It's a backup so that if you do lose power, you can still pedal home. Major pain to pedal an 80 lb fixed gear bike, but This is standard on ebikes and doesn't contribute much weight or cost in itself.
Joe smokes weed in a state that outlaws it. Elon rants about fraud when he has been the single biggest purveyor of vaporware to the federal government. Thiel is married to a man but promotes the consolidation of power for the advancement of project 2025 because the super rich are immune to any new deprivation of freedoms.
All that guarantees freedom in the new system is adequate measures of compliance and capital. For all others, name the person, and the machine will autonomously identify a thought crime.
The best part about rolling back free speech protections is that the internet is full of evidence for ex post facto indictment. Enemies of the state have been openly parading their dissent. For those who recognize this, speech is already chilling fast.
Look mom! I’m in a hearing for a passive aggressive internet post :D
A strategy games like baldurs gate would be amazingly immersive if selected characters were fixed to the table and the full world was rendered to the horizon
Follow the golden rule, be the person that you would want to work with. Be valuable enough so that you have some choice in which team you join.
I don't know of any other advice to this end that would apply universally. It depends on specifics of the operating modes and types of relationships you'd develop in the companies that you would fit with.
Seconding this. Thanks for sharing! Also my page crashed on iphone14 with chrome when attempting small changes to the pyodide cell (even just changing the axis labels)
At 2 trips per day for 300M Americans over 7 days, that would put the rideshare takeover at ~4.2Bn. If we extrapolated based on the referenced graph and exponential growth, that would put the takeover at 2029 :)
Its safe to assume that the limiting factors will soon become sourcing of components of the perception and control stacks.
Yeah, I wonder how much money they're pouring into lidar production. Particularly considering that they've partnered with Hyundai, Stellantis, Mercedes-Benz Group AG, Jaguar Land Rover, and Volvo.
I work on Time-of-flight camera's that need to handle the kind of data that you're referring too.
Each pixel takes a multiple measurements over time of the intensity of reflected light that matches the emission pulse encodings. The result is essentially a vector of intensity over a set of distances.
A low depth resolution example of reflected intensity by time (distance):
i: _ _ ^ _ ^ - _ _
d: 0 1 2 3 4 5 6 7
In the above example, the pixel would exhibit an ambiguity between distances of 2 and 4.
The simplest solution is to select the weighted average or median distance, which results in "flying pixels" or "mixed pixels" for which there are existing efficient techniques for filtration. The bottom line is that for applications like low-latency obstacle detection on a cost-constrained mobile robot, there's some compression of depth information required.
For the sake of inferring a highly realistic model from an image, Neural radiance fields or gaussian splats may best generate the representation that you might be envisioning, where there would be a volumetric representation of material properties like hair. This comes with higher compute costs however and doesn't factor in semantic interpretation of a scene. The Top performing results in photogrammetry have tended to use a combination of less expensive techniques like this one to better handle sparsity of scene coverage, and then refining the a result using more expensive techniques [1].
