9/
Table of #DOGE members.
(41 - 45) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
41) #KatieMiller, Spokesperson
42) #MichaelMinski, Liaison, #FHFA and #HUD
43) #BryanneMichelleMlodzianowski, Expert (HR), #OPM
44) #AramMoghaddassi, #USDT
45) #JustinMonroe, Expert (security), #FBI
8/
Table of #DOGE members.
(36 - 40) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
36) #ScottLangmack, Liaison, #HUD
37) #JeremyLewin, Liaison, #CFPB, #USAID, #DOS
38) #KendallLindemann, Expert (HR), #EOP, #USDS
39) #KathrynArmstrongLoving, Federal detailee, #EPA
40) #TarakMakecha, Finance executive, DOS
7/
Table of #DOGE members.
(31 - 35) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
31) #ColeKillian, Federal Detailee, #EPA
32) #GavinKliger, Senior advisor to the Director, #CFPB, #OPM, #USAID, #IRS
33) #KeenanKmiec, Lawyer, #EOP
34) #TomKrause, Fiscal Assistant Secretary of the Treasury, #USDT
35) #ScottKupor, Director, OPM
6/
Table of #DOGE members.
(26 - 30) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
26) #TylerHassen, Liaison, #USBR
27) #GregHogan, Chief Information Officer, #OPM
28) #NicoleHollander, #GSA
29) #StephanieHolmes, Head of human resources
30) #AnthonyJancso, Recruiter
#Congratulations to #Wikipedia for breaking their interface with whatever update they've just deployed.
In Firefox, the layout for tables goes wider than the viewport, but there is no way to scroll horizontally, so half the content is impossible to see.
It’d be interesting to do an audit of all the deleted American government pages and see which of them can/should/don’t have #Wikipedia articles. Is anyone doing that yet?
https://glammr.us/@jessamyn/114167314994107129
"Anyone at an AI company who stops to think for half a second should be able to recognize they have a vampiric relationship with the commons. While they rely on these repositories for their sustenance, their adversarial and disrespectful relationships with creators reduce the incentives for anyone to make their work publicly available going forward (freely licensed or otherwise). They drain resources from maintainers of those common repositories often without any compensation. They reduce the visibility of the original sources, leaving people unaware that they can or should contribute towards maintaining such valuable projects. AI companies should want a thriving open access ecosystem, ensuring that the models they trained on Wikipedia in 2020 can be continually expanded and updated. Even if AI companies don’t care about the benefit to the common good, it shouldn’t be hard for them to understand that by bleeding these projects dry, they are destroying their own food supply.
And yet many AI companies seem to give very little thought to this, seemingly looking only at the months in front of them rather than operating on years-long timescales. (Though perhaps anyone who has observed AI companies’ activities more generally will be unsurprised to see that they do not act as though they believe their businesses will be sustainable on the order of years.)
It would be very wise for these companies to immediately begin prioritizing the ongoing health of the commons, so that they do not wind up strangling their golden goose. It would also be very wise for the rest of us to not rely on AI companies to suddenly, miraculously come to their senses or develop a conscience en masse.
Instead, we must ensure that mechanisms are in place to force AI companies to engage with these repositories on their creators' terms."
https://www.citationneeded.news/free-and-open-access-in-the-age-of-generative-ai/
5/
Table of #DOGE members.
(21 - 25) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
21) #JustinFulcher, Liaison, #VA
22) #JoeGebbia
23) #AmyGleason, Acting Administrator of #USDS and #USDSTO, USDS
24) #AntonioGracias, Musk ally
25) #MichaelGrimes, #DOC
Grouping Concepts Temporally Instead of Topically, Using Wikipedia Data
There's been so much talk about "vibes" in programming lately that it gave me an idea for a Wikipedia tool. If you accept the idea that Wikipedia page view data can be used as "fossilized attention" (indicators of public interest in a topic) then you can use information extracted from page view data -- like derivation from average views (z-score) -- as a benchmark of public interest.
4/
Table of #DOGE members.
(16 - 20) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
16) #StephenDuarte, Expert (HR), #OPM
17) #LelandDudek, Acting Commissioner, #SSA
18) #StephenEhikian, Acting Administrator, #GSA
19) #MarkoElez, Expert (coding), #USDT
20) #LukeFarritor, Executive Engineer in the Office of the Secretary, #HHS, #DOS
3/
Table of #DOGE members.
(11 - 15) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
11) #NateCavanaugh, #GSA
12) #GeorgeCooper, Recruiter
13) #SamCorcos, #IRS
14) #EdwardCoristine, Expert (coding), #DT, #OPM, #DOS, #DHS, #FEMA, #USAID
15) #SteveDavis, Musk second-in-command, #EOP, OPM, GSA
The future of free and open access isn't about saying “wait, not like that” — it’s about saying "yes, like that, but under fair terms”.
It would be wise for these companies to begin prioritizing the ongoing health of the commons. It would also be wise for the rest of us to not rely on AI companies to come to their senses or develop a conscience en masse, and instead force them to engage on creators’ terms.
Anyone at an AI company who stops to think for half a second should be able to recognize they have a vampiric relationship with the commons. By operating in this purely extractive way, they destroy the things that underpin their businesses.
Instead of worrying about “wait, not like that”, I think we need to reframe the conversation to “wait, not only like that” or “wait, not in ways that threaten open access itself”.
But if we want to create a world in which every single human being can freely share in the sum of all knowledge, we should stop attempting to wall off the commons.
The impulse is often to tighten the licenses, or stop publishing under free licenses (if at all). But this threatens to destroy the very commons we set out to build.
The free and open access vision is inspiring, but there are instances in which people who freely license their work go “wait, no, not like that”. People reselling their free works, tech companies profiting off their FOSS code, and AI companies training on their material.
Newsletter: The real threat isn't AI using open knowledge — it's AI companies killing the projects that make knowledge free. The future of free and open access isn't saying “wait, not like that” — it’s saying "yes, like that, but under fair terms”.
https://www.citationneeded.news/free-and-open-access-in-the-age-of-generative-ai/
2/
Table of #DOGE members.
(06 - 10) (of 68)
From #Wikipedia: https://en.wikipedia.org/wiki/Department_of_Government_Efficiency#DOGE_members
06) #AlexandraBeynon, Expert (coding), #ED
07) #RiccardoBiasini, Senior Advisor to the Director, #OPM
08) #BrianBjelde, Senior Advisor, OPM
09) #AkashBobba, Expert (coding), OPM, #GSA
10) #JamesBurnham, General Counsel, #EOP
waiting for summer 
