I really like using aider (https://aider.chat) and heavily leverage the /ask command to have a quick chat on intent and context before prompting it for the code I need.
My understanding (not an expert) is a lot of problem domains have very sparse / infrequent rewards - imagine if the only reward you gave a minecraft agent was when it mined a diamond, it would take a lot of gameplay for it to randomly do that and get a reward. So researchers spend time tuning the reward space (oh you mined some dirt, here's a tiny reward. Oh you mined rock, a greater reward, etc) but it's kind of akin to hand crafted feature detection from the pre-neural network days. The Q* mystery is did OpenAI 'solve' reward modelling the same way neural networks solved feature detection.
Probably worth considering the efficiency of converting that stored energy to useful work. Average ICE efficiency is ~20% yielding effective storage of ~2600 Wh/kg.
Roughly an "order of magnitude" more density.
EDIT: Hadn't refreshed the page since lunch. Many have made this point already. Apologies haha
Internet traffic via a geostationary satellite has a minimum theoretical round-trip latency of at least 477 ms (between user and ground gateway), but in practice, current satellites have latencies of 600 ms or more. Starlink satellites would orbit at 1⁄30 to 1⁄105 of the height of geostationary orbits, and thus offer more practical Earth-to-sat latencies of around 25 to 35 ms, comparable to existing cable and fiber networks[51] (although transmitting a signal halfway around the globe takes at least 67 ms at the speed of light).