It really depends on the peering contract. Most are not for transit, but rather just destinations, and generally the side that sends more pays, so that means more traffic to HE if tunnels are in use.
I've found what works best here is just switching to every 2 weeks or every 4 weeks. If you have little to talk about in a 1:1, feel free to end early, and then double the length of time until the next one.
I agree, but these are also the exact constraints that lead to an early leader getting overtaken by a longer term, yet better set of plans. Not saying that's the case here, but given how much success Waymo has had so far, over really everything Tesla has produced, says quite a bit about the likelihood of the approach, even if it's not yet there.
This is an interesting viewpoint, but isn't it also solveable?
Just because the human in the scenario only took vision as input, why does that matter to the training data and the model? The actions are the same.
To put it another way, what about all the cultural context the human had, or the sounds, smells, past experiences at the same intersection, etc? Even Tesla can't record this, but I'm not sure that matters.
While I agree with basically all of this, and find the FSD on my Tesla to be quite useful, a question pops into my mind.
Why can't Waymo ALSO develop the same smarts and just also solve the sensor fusion issue such that they can use the right set of sensors in the right environmental conditions, and then leapfrog Tesla's capabilities?
I feel you on every point, but I'd point out that they sell VERY reasonably priced replacement parts for basically everything on the A1 Mini that I have that is worth repairing.
If you turn on LAN mode, it acts exactly like every other printer. You can print directly to it from any slicer over your LAN, or dump gcode on the SD card directly.
You're missing two things from the whole picture:
1. Cloud mode works without local network access, so their server is involved in the transit of the data to the printer. This is pretty minor, but still within their rights to preserve.
2. For printing from the app, they actually run the computationally expensive slicing algorithm on their servers, so this is totally reasonable to protect.
This is surprisingly close to a personal theory I've been working on. I've been describing how to use AI to people as engaging the world model in their head, organization, or software.
I'd love to talk more live. I think I have some ideas you'd be interested in. Find me in my profile.
I find connecting understanding between humans and agents is one of the most important parts of the agentic development cycle, and markdown is a great way to handle that.
Not only can you point it at an entire directory, you can point it at multiple projects, quick load a project with a keyboard shortcut, and also easily see recent file that changed to help you find the 75th file your agent just wrote for you.
Recently, I've started to add a review interface where you can track changes, and add comments for your agent, and then instead of trying to do some complicated integration with an agent, it just has a copy button, and it copies all the comments, which context, and instructions for the agent how to reply.
I also find that I generate TONS of markdown junk during development, and I needed a way to handle it and keep it out of the main repository so I built this tool:
I use it because they offer absurdly cheap prices that they're clearly losing money on. I can get $1000 at API prices of Opus 4.6, for in the range of $2 my cost through copilot.
You can do this with copilot, for the $40/mo range, AND you get to use opus 4.6 for all of it. Copilot is absurdly cheap if you can make it fit your work profile.