Any sufficiently advanced disaster preparedness is indistinguishable from revolutionary dual power. This essay is a bit of a transition between the theory I've written earlier, and more concrete plans.
Even though I only touched on my life on the commune, it was hard not to write more. These are such weird spaces, with so much invisible opportunity. But they're also just so unique and special. For all the stress and uncertainty of making sure you stayed on Lorean's (the head priestess), there were also those long summer nights with the whole community (except the old lady) gathered around a fire, talking and drinking. There was almost a child-like play to the whole time.
There were so Fridays I'd come home with a couple of gallons of beer from the real world, folks would bring things from the garden, someone would grill a steak, everyone who didn't cook would clean up, and we'd just hang out and have fun. So many evenings I'd go over to Miles place with a guitar, or with his guitar, and we'd pass it around over a few beers, talking about philosophy, Star Wars, or some book or other. It's hard not to write about the strange magic of that space.
My partner and I bonded over similar experiences, mine on a weird little religious commune in California and theirs as a temporary worker at Omega Institute. Both had exploitation, people on weird power trips, frustrating dynamics, but also a strange magic and freedom. Both were sort of fantasy worlds, but places that let us see through this one, let us imagine something that something else is possible behind the veil.
There are many such veils.
Perhaps it's fitting that this is more meandering, as a good wander can help the transition between lots of hard thinking and lots of hard working.
https://anarchoccultism.org/building-zion/evaluating-options
Editing feedback (especially typos, spelling, grammar) is always welcome, as are questions and even wider structural advice. I've been adding the handles of folks who provide feedback to the intro in a "thank you" section. If you do help and wouldn't like to be added, please let me know.
This is a subtweet...
People who are not anti-capitalist sometimes wonder: "Why is there a monopoly on X life-critical thing?" (E.g., epipens, insulin, web search).
This one is really simple actually: because monopolies are more profitable than competition, and the foundation of capitalism is that capital = power.
Various societies have recognized the necropolitical outcomes of monopolies and have tried to erect barriers to monopoly; we all know that monopolies are bad, death-and-suffering-causing things. But since these societies mostly remain capitalist, they allow these barriers to be eroded by the power of capital (to do otherwise would be to repudiate capitalism because it puts a limit on the power of money). The barriers are ineffective, and the capital = power equation holds, and monopolies result and get to do their killing & maiming thing (remember: even things like social media monopolies that you wouldn't expect to pay for political assassinations like a mining company still profit from inciting genocides). *Sometimes* there are oligopolies instead of monopolies, but instances of really competitive markets are pretty rare for things that are widely sought-after.
The "government will manage the markets to prevent bad outcomes like monopolies" strategy has failed repeatedly, spectacularly, and almost universally. To actually prevent monopolies you need a population that no longer believes that money should equal power, it's that simple. Sadly, it's actually not that simple, since all of the alternatives which equate something else to power, like "the king" or "party loyalty as judged by the supreme leader" have the same problems or worse. The attitude you need to cultivate is "nobody should have power," which is hard because *all* of the power-systems we have constantly propagandize against this attitude in myriad ways. Still, in the future once we've broken free of this age where hierarchy is accepted, people will look back and wonder whether the historical records are even credible given how much needless death and suffering were endured with little resistance.
#anarchy #capitalism
Of course it's not only the money which is broken.
The music industry is also very crappy. Major pop stars rake in billions while most artists starve. There's only three record companies left, acting as gate keepers determining which sings get the payola to get radio play and Spotify basically gave up paying small artists in order to give Joe Rogan hundreds of millions of dollars.
Terrible situation.
So can bitcoin and lightning fix this?
Thus the after party here in Manchester.
Musicians and rappers at the event embrace V4V, value for value. Busking on the internet. Payment links on screen and on the live stream as they play.
Ainsley Costello tells us that her first song on fountain.fm made her a million sats, way more than any Spotify stream could make even if they still paid small artists.
She played us that song and then a whole range of artists took to the stage, live streamed over nostr, with donations coming in from all over the world.
All of them were talented and entertaining, but in particular Green Sands were tight and energetic and rocking, Edwin Williamson was deep and baritone and country, Roger 9000 really pumped the crowd with his bitcoin based songs and great tiny digital guitar and The Crypto raptor gets a special mention.
It was a really fun party, with musicians who all believe there is a better way than the terrible music industry.
Fast change overs and short sets means there were like ten acts in four hours among a friendly crowd in a dirty dive bar who all shared this common cause.
Full act list, all of whom are with checking out.
Ainsley Costello
The crypto raptor
Andy prince
Green sands
Edwin Williamson
Nathan abbot
G o l d
Longy
Roger 9000
Fable
#bitfest #music #v4v #bitcoin
Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot
I’ve seen a lot of posts from afar to the effect of “Why aren’t people in Minneapolis / the US doing anything?? When will they take action???”
…and, well, running with the metaphor from that thread, I guess maybe they’re looking at the top of the bread and wondering why it’s not golden brown already.
I want you to know, I want the world to know: never in my life, not even after the murder of George Floyd, have I seen the amount of sustained daily action I’ve seen here these last few weeks.
Not all flower visitors are equally helpful to the flower; separately tracking bees that buzz to release pollen and bees that steal pollen from flowers in a bunch of Chamaecrista species shows how tracking visitation alone would misrepresent the interactions
https://doi.org/10.1111/nph.70758
<…
Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot
Worked on some more #Gentoo global #jobserver goodies today.
Firstly, Portage jobserver support patch: #PyTest jobs will also be counted towards total job count.
Again, it's not a perfect solution, but it works reasonably. The plugin still starts -n jobs as specified by the arguments, but it acquired job tokens prior to executing every test, therefore delaying actual testing until tokens are available. It doesn't seem to cause noticeable overhead either.