3,257 karma · joined September 5, 2024
XGBoost’s search optimized accuracy, and afterwards I also compare by area under the curve. Which means the “fourteen of fourteen on AUC” is against a boosting model that was not tuned for that metric. Tuning it for AUC would probably improve it there; I did not measure that.
So, not a fair test?I also did not get why Xgboost had to count its training time for the inference. You only train once. I guess in some scenario, where someone says, "I need the best model now, you have five minutes on this singular dataset", but I have never been in that situation.
I would feel better if scripts were released, because I am fairly dubious. I take it as a given that a tabular model has been pre-trained on all of the public benchmark datasets, but that is what it is.
The slop was so meandering, I am not sure what is truth or not.
You otherwise get accustomed to some wishy-washy blobs of data that get passed around and find it normal. Maybe you include some ad hoc guard rails here and there, which catches the egregious errors, but there is always some lingering uncertainty. Some string that should have been an int, missing key here, the object which never had the validation check, etc
It is like unit-testing - more-up front work, but I could never go back to a world where I did not get these automated assurances. Sadly, I am a grug-brain which could only feel the lesson from personal experience.
Regardless, it still comes down to: things are bad and unaffordable. The powers that be keep claiming that AI is about to take our jobs and end the world. Why wouldn't people be mad?
No rain drop thinks it is responsible for the flood, but I am hard-pressed to imagine a blog article greatly swayed hearts and minds.
Some highlights:
Driven by record solar growth, low-carbon power generation increased by 887 TWh in 2025, outpacing electricity demand growth of 849 TWh. Solar power alone met 75% of the net increase in electricity demand. Together with wind, the two sources met almost all (99%) demand growth. For the first time since the Covid-19 pandemic in 2020, and only the fifth time this century, fossil generation did not rise, recording a small fall of 38 TWh (-0.2%).
The global fall in fossil generation was driven by a historic reversal in fossil trends in China and India, the largest and third-largest fossil power countries globally. 2025 was the first year this century when fossil generation fell in both countries. In China, it fell by 56 TWh (-0.9%), marking the first decline since 2015.
For the first time in 100 years, renewables (33.8%, 10,730 TWh) overtook coal power (33.0%, 10,476 TWh) in the global electricity mix as continued rapid growth in solar and wind pushed the share of renewables above a third of global generation. Coal power dropped 63 TWh (-0.6%) in 2025, marking the first fall since the Covid-19 pandemic in 2020. Combined with continued electricity demand growth, this meant coal fell below a third of global generation for the first time in history.
[0] https://ember-energy.org/latest-insights/global-electricity-...I was wondering how you could possibly sync up something like a Carrier that has multiple fighters.
Is it just that the providers are generating tons of synthetic datasets on coding tasks so that the models get more exposure to the right thing to do? Every time someone points out an LLM stupidity they add some training data to patch over the weakness (trivial to generate "there are two 'l's in llama")?
They were criminally under appreciated by management. Their career trajectory significantly worse than those who engaged in the highly visible projects.
Press article: https://www.livescience.com/secret-classified-satellite-trum...
Blog write up: https://sattrackcam.blogspot.com/2019/09/image-from-trump-tw...