Intent (should) be what matters. If you want to learn how to train AI and use copyrighted material in your learning - I don’t care in the slightest at all.
In fact if you do this as a nonprofit or at an educational institution in a teaching context it’s explicitly allowed by fair use already.
If you do it individually, idk I’m not a lawyer. But it should be allowed on principle.
But if you then go take your trained AI and deploy it for commercial purposes that’s a different story and should have protections for the original rights holders.
“Fair use” allows for educational usage of copyrighted material. Technically it probably is not fair use as Microsoft isn’t an educational institution or a nonprofit.
But come on … these guides really are for learning purposes. Doesn’t seem like a big deal to me at all. They aren’t even hosting it, just pointing to kaggle who is hosting it.
On principle copyright law should allow this kind of learning use case anyway.
Yea agree.. This is the same discussion point that came up last time they had an incident.
I really don’t buy this requirement to always deploy state changes 100% globally immediately.
Why can’t they just roll out to 1%, scaling to 100% over 5 minutes (configurable), with automated health checks and pauses? That will go along way towards reducing the impact of these regressions.
Then if they really think something is so critical that it goes everywhere immediately, then sure set the rollout to start at 100%.
Point is, design the rollout system to give you that flexibility. Routine/non-critical state changes should go through slower ramping rollouts.
Code and Config should be treated similarly. If you would use a ring based rollout, canaries, etc for safely changing your code, then any config that can have the same impact must also use safe rollout techniques.
Does their ring based rollout really truly have to be 0->100% in a few seconds?
I don’t really buy this requirement. At least make it configurable with a more reasonable default for “routine” changes. E.g. ramping to 100% over 1 hour.
As long as that ramp rate is configurable, you can retain the ability to respond fast to attacks by setting the ramp time to a few seconds if you truly think it’s needed in that moment.
100% agree. i’m not sure why everyone is clowning on them here. This process is a win. Do people want this all being hidden instead in a forked private repo?
It’s showing the actual capabilities in practice. That’s much better and way more illuminating than what normally happens with sales and marketing hype.
I actually have the opposite position on this. 1st world countries already have the funds and economy to pursue exactly what you describe. Just they lack the political will. I don’t care to subsidise that intentional lack of investment.
I would much rather give to charities focusing on countries that don’t have the economy/ability to fix their basic issues.
Call me pessimistic, but as the sidewalk pattern becomes more common for IoT, I wouldn’t be surprised if a “malfunctioning radio” just results in the device not working properly.
Make it a roundabout with protected pedestrian crossings. That forces drivers to be looking at the conflict point with pedestrians as they manoeuvre the roundabout.
If you do not wait in the intersection itself then you would never get a chance to turn in many intersections. The only solution is to always wait in the intersection itself.
In an actual emergency, cell towers may not function.
AM has significantly further range too and is pretty easy to standup as a backup for an area.
It’s also incredibly common for road conditions to still be shared over AM - you see this a lot while driving with all the “advisory - tune to …. AM” signs everywhere.
This has been described as similar to Uber/Lyft surging but it’s different in a few critical ways that I suspect consumers are less tolerant of.
When Uber/Lyft are surging it incentivise more drivers to go to the surge area. This raises supply and the surge rate decreases. Drivers are distributed automatically where they are needed. Overall trips taken should be higher compared with a no surging model. So it shifts both the demand (higher ride price) and the supply curves dynamically. That’s an easier model to market to customers as there is at least some logical sense behind it.
However in Wendys case dynamic pricing has no effect on supply. It just modifies the demand curve.
Fundamentally they are betting that their food demand is inelastic enough that they’ll make more money overall. That just feels more exploitative and is going to be harder to market.
I’ve done something similar in one of our production services. There was a problem with extremely long GC pauses during Gen2 garbage collection (.NET uses a multigenerational GC design). Pauses could be many seconds long or more than a minute in extreme cases.
We found the underlying issue that caused memory pressure in the Gen2 region but the fix was to change some very fundamental aspects of the service and would need to have some significant refactoring. Since this was a legacy service (.net framework) that we were refactoring anyway to run in new .NET (5+), we decided to ignore the issue.
Instead we adjusted the GC to just never do the expensive Gen2 collections (GCLatencyMode) and moved the service to run on higher memory VMs. It would hit OOM every 3 days or so, so we just set instances to auto-restart once a day.
Then 1 year later we deployed the replacement for the legacy service and the problem was solved.
It’s pretty simple - software engineering compensation can be 2x-4x or more in the US compared to other western democracies. Money circumvents a lot of the problems you describe.
In fact if you do this as a nonprofit or at an educational institution in a teaching context it’s explicitly allowed by fair use already.
If you do it individually, idk I’m not a lawyer. But it should be allowed on principle.
But if you then go take your trained AI and deploy it for commercial purposes that’s a different story and should have protections for the original rights holders.