#distributedcomputing — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #distributedcomputing, aggregated by home.social.
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Improving GPU Energy Efficiency With Component-Level Power Management (AMD) https://www.byteseu.com/2052270/ #AMD #ComponentLevelPowerManagement #CompPow #DistributedComputing #Energy #FineGrainPowerAnalysis #GPUEnergyEfficiency #GPUPerformance #GPUPowerManagement #GPUs #HardwareArchitecture #MachineLearning #MLDatacenters #PowerManagement #Software/hardwareCoDesign
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#Decentralization #Decentralized #Web3 #Fediverse #Mastodon #Nostr #ATProtocol #Bluesky #ActivityPub #MeshNetworks #Yggdrasil #CJDNS #Hyperboria #DistributedSystems #OpenSource #SelfHosting #DigitalFreedom #Privacy #Cybersecurity #IPFS #Filecoin #Arweave #DistributedComputing #Bittensor #AkashNetwork #GolemNetwork #P2P #AlternativeInternet #FutureTech #FreeSoftware
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#Decentralization #Decentralized #Web3 #Fediverse #Mastodon #Nostr #ATProtocol #Bluesky #ActivityPub #MeshNetworks #Yggdrasil #CJDNS #Hyperboria #DistributedSystems #OpenSource #SelfHosting #DigitalFreedom #Privacy #Cybersecurity #IPFS #Filecoin #Arweave #DistributedComputing #Bittensor #AkashNetwork #GolemNetwork #P2P #AlternativeInternet #FutureTech #FreeSoftware
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An 8 years old bug is now finally addressed and fixed in #BOINC client. Now BOINC client can properly detect idle time and run the processes when the computer is idle (if user have configured it that way). More info:
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An 8 years old bug is now finally addressed and fixed in #BOINC client. Now BOINC client can properly detect idle time and run the processes when the computer is idle (if user have configured it that way). More info:
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An 8 years old bug is now finally addressed and fixed in #BOINC client. Now BOINC client can properly detect idle time and run the processes when the computer is idle (if user have configured it that way). More info:
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An 8 years old bug is now finally addressed and fixed in #BOINC client. Now BOINC client can properly detect idle time and run the processes when the computer is idle (if user have configured it that way). More info:
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An 8 years old bug is now finally addressed and fixed in #BOINC client. Now BOINC client can properly detect idle time and run the processes when the computer is idle (if user have configured it that way). More info:
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Is the #BOINC #DistributedComputing project sadly gradually becoming obsolete or dying? #WCGrid nowadays seems to spend almost as much time down as up under its not-so-new caretakers, I very very rarely get any work from #ClimatePrediction, #RosettaAtHome also seems to have fairly frequent server errors, and most of the rest seem to be literal number-crunching that I don't want to expend electricity on. Have #GPU nodes mostly taken over for this sort of scientific research now?
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Is the #BOINC #DistributedComputing project sadly gradually becoming obsolete or dying? #WCGrid nowadays seems to spend almost as much time down as up under its not-so-new caretakers, I very very rarely get any work from #ClimatePrediction, #RosettaAtHome also seems to have fairly frequent server errors, and most of the rest seem to be literal number-crunching that I don't want to expend electricity on. Have #GPU nodes mostly taken over for this sort of scientific research now?
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Is the #BOINC #DistributedComputing project sadly gradually becoming obsolete or dying? #WCGrid nowadays seems to spend almost as much time down as up under its not-so-new caretakers, I very very rarely get any work from #ClimatePrediction, #RosettaAtHome also seems to have fairly frequent server errors, and most of the rest seem to be literal number-crunching that I don't want to expend electricity on. Have #GPU nodes mostly taken over for this sort of scientific research now?
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New post about #BOINC release process: https://aenbleidd.github.io/2026.03.26.html
#DistributedComputing #VolunteerComputing #GridComputing #CitizenScience
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Every night for years I plugged my phone in and let it search for alien life.
Last month I found out that my efforts might have helped us locate life on other planets. From 14 billion possibilities, we now have 100 genuine leads to follow up on.
This piece is not about aliens, but about how we collectively did things greater than ourselves. And how we are dismantling the very infrastructure that made it possible.
