It is interesting that they are paying attention to that, as the section on interactivity [1] brings "how to disable a cell" that would prevent its reactivity and anything that depends upon. With some careful placement, you can disable the automatic calculations. Another thing would be tuning the parameter for confirming before a long runtime. Set it to 50 ms, and only run "almost instant things".
There is a lot of "throw it against the wall, and if it sticks, write it up" empirical work against benchmarks. It leads to post-hoc rationalization of the work and browser plugins using LLMs to find references for work that is already written.
It is a bureaucratic view about "you need a citation for this", where people misunderstand the citation as a checkbox, instead of "you need to substantiate this claim, as I, the reviewer, do not accept this as a fact".
The estimated area of no-till in Brazil is between 33 to 50 million hectares. It won't be hard for you to find videos of no-till corn being planted following soybean. There is also grass cover that is planted after the main crop season, that is later grazed. This cover stays till the next year and the new crop is planted without tilling. You may need to use "plantio direto" "milho safrinha" and "braquiária de cobertura" plus some translation.
llama models pushed the envelope for a while, and having them "open-weight" allowed a lot of tinkering. I would say that most of fine tuned evolved from work on top of llama models.
longer answer: Random forests use the average of multiple trees that are trained in a way to reduce the correlation between trees (bagging with modified trees). Boosting trains sequentially, with each classifier working on the resulting residuals so far.
I am assuming that you meant boosted decision trees, sometimes gradient boosted decisions trees, as usually one have boosted decision trees. I think xgboost added boosted RF, and you can boost any supervised model, but it is not usual.
You can see aggregated results on `stats` [1] for every year. In general, half the people drop in the first 3-4 days. For last year, by day 12 there is less than 1/5 of day 01. While the stats do count people that completed later, the shape appears to track well with what I saw during the events since 2021.
I am not aware of Eric saying something about that alternative, but this comment on reddit[1] makes a lot of sense to me:
> Given that part 2 is often a very simple modification of part 1, this could lead to many of the days being total letdowns. I can enjoy a simple puzzle, but I'd be a bit disappointed if one day is a single line change to the previous day.
I'd also add that not having to be worried everyday about something makes a lot of sense. He can have fewer days "on call" in December with.
You could think about how most people can get away without doing anything physical to survive, so we must artificially exercise to be healthy. The question then is if this analogy hold for mental capacities, and I think it does.
[1]https://discourse.julialang.org/t/pluto-1-0-release/137296#p...