Veganism Social

dragotinWe would highly appreciate a little help from Linux <a href="https://indieweb.social/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> to provide the best desktop integration for <a href="https://indieweb.social/tags/OpenCloud" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenCloud</a> users. Please add a few low maintenance effort packages to the core repos. More on that here: <a href="https://opencloud.eu/en/news/linux-packaging-opencloud-desktop-app" rel="nofollow noopener" translate="no" target="_blank">https://opencloud.eu/en/news/linux-packaging-opencloud-desktop-app</a>

Soh Kam YungArticle from <a href="https://fedi.lwn.net/@lwn" class="u-url mention" rel="nofollow noopener" target="_blank">@lwn</a> "The first honest-to-goodness distribution with a proper installer was MCC Interim Linux, created by Owen Le Blanc, released publicly in early 1992. I recently reached out to Le Blanc to learn more about his work on the distribution, what he has been doing since, and his thoughts on Linux in 2025."<a href="https://lwn.net/Articles/1017846/" rel="nofollow noopener" translate="no" target="_blank">https://lwn.net/Articles/1017846/</a><a href="https://mstdn.io/tags/Computers" class="mention hashtag" rel="nofollow noopener" target="_blank">#Computers</a> <a href="https://mstdn.io/tags/Linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#Linux</a> <a href="https://mstdn.io/tags/LinuxHistory" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinuxHistory</a> <a href="https://mstdn.io/tags/Distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#Distributions</a> <a href="https://mstdn.io/tags/Software" class="mention hashtag" rel="nofollow noopener" target="_blank">#Software</a>

Eric Maugendre<a href="https://ieji.de/@Lu" class="u-url mention" rel="nofollow noopener" target="_blank">@Lu</a> Thank you for the pointer! Looking at the evolution i feel that users act more and more on their dissatisfaction with <a href="https://hachyderm.io/tags/CinnamonDesktop" class="mention hashtag" rel="nofollow noopener" target="_blank">#CinnamonDesktop</a>. I note that: * the number of downloads of <a href="https://hachyderm.io/tags/MATEDesktop" class="mention hashtag" rel="nofollow noopener" target="_blank">#MATEDesktop</a> increased 50% in a year; * the number of downloads of <a href="https://hachyderm.io/tags/EDGEDesktop" class="mention hashtag" rel="nofollow noopener" target="_blank">#EDGEDesktop</a> decreased 50% in a year.<a href="https://hachyderm.io/tags/desktopEnvironments" class="mention hashtag" rel="nofollow noopener" target="_blank">#desktopEnvironments</a> <a href="https://hachyderm.io/tags/desktopEnvironment" class="mention hashtag" rel="nofollow noopener" target="_blank">#desktopEnvironment</a> <a href="https://hachyderm.io/tags/distros" class="mention hashtag" rel="nofollow noopener" target="_blank">#distros</a> <a href="https://hachyderm.io/tags/Linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#Linux</a> <a href="https://hachyderm.io/tags/LinuxDistributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinuxDistributions</a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a>

Miki :rstats:<a href="https://techhub.social/tags/30DayChartChallenge" class="mention hashtag" rel="nofollow noopener" target="_blank">#30DayChartChallenge</a> Día 12: Gov Data Day! 🏛️ Explorando la distribución del spread 10Y-2Y del Tesoro USA (datos de FRED desde 1976).Este histograma/densidad va más allá del valor diario: muestra la *probabilidad* histórica de cada nivel del spread. ¡Clave para entender expectativas económicas!Puntos clave: * Modo principal > 0 (curva normal es lo más común). * ¡La inversión (<0, línea discontinua) tiene una probabilidad no trivial! ⚠️ Es la famosa señal pre-recesión. La distribución nos dice cuán "normal" es esa señal en perspectiva histórica. * La forma general revela info sobre la dinámica de tipos.Una visualización sobre la estructura probabilística de un indicador líder fundamental.🛠️ <a href="https://techhub.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> <a href="https://techhub.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#ggplot2</a> <a href="https://techhub.social/tags/quantmod" class="mention hashtag" rel="nofollow noopener" target="_blank">#quantmod</a> <a href="https://techhub.social/tags/grid" class="mention hashtag" rel="nofollow noopener" target="_blank">#grid</a> 📂 Código/Repo: <a href="https://t.ly/0RDmK" rel="nofollow noopener" translate="no" target="_blank">https://t.ly/0RDmK</a><a href="https://techhub.social/tags/Day12" class="mention hashtag" rel="nofollow noopener" target="_blank">#Day12</a> <a href="https://techhub.social/tags/Distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#Distributions</a> <a href="https://techhub.social/tags/datagov" class="mention hashtag" rel="nofollow noopener" target="_blank">#datagov</a> <a href="https://techhub.social/tags/dataviz" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataviz</a> <a href="https://techhub.social/tags/DataVisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataVisualization</a> <a href="https://techhub.social/tags/YieldCurve" class="mention hashtag" rel="nofollow noopener" target="_blank">#YieldCurve</a> <a href="https://techhub.social/tags/InterestRates" class="mention hashtag" rel="nofollow noopener" target="_blank">#InterestRates</a> <a href="https://techhub.social/tags/Economics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Economics</a> <a href="https://techhub.social/tags/Finance" class="mention hashtag" rel="nofollow noopener" target="_blank">#Finance</a> <a href="https://techhub.social/tags/Recession" class="mention hashtag" rel="nofollow noopener" target="_blank">#Recession</a> <a href="https://techhub.social/tags/DataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataAnalysis</a> <a href="https://techhub.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#ggplot2</a>

