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.
There is a multitude of applications leveraging parts of the spectra different than the visible. I come from an agricultural background, and you can see examples from improving classification of land use, detection and classification of diseases, nutritional status assessment, indirect measurements of properties of plants and soil... it is endless, and every time any part of the tool stack gets cheaper, you have more and more potential applications.
This comment [1] have a nice description for the library.
If anything, OCaml's error messages are something that sucks, especially for newcomers. The `Error: Syntax Error` message that points to an empty last line in the file leaves you doing the parser work, in a language that you don't understand.
I'd guess that you can equate "screen time" to "smartphone use" in most of the population but here. Similar to what people are using the screen for.
To include so many variations of screen would complicate a lot the study, and without a prior about why it should matter, you are probably safer starting with something more homogeneous.
When I've dealt with R in production, cursed meant:
- Difficult to keep package versioning, even with "renv".
- If an analyst decide to use a single function from the "tidyverse", you have a tons of dependencies.
- Large docker images (1G+) due to packages like "devtools" and very large dependency tree for the "productivity packages" (see above).
- Hard to communicate with the process. With luck, you can set it up and work with 'r-script' [1]. Without luck, stdout from process or simple files for io.
In the end, to have a nice webapp, we ended up rewriting the R code into typescript. Julia don´t solve this also, as you have a hard time to set it up to communicate with other things. It seems that we can´t avoid the "2 or 3 languages" problem if you don´t use python.
I think that the the game culture have changed into something where the DM (dungeon master) is just a enforcer of rules/npc builder. Most of the arguments in the text should be discretionary to the DM. If a DM chooses to enforce a "medieval" setting, the campaign will be medieval. "Knights mentioned", "any time select a land", well, I guess the DM can mention knights and not treat land as something that can be bought as long as you have money. It was very different playing a campaign in Forgotten Realms, Dragonlance or Greyhawk, or having a custom world built by a DM.