thing that didn't work was the fingerprint scanner. No big deal, but it seems peripheral device driver support is still a bit janky.
Running the self-hosted apps under Docker, though, worked without a hitch. chef's kiss
You've been using cheap cables.
Next step up is a JCAT: https://audiobacon.net/2019/11/02/the-jcat-signature-lan-a-1000-ethernet-cable/amp/
/s if not obvious.
I just posted a link to a review by someone who has been playing with it for a while and talks about a lot of use-cases: https://lemmy.ml/post/18938549
This topic came up when self-driving was first coming up. If a car runs over someone, who is to blame?
- Person in driver seat
- Dealer
- Car manufacturer
- Supplier who provided the driving control system
- The people who designed the algorithm and did the ML training
- People who wrote and tested the code
- Insurer
Most of these would likely be indemnified by all kinds of legal and contractual agreements, but the matter would still stand that someone died.
Just spent a week manually moving everything off Authy. Total pain, but there are lots of better solutions out there now.
I'vd tried multiple times to convert existing code or createnew ones using LLMs. The first attempts are OK, but once you start refining the prompts, they all go off-the-rails.
Most of the time, the generated code uses old or deprecated libraries or APIs. You point that out and they correct it. But a few iterations later, you're refining something else and the old, deprecated calls come back. Once again, you point it out and it gets corrected.
Forget trying to correct it yourself by hand, because now it's diverged from the LLM context. And this can happen in multiple places in the code. Rinse. Repeat.
At some point you just give up. Either it's wrong or it will be wrong in different ways later. You have to read through every line to find strange, divergent errors. Over and over. It gets exhausting.
At the end, it feels like maybe you could have done it faster and more quickly yourself, but the time has already been sunk.
Anything they go after today is 18-24 months out. Chasing after AI would be pretty risky. Desktops and laptops are moving to ARM and RISC-V. Their best bet is to go after whatever enterprise data centers will need a couple of years from now.
If I were laying bets, it would be to go after power and heat efficiency. Like, hard. Take their time out in the wilderness, then come back with chips that save the planet from climate collapse.
Most discovery is via hashtags since you can subscribe to one (for example #press gets you lots of news).
Also, following and blocking individual accounts, as appropriate. You're not going to get the sort of random exposure to strangers that algorithmic boosting gets you on other social media.