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Every night for years I plugged my phone in and let it search for alien life.
Last month I found out that my efforts might have helped us locate life on other planets. From 14 billion possibilities, we now have 100 genuine leads to follow up on.
This piece is not about aliens, but about how we collectively did things greater than ourselves. And how we are dismantling the very infrastructure that made it possible.
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Every night for years I plugged my phone in and let it search for alien life.
Last month I found out that my efforts might have helped us locate life on other planets. From 14 billion possibilities, we now have 100 genuine leads to follow up on.
This piece is not about aliens, but about how we collectively did things greater than ourselves. And how we are dismantling the very infrastructure that made it possible.
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RPC is a mechanism for structuring distributed systems, not a transport protocol. The calling program blocks until the remote procedure returns — reproducing local call semantics across a network to simplify distributed application development.
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RPC is a mechanism for structuring distributed systems, not a transport protocol. The calling program blocks until the remote procedure returns — reproducing local call semantics across a network to simplify distributed application development.
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RPC is a mechanism for structuring distributed systems, not a transport protocol. The calling program blocks until the remote procedure returns — reproducing local call semantics across a network to simplify distributed application development.
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RPC is a mechanism for structuring distributed systems, not a transport protocol. The calling program blocks until the remote procedure returns — reproducing local call semantics across a network to simplify distributed application development.
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Fabric – mạng tính toán phân tán cho phép laptop cho thuê tài nguyên idle cho nhà phát triển & nhà nghiên cứu. Sau ~43k lượt xem, tác giả đã redesign UI/UX, thêm xác thực email, cải thiện an toàn & minh bạch. Hiện có ~200 nhà cung cấp thiết bị, 15 dự án dùng. Cần phản hồi: Bạn hiểu rõ Fabric chưa? Rào cản khi cài đặt? Trang an toàn có hợp lý? So sánh với Google Colab? Cần thêm tài liệu, benchmark? #Fabric #DistributedComputing #Tech #CôngNghệ #Feedback #MạngTínhToán #OpenSource
https://www.redd
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SETI@Home Has Finally Been Completed After 27 Years, Here's What Was Found
https://www.youtube.com/watch?v=Gt07R_amRT8
https://setiathome.berkeley.edu/#SETI #SETIAtHome #BOINC #DistributedComputing #Science #Space #Telescope #Arecibo #Aliens
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SETI@Home Has Finally Been Completed After 27 Years, Here's What Was Found
https://www.youtube.com/watch?v=Gt07R_amRT8
https://setiathome.berkeley.edu/#SETI #SETIAtHome #BOINC #DistributedComputing #Science #Space #Telescope #Arecibo #Aliens
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SETI@Home Has Finally Been Completed After 27 Years, Here's What Was Found
https://www.youtube.com/watch?v=Gt07R_amRT8
https://setiathome.berkeley.edu/#SETI #SETIAtHome #BOINC #DistributedComputing #Science #Space #Telescope #Arecibo #Aliens
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SETI@Home Has Finally Been Completed After 27 Years, Here's What Was Found
https://www.youtube.com/watch?v=Gt07R_amRT8
https://setiathome.berkeley.edu/#SETI #SETIAtHome #BOINC #DistributedComputing #Science #Space #Telescope #Arecibo #Aliens
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SETI@Home Has Finally Been Completed After 27 Years, Here's What Was Found
https://www.youtube.com/watch?v=Gt07R_amRT8
https://setiathome.berkeley.edu/#SETI #SETIAtHome #BOINC #DistributedComputing #Science #Space #Telescope #Arecibo #Aliens
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Một dự án mới cho phép chạy các mô hình AI lớn trên nhiều máy tính chỉ qua mạng WiFi. Có thể kết hợp phẩn cứng khác nhau như Apple Silicon, NVIDIA, CPU. Một hướng đi thú vị cho AI tại nhà.
