First impression of 5.6 Sol in Codex is fantastic — the model asks dozens of clarifying questions before starting to implement where other models (including 5.5 and Terra) just yolo it with assumptions that needed to be walked back later.
Interesting that your list omits China considering they have firm plans to launch ~41k satellites for government/commercial constellations plus ITU filings for >200k proposed satellites.
Neat, implementing this would be a great marketing move for Google/OpenAI/Amazon/MS. Relatively cheap way to win a lot of goodwill from the millions of people who don't know much about the space but are swayed by current water usage arguments.
Of course location matters a ton in the water usage argument but I'm not sure how relevant this actually is when it comes to winning over hearts and minds.
Tons of traditional companies have x-relations employees, they're just not titled as such. Attending trade shows, developing distributor relationships, constructing and distributing demo materials, building local/state/federal government support networks, driving the branded truck around town — all of these are very common in non-tech industries and are basically analogous to DevRel engineer responsibilities.
I find golf courses to be a more effective framing. Even if the alfalfa is consumed by animals, it's still a part of the food supply chain and gives people the easy response, "yeah, but we need to eat, we don't need datacenters."
Google's 10.9B gallons in 2025 is equivalent to ~55 18-hole golf courses (200M gallons/year average in the US). Which provided more value to the economy and to you as an individual last year? Google or 55 out of ~15k total golf courses in the US?
Appreciate the long answer. Why is it more likely that Gemini 3 Pro/Flash/Lite are distillations of the same parent model than that they’re different training runs on the same dataset, with minor version bumps being different post-training setups?
Co-founder: https://kolena.com
Atlas of Space: https://atlasof.space