All authorized shares are issued, and then the charter is amended through board action and more shares are authorized and issued at each stage.

Reinvesting is a minority, edge case. I can't think of a single VC company in the Bay area that can have founders reinvest shares and not cause issues raising.

If it's a positive outcome, then preference has no role.

This game models good outcomes, with warnings for things like down rounds

There are a ton of edge cases, but the goal was to serve as an intro, and to get a grasp of the basics.

Links in the intro to a more serious look at things.

Comments URL: https://news.ycombinator.com/item?id=47915274

Points: 36

# Comments: 24

Ask/comment away!

Comments URL: https://news.ycombinator.com/item?id=34631711

Points: 3

# Comments: 2

Our current test vehicle uses remote operation a lot!

As with any large opportunity, there are multiple players in the space, both new and old. As seen with the "innovator's dilemma", I'm not sure I'd place my bets on tech straight out of the 1980/90s/2000s (sheep or no sheep ;) )

We are approaching the ML problem somewhat differently, we try to give the robot an understanding of navigability of a space. This is done with semantically segmented images, overlayed with depth data where needed.

Once we have this, we flatten it into a 2d costmap of areas where kinematically (ie: ground clearance, terrain handling ability, allowable areas, etc) the vehicle is allowed to go. This is fed to our planner, which in turn generates valid paths for the vehicle to take.

The particular cameras and lidars used are abstracted away in a Hardware Abstraction Layer (HAL) that I've described a bit elsewhere on this page.

Cheers!

Definitely, a lot of our work goes into ensuring our Hardware Abstraction Layer (HAL) works well across vehicle types and sensor types. Check out some of my other replies here to double click on this.

For your other notes, Stefan's already covered it, but the TLDR is that we are building on vehicle autonomy, not simulation environments or systems (Caladan is just to get people an easy way to use the Polymath API, without needing a robot first :) )

Our actual product is autonomy on real vehicles.

We don't plan to ever build any kind of high fidelity sim, just sticking to basic Gazebo or similar.

The approach we are taking to this is two fold: 1) Robust self monitoring and metrics, ensuring we measuring system performance (we are using Prometheus to allow us to scale to (hopefully) millions of devices. 2) Ensuring we tie those metrics to a rolling buffer of the last 90 seconds (or so) of data. When an issue occurs, we automatically save to disk, and allow remote operators to pull the raw sensor data as needed.

With the raw data, we can (and do!) play this back through our stack and step through the behavior tree and control outputs, comparing them to what actually happened on device. The system is certainly deterministic enough to allow this sort of debugging and testing!

We will have a set of articles on this approach in the future!