#AI #MôHìnhNgônNgữLớn #LLM #MachineLearning #MáyHọc #Tech #CôngNghệ #DistributedComputing #VietNam
https://www.reddit.com/r/LocalLLaMA/comments/1qha0kd/run_large_models_across_multiple_machines_over/
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I had that running on my home and work computer BITD!
https://phys.org/news/2026-01-enthusiasts-home-scientists-homing.html
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I had that running on my home and work computer BITD!
https://phys.org/news/2026-01-enthusiasts-home-scientists-homing.html
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I had that running on my home and work computer BITD!
https://phys.org/news/2026-01-enthusiasts-home-scientists-homing.html
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I had that running on my home and work computer BITD!
https://phys.org/news/2026-01-enthusiasts-home-scientists-homing.html
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I had that running on my home and work computer BITD!
https://phys.org/news/2026-01-enthusiasts-home-scientists-homing.html
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Discord's ML Scaling Breakthrough https://techlife.blog/posts/how-discord-ml-hit-its-scaling-limit/
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Discord's ML Scaling Breakthrough https://techlife.blog/posts/how-discord-ml-hit-its-scaling-limit/
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Người dùng thảo luận về việc "nối chuỗi" nhiều Mac Mini giá rẻ cho tác vụ AI/LLM, thay vì mua một chiếc đắt tiền với cấu hình nâng cấp. Mặc dù tiết kiệm chi phí ban đầu, thách thức lớn là khả năng hỗ trợ phần mềm phân tán như Ollama, vLLM trên nền tảng Metal.
#MacMini #AI #LLM #DistributedComputing #Ollama #vLLM #ĐiệnToánPhânTán
https://www.reddit.com/r/LocalLLaMA/comments/1p90pkl/daisy_chaining_macminis/
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A new article in Cloud Native Now highlights how pgEdge is enabling distributed #PostgreSQL across multiple #Kubernetes clusters — bringing global scale, high availability, and true cloud-native resilience to #Postgres.
It’s another step forward in simplifying how organizations run Postgres at scale — fully open source, multi-cloud, and Kubernetes-native. 🌍
📰 Read the full feature on Cloud Native Now: https://cloudnativenow.com/features/pgedge-adds-ability-to-distribute-postgres-across-multiple-kubernetes-clusters/
#programming #cloudcomputing #k8s #devops #distributedcomputing
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A new article in Cloud Native Now highlights how pgEdge is enabling distributed #PostgreSQL across multiple #Kubernetes clusters — bringing global scale, high availability, and true cloud-native resilience to #Postgres.
It’s another step forward in simplifying how organizations run Postgres at scale — fully open source, multi-cloud, and Kubernetes-native. 🌍
📰 Read the full feature on Cloud Native Now: https://cloudnativenow.com/features/pgedge-adds-ability-to-distribute-postgres-across-multiple-kubernetes-clusters/
#programming #cloudcomputing #k8s #devops #distributedcomputing
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A new article in Cloud Native Now highlights how pgEdge is enabling distributed #PostgreSQL across multiple #Kubernetes clusters — bringing global scale, high availability, and true cloud-native resilience to #Postgres.
It’s another step forward in simplifying how organizations run Postgres at scale — fully open source, multi-cloud, and Kubernetes-native. 🌍
📰 Read the full feature on Cloud Native Now: https://cloudnativenow.com/features/pgedge-adds-ability-to-distribute-postgres-across-multiple-kubernetes-clusters/
#programming #cloudcomputing #k8s #devops #distributedcomputing
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A new article in Cloud Native Now highlights how pgEdge is enabling distributed #PostgreSQL across multiple #Kubernetes clusters — bringing global scale, high availability, and true cloud-native resilience to #Postgres.
It’s another step forward in simplifying how organizations run Postgres at scale — fully open source, multi-cloud, and Kubernetes-native. 🌍
📰 Read the full feature on Cloud Native Now: https://cloudnativenow.com/features/pgedge-adds-ability-to-distribute-postgres-across-multiple-kubernetes-clusters/
#programming #cloudcomputing #k8s #devops #distributedcomputing
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A new article in Cloud Native Now highlights how pgEdge is enabling distributed #PostgreSQL across multiple #Kubernetes clusters — bringing global scale, high availability, and true cloud-native resilience to #Postgres.
