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Comment hmm, old data (Score 1) 194

Just noticed the 11717 MW was lower than the 17,000 so took a closer look, while that axis article was updated August 14, 2026 it used data from 2024:

according to the U.S. Energy Information Administration’s State Energy Data System for 2024

More recent data from July 2026:

Texas has now passed California to lead the country, at 17,082 MW against 15,698 MW.

Comment We have battery storage in Texas (Score 1) 194

Texas has 7,905 MW of battery storage, putting us at #2 in the US behind California's 11,717 MW, and I'm paying 12 cents a kWh.

Check the Previous Day on ERCOT's Fuel Mix you'll see the same thing is happening in Texas, though ERCOT's graph shows the sun in orange instead of yellow and our battery storage is shown in maroon instead of blue.

Comment Re:Taking a good thing too far (Score 1) 252

n the first, it was following the installer down the road, which is always suspicious.

And the installer was installing cameras that follow everyone down the road. So you say that following one person down the road is "always suspicious", do you agree that following EVERYONE down the road is "always suspicious"?

If the reporter had gotten there first and installed cameras all along the road to follow the Flock installer, would you say that is less suspicious?

Comment Re:We all know the real reason (Score 1) 118

The compute cost for DeepSeek V3 was $5.6M. Training costs for traditional US frontier labs model is about $70 to $200M+, with estimates of up to $1B+ to train the next generation models. It's expensive, but it's not even in the ballpark of "There is no viable economic model to do so." Google revenue in Q2 was $1.32 billion *per day*. $1B is trivially less than a day; $5.6M is just 6 minutes of revenue.

The cost problem is in the compute buildout and the subsidized compute to capture users.

> It remains to be seen if even using their models can be profitable,

I presume we can stipulate there exists some tasks that are cheaper to do using an LLM than doing it "by hand". As a such, using an LLM model can be profitable. To me, it is also obvious that the business of serving an LLM model can be profitable. There's large benefits to being able to get tokens quickly, and you can expect people to pay for that, even if we presume they could run the model locally with no maintenance overhead. There's even cases where using the cloud is cheaper than you can serve locally at all - I saw one guy that measured his added cost of electricity when his computer ran a specific LLM, and it was more expensive to run it locally than to buy tokens from the cheapest inference provider for the same model, presumably because they had better hardware and cheaper electricity.

Comment Re:I just wish (Score 1) 96

Im a bit worried it might be too wide but I do like the idea of a smaller phone that expands into a fairly capable iPad mini. I never understood the Samsung obsession with the clamshell design, that doesn't give me much flexibility... and the tri-fold just seemed a bit too much when I saw it in person. I haven't seen a folding phone that had dimensions I could live with day to day.

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