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#hypothesis

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A hypothetical scenario behind the anti seed oil movement is that the proponents are being bought off by various #lobbies. This would be seen if the profit margins of non seed oils is higher than seed oils.

Some examples of a non seed oil come from the #dairy industry and it seems in general that the global market size of dairy is many folds greater than for seed oils. Additionally, it seems like the dairy industry gets also many folds more subsidies than the seed oils industry. These both statements would confirm the original #hypothesis.

Though if in return the #subsidies
for the seed oils industry would increase, therefore providing the dairy industry with cheaper material, then it would create an increased profit margin. Thus questioning the original hypothesis if it is financially driven.

Alternatively, one could still explain it away as financially driven with the idea of having money now over having potentially more money tomorrow.

My hypothesis is that people have been led to believe that having a lesser number of follows in ratio to followers is proof of success in social media, but this pruning view is detrimental to long term progress of a social media platform.

I would suggest that a follower's ability to be in synchronisation with another individual is momentary, and social media followers need to access more accounts to achieve beneficial harvesting. Allowing followed accounts to be off-topic is natural, and finding interest in their progress cannot always be satiated as a chronological dosage. Humans are occassion based, and this implies occasional interest and sometimes curiosity.

A few accounts may artificially maintain a follower's daily staple, but we should see their efforts as the exception, and not set that as a tide mark of any kind. Projecting daily staples as an expectation will lead to a greater losses in the social contract across each platform. A naturally compensated outcome is to perceive drifts and allow for chaotic reconnections. Hence, requiring a less tense stance on pruning in a collection of follows.

Heavy chronological demands are too closely related to dark patterns, and detrimental social media addiction. Exploring a greater group of follows inverts the expectations on the followed, and allows for a natural social contract to exist.

• It needs to be tempered with admission that chronological expectations are how some people push themselves to achieve anything at all.
• Not everyone uses fewer follows the same way, and some have a beneficial reason for pruning.

That's the gist!

Nie znoszę wielu rzeczy… a wśród nich znajduje się również paczka Pythona #Hypothesis.

Dlaczego? Bo naprawdę *mam już dość* padających "health checks" (czyli błędów typu "dane dla testów są generowane zbyt powoli"). jasne, takie informacje są przydatne autorom testów. Dla nas, dystrybucji — są bezużyteczne. Ja chcę wiedzieć, czy dana paczka działa. Nie obchodzi mnie, czy generowanie testów zajęło pół sekundy więcej niż autorzy Hypothesis uznają za stosowne, i nie mam czasu na to, żeby te problemy ciągle wszystkim zgłaszać, po to tylko, żeby te zgłoszenia były ignorowane, a testy dalej padały.

A najgorsze w tym wszystkim? Autorzy paczki Hypothesis mają to w rzyci. Nie chcą zaoferować nam możliwości wyłączenia tych testów. No tak, przecież możemy dodać inny "profil ustawień"! Tyle że wtedy musimy z osobna prosić autorów każdej paczki, która używa Hypothesis, albo sami je łatać. No dobra, alternatywnie możemy zupełnie wyłączyć testy używające Hypothesis — przecież to tylko fuzzing! Rzecz jasna, jest oczywiście sporo paczek, w których to jedyne testy dla danej funkcjonalności…

github.com/HypothesisWorks/hyp

GitHubPlease make it possible to disable health checks via pytest command-line args · Issue #2533 · HypothesisWorks/hypothesisBy mgorny

I guess I hate a lot of things… and among them, there's the #Python #Hypothesis package.

Why? Because *I am so tired* of failing health checks (e.g. errors because test data is generated "too slow"). Sure, these are useful to test authors. They are completely useless for downstream testing. I just want to know if the package works. I don't care if this time test generation randomly took half a second more than Hypothesis authors liked. I don't want random false positives all the time, and I don't have time to keep reporting them to countless upstreams, so that they just ignore my bug reports and tests still keep failing randomly for us.

And the worst part? Hypothesis upstream just doesn't care. They just don't want to add an override for downstream packagers. Oh yes, we can just add another settings profile! We just need to either talk every consumer of Hypothesis to provide one or patch every consumer. Or, well, they suggest just disabling tests using Hypothesis altogether — it's only fuzzing! If only people didn't rely on them as the only tests for given functionality…

github.com/HypothesisWorks/hyp

GitHubPlease make it possible to disable health checks via pytest command-line args · Issue #2533 · HypothesisWorks/hypothesisBy mgorny

Replacing #Omnivore seems more difficult than I thought:

I am looking for
* Collecting articles in the web, via RSS and ideally newsletters
* Reading and highlighting (on mobile - ideally also offline)
* Getting the highlights into #LogSeq

#Wallabag looks good but can't highlight on mobile devices

#Raindrop has a logseq integration but this project is discontinued

#Hypothesis does not work offline and does not have a RSS aggregator

None of these options is a really good replacement...

Replied in thread

@paninid p-values, to a large extent, exist because calculating the posterior is computationally expensive. Not all fields use the .05 cutoff.

A p-value is an #estimate of p(Data | Null Hypothesis). If the two #hypotheses are equally likely and they are mutually exclusive and they are closed over the #hypothesis space, then this is the same as p(Hypothesis | Data).

Meaning, under certain assumption, the p-value does represent the actually probability of being wrong.

However, given modern computers, there is no reason that #Bayesian odds-ratios can't completely replace their usage and avoid the many many problems with p-values.

A quotation from Feynman, Richard:

«
And so our kind of imagination is quite a difficult game. One has to have the imagination to think of something that has never been seen before, never been heard of before. At the same time the thoughts are restricted in a strait jacket, so to speak, limited by the conditions that come from our knowledge of the …
»

Full quote, sourcing, notes:
wist.info/feynman-richard/7175

WIST · The Feynman Lectures on Physics, Vol. 2, ch. 20 "Solutions of Maxwell's Equations in Free Space," sec. 20–3 "Scientific Imagination" (1964) - Feynman, Richard | WIST QuotationsAnd so our kind of imagination is quite a difficult game. One has to have the imagination to think of something that has never been seen before, never been heard of before. At the same time the thoughts are restricted in a strait jacket, so to speak, limited by the…