It’s another step forward in simplifying how organizations run Postgres at scale — fully open source, multi-cloud, and Kubernetes-native. 🌍
📰 Read the full feature on Cloud Native Now: https://cloudnativenow.com/features/pgedge-adds-ability-to-distribute-postgres-across-multiple-kubernetes-clusters/
#programming #cloudcomputing #k8s #devops #distributedcomputing
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Today I introduced a much-needed feature to #GPUSPH.
Our code supports multi-GPU and even multi-node, so in general if you have a large simulation you'll want to distribute it over all your GPUs using our internal support for it.
However, in some cases, you need to run a battery of simulations and your problem size isn't large enough to justify the use of more than a couple of GPUs for each simulation.
In this case, rather than running the simulations in your set serially (one after the other) using all GPUs for each, you'll want to run them in parallel, potentially even each on a single GPUs.
The idea is to find the next avaialble (set of) GPU(s) and launch a simulation on them while there are still available sets, then wait until a “slot” frees up and start the new one(s) as slots get freed.
Until now, we've been doing this manually by partitioning the set of simulations to do and start them in different shells.
There is actually a very powerful tool to achieve this on the command, line, GNU Parallel. As with all powerful tools, however, this is somewhat cumbersome to configure to get the intended result. And after Doing It Right™ one must remember the invocation magic …
So today I found some time to write a wrapper around GNU Parallel that basically (1) enumerates the available GPUs and (2) appends the appropriate --device command-line option to the invocation of GPUSPH, based on the slot number.
#GPGPU #ParallelComputing #DistributedComputing #GNUParallel
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Today I introduced a much-needed feature to #GPUSPH.
Our code supports multi-GPU and even multi-node, so in general if you have a large simulation you'll want to distribute it over all your GPUs using our internal support for it.
However, in some cases, you need to run a battery of simulations and your problem size isn't large enough to justify the use of more than a couple of GPUs for each simulation.
In this case, rather than running the simulations in your set serially (one after the other) using all GPUs for each, you'll want to run them in parallel, potentially even each on a single GPUs.
The idea is to find the next avaialble (set of) GPU(s) and launch a simulation on them while there are still available sets, then wait until a “slot” frees up and start the new one(s) as slots get freed.
Until now, we've been doing this manually by partitioning the set of simulations to do and start them in different shells.
There is actually a very powerful tool to achieve this on the command, line, GNU Parallel. As with all powerful tools, however, this is somewhat cumbersome to configure to get the intended result. And after Doing It Right™ one must remember the invocation magic …
So today I found some time to write a wrapper around GNU Parallel that basically (1) enumerates the available GPUs and (2) appends the appropriate --device command-line option to the invocation of GPUSPH, based on the slot number.
#GPGPU #ParallelComputing #DistributedComputing #GNUParallel
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Today I introduced a much-needed feature to #GPUSPH.
Our code supports multi-GPU and even multi-node, so in general if you have a large simulation you'll want to distribute it over all your GPUs using our internal support for it.
However, in some cases, you need to run a battery of simulations and your problem size isn't large enough to justify the use of more than a couple of GPUs for each simulation.
In this case, rather than running the simulations in your set serially (one after the other) using all GPUs for each, you'll want to run them in parallel, potentially even each on a single GPUs.
The idea is to find the next avaialble (set of) GPU(s) and launch a simulation on them while there are still available sets, then wait until a “slot” frees up and start the new one(s) as slots get freed.
Until now, we've been doing this manually by partitioning the set of simulations to do and start them in different shells.
There is actually a very powerful tool to achieve this on the command, line, GNU Parallel. As with all powerful tools, however, this is somewhat cumbersome to configure to get the intended result. And after Doing It Right™ one must remember the invocation magic …
So today I found some time to write a wrapper around GNU Parallel that basically (1) enumerates the available GPUs and (2) appends the appropriate --device command-line option to the invocation of GPUSPH, based on the slot number.
#GPGPU #ParallelComputing #DistributedComputing #GNUParallel
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Today I introduced a much-needed feature to #GPUSPH.
Our code supports multi-GPU and even multi-node, so in general if you have a large simulation you'll want to distribute it over all your GPUs using our internal support for it.