LinuxNews.deParticleOS – ein systemd-Betriebssystem <a href="https://linuxnews.de/particleos-ein-systemd-betriebssystem/" rel="nofollow noopener" translate="no" target="_blank">https://linuxnews.de/particleos-ein-systemd-betriebssystem/</a> <a href="https://social.anoxinon.de/tags/systemd" class="mention hashtag" rel="nofollow noopener" target="_blank">#systemd</a> <a href="https://social.anoxinon.de/tags/poettering" class="mention hashtag" rel="nofollow noopener" target="_blank">#poettering</a> <a href="https://social.anoxinon.de/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> <a href="https://social.anoxinon.de/tags/linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#linux</a>

Thor A. HoplandIt there's one thing I know, it's that <a href="https://snabelen.no/tags/linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#linux</a> <a href="https://snabelen.no/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> that are community driven turn out to be the most reliable. My current go to for regular users is <a href="https://snabelen.no/tags/Fedora" class="mention hashtag" rel="nofollow noopener" target="_blank">#Fedora</a>, because it is a solid distribution that's cutting edge and it's designed by the community. I can't say the same for <a href="https://snabelen.no/tags/NixOS" class="mention hashtag" rel="nofollow noopener" target="_blank">#NixOS</a>. The amount of internal fragmentation and in fighting is too damned high, and there is possible conflict of interest that sits like an elephant in the room. So I'm thinking... I might make a switch soon.

TUXEDO<a href="https://tilvids.com/video-channels/thelinuxexperiment_channel" class="u-url mention" rel="nofollow noopener" target="_blank">@thelinuxexperiment_channel</a> Linux Experiment had asked you, what's your favorite Distros? You've also voted for TUXEDO OS. 🤗<a href="https://tilvids.com/w/f5955f2e-f110-42bf-a301-8d3a75549e33" rel="nofollow noopener" translate="no" target="_blank">https://tilvids.com/w/f5955f2e-f110-42bf-a301-8d3a75549e33</a><a href="https://linuxrocks.online/tags/tuxedo" class="mention hashtag" rel="nofollow noopener" target="_blank">#tuxedo</a> <a href="https://linuxrocks.online/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> <a href="https://linuxrocks.online/tags/linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#linux</a>

Eric Maugendre<a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@data</a> <a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@datadon</a> 🧵Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling. Regression Redress restrains bias by segregating the residual values. My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener" translate="no" target="_blank">http://data.yt/kit/regression-redress.html</a><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataDev</a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIDev</a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelEvaluation</a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelling</a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataLearning</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearRegression</a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#probability</a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#probabilities</a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#stats</a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#correctionRatio</a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#accuracy</a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#RegressionRedress</a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#RStats</a>

Eric Maugendre<a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@data</a> <a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@datadon</a> 🧵How to assess a statistical model? How to choose between variables?Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#correlation</a> is irrelevant if you suspect that the relationship is not a straight line.If monotonic relationship: "<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener" target="_blank">#Spearman</a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed". "<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener" target="_blank">#Kendall</a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values." Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener" translate="no" target="_blank">https://statisticseasily.com/kendall-tau-b-vs-spearman/</a><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener" target="_blank">#normality</a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#normalDistribution</a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataDev</a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIDev</a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelEvaluation</a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelling</a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataLearning</a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#featureEngineering</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearRegression</a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#probability</a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#probabilities</a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#stats</a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#correctionRatio</a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener" target="_blank">#Pearson</a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#regressionRedress</a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a>

David J. Atkinson<a href="https://saturation.social/@clive" class="u-url mention" rel="nofollow noopener" target="_blank">@clive</a> This suggests that one could detect <a href="https://c.im/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> written material by comparing the word distribution to that of human writing, assuming a large enough sample. It may also be true that the word distributions from various <a href="https://c.im/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> models are different from each, in which case you now have an AI fingerprint. Comparing multiple <a href="https://c.im/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> is relatively straightforward with <a href="https://c.im/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a>.

Eric MaugendreIn 2016, the American Statistical Association <a href="https://hachyderm.io/tags/ASA" class="mention hashtag" rel="nofollow noopener" target="_blank">#ASA</a> made a formal statement that "a p-value, or statistical significance, does not measure the size of an effect or the importance of a result".It also stated that "p-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone".<a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#nullHypothesis</a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#probabilities</a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#probability</a> <a href="https://hachyderm.io/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#maths</a> <a href="https://hachyderm.io/tags/mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#mathematics</a> <a href="https://hachyderm.io/tags/vectors" class="mention hashtag" rel="nofollow noopener" target="_blank">#vectors</a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#data</a> <a href="https://hachyderm.io/tags/bigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#bigData</a> <a href="https://hachyderm.io/tags/matrices" class="mention hashtag" rel="nofollow noopener" target="_blank">#matrices</a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#distributions</a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#stats</a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a>

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