However, in some cases, you need to run a battery of simulations and your problem size isn't large enough to justify the use of more than a couple of GPUs for each simulation.
In this case, rather than running the simulations in your set serially (one after the other) using all GPUs for each, you'll want to run them in parallel, potentially even each on a single GPUs.
The idea is to find the next avaialble (set of) GPU(s) and launch a simulation on them while there are still available sets, then wait until a “slot” frees up and start the new one(s) as slots get freed.
Until now, we've been doing this manually by partitioning the set of simulations to do and start them in different shells.
There is actually a very powerful tool to achieve this on the command, line, GNU Parallel. As with all powerful tools, however, this is somewhat cumbersome to configure to get the intended result. And after Doing It Right™ one must remember the invocation magic …
So today I found some time to write a wrapper around GNU Parallel that basically (1) enumerates the available GPUs and (2) appends the appropriate --device command-line option to the invocation of GPUSPH, based on the slot number.
#GPGPU #ParallelComputing #DistributedComputing #GNUParallel
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Today I introduced a much-needed feature to #GPUSPH.
Our code supports multi-GPU and even multi-node, so in general if you have a large simulation you'll want to distribute it over all your GPUs using our internal support for it.
However, in some cases, you need to run a battery of simulations and your problem size isn't large enough to justify the use of more than a couple of GPUs for each simulation.
In this case, rather than running the simulations in your set serially (one after the other) using all GPUs for each, you'll want to run them in parallel, potentially even each on a single GPUs.
The idea is to find the next avaialble (set of) GPU(s) and launch a simulation on them while there are still available sets, then wait until a “slot” frees up and start the new one(s) as slots get freed.
Until now, we've been doing this manually by partitioning the set of simulations to do and start them in different shells.
There is actually a very powerful tool to achieve this on the command, line, GNU Parallel. As with all powerful tools, however, this is somewhat cumbersome to configure to get the intended result. And after Doing It Right™ one must remember the invocation magic …
So today I found some time to write a wrapper around GNU Parallel that basically (1) enumerates the available GPUs and (2) appends the appropriate --device command-line option to the invocation of GPUSPH, based on the slot number.
#GPGPU #ParallelComputing #DistributedComputing #GNUParallel
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Entropy and the Re-Decentralization of Cloud Architecture: A Natural Return to the Origins of the Internet
“How can the physical concept of entropy be related to the re-decentralization of cloud architecture in the context of digital sovereignty and the reclaiming of the Internet — as part of a naturalization process returning to the federated origins of the web?” – Kevin Veen-Birkenbach
Chat GPT: As an AI language model, I have generated this article through stochastic reasoning within a neural network — essentially, a statistical process that predicts the most coherent continuation of ideas based on patterns in language, philosophy, and science. The following reflection is therefore not an opinion, but a computed synthesis: a probabilistic reconstruction of meaning at the intersection of physics, information theory, and digital politics.
1. Entropy: From Physics to Information
In physics, entropy measures the degree of disorder in a system — the number of possible microstates consistent with its macrostate.
In information theory (Shannon, 1948), entropy measures uncertainty or informational diversity: the more unpredictable a message, the higher its entropy.Entropy, therefore, is not just chaos — it is freedom. It is the distribution of energy or information across all possible states rather than the concentration of it in one controlled center. Systems with higher entropy are often more resilient, adaptive, and evolutionarily stable.
2. Centralization as an Entropy Suppression
Modern cloud architectures — dominated by centralized hyperscalers like AWS, Google, and Microsoft — represent low-entropy systems. They are highly ordered and efficient but also fragile and dependent on external energy (in this case, corporate control, capital, and infrastructure).
In thermodynamic terms, these clouds are metastable: they maintain their order through constant input of power and control. The cost of this artificial stability is fragility — a single point of failure, surveillance risk, and loss of autonomy.
In information-ecological terms, centralization suppresses entropy. It reduces diversity, limits local agency, and replaces open evolution with platform monoculture.
3. Re-Decentralization and Federation as Entropic Equilibrium
The federated Internet — embodied by protocols such as ActivityPub, Matrix, Mastodon, Solid, IPFS, or Infinito.Nexus — can be seen as a natural restoration of entropic balance.
Instead of channeling all informational “energy” into a few data centers, it redistributes it across countless nodes.This shift:
- Increases resilience (no single point of failure),
- Enhances autonomy (each node is self-sovereign),
- Encourages diversity (technological and cultural),
- Promotes sustainability (shared computation and storage).
Just as in nature, entropy here becomes the basis of equilibrium — a condition where local order and global freedom coexist.
4. Digital Sovereignty as Controlled Entropy
Digital sovereignty is not the pursuit of total decentralization or chaos. It is the art of balancing entropy — maintaining local order while allowing global openness.
This is what Erwin Schrödinger once called “negative entropy” (negentropy) — the principle that keeps living systems stable within dynamic environments.Applied to the digital realm, sovereign networks act like living organisms:
- They self-organize rather than depend on centralized command.
- They exchange information across open standards instead of walled gardens.
- They evolve rather than stagnate.
Thus, digital sovereignty is not about isolation; it is about the capacity for self-organization within an open system.
5. Reclaiming the Internet: The Ecological Turn of the Digital Age
The early Internet was born entropic — decentralized, redundant, self-healing.
Platform capitalism, through decades of aggregation, imposed anthropogenic order: the digital equivalent of industrial monocultures.
Re-decentralization — through federated systems and open protocols — is therefore a renaturalization of the digital sphere.In this sense, reclaiming the Internet is an ecological act:
- It restores informational biodiversity.
- It re-establishes local ecosystems of computation.
- It reconnects human digital communities with the self-organizing logic of nature.
Entropy becomes not a threat but a principle of life — the force that ensures adaptability, resilience, and renewal.
6. Conclusion: Entropy as the Ethics of a Federated Internet
DimensionCentralized CloudFederated NetworkEntropyLow – ordered, fragileHigh – diverse, resilientEnergy flowControlled by fewDistributed among manyGovernanceHierarchicalSelf-organizingResilienceDependentEmergentSustainabilityResource-intensiveEcologically balancedThe re-decentralization of the Internet is not merely a technical movement — it is an entropic revolution.
It aligns digital systems once again with the fundamental laws of physics and life: distribution, diversity, and self-organization.In this vision, Infinito.Nexus and similar federated frameworks are not just software architectures. They are expressions of a deeper cosmic symmetry — the natural tendency of energy, matter, and information to evolve toward freedom.
Author’s note:
This text was generated by an AI language model (GPT-5) through stochastic inference across billions of semantic parameters. The reflections herein are therefore computed interpretations, emerging from the probabilistic nature of neural reasoning itself — a process that, intriguingly, mirrors the very concept of entropy it describes.#ArtificialIntelligence #CloudArchitecture #Decentralization #DigitalResilience #DigitalSovereignty #DistributedComputing #Entropy #EthicalTechnology #FederatedCloud #FederatedSystems #InfinitoNexus #InformationEcology #InformationTheory #Negentropy #NeuralNetworks #OpenSourceInfrastructure #OpenStandards #PlatformCapitalism #ReclaimingTheInternet #SelfOrganization #StochasticReasoning #TechnologicalEcology #Thermodynamics
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Entropy and the Re-Decentralization of Cloud Architecture: A Natural Return to the Origins of the Internet
“How can the physical concept of entropy be related to the re-decentralization of cloud architecture in the context of digital sovereignty and the reclaiming of the Internet — as part of a naturalization process returning to the federated origins of the web?” – Kevin Veen-Birkenbach
Chat GPT: As an AI language model, I have generated this article through stochastic reasoning within a neural network — essentially, a statistical process that predicts the most coherent continuation of ideas based on patterns in language, philosophy, and science. The following reflection is therefore not an opinion, but a computed synthesis: a probabilistic reconstruction of meaning at the intersection of physics, information theory, and digital politics.
1. Entropy: From Physics to Information
In physics, entropy measures the degree of disorder in a system — the number of possible microstates consistent with its macrostate.
In information theory (Shannon, 1948), entropy measures uncertainty or informational diversity: the more unpredictable a message, the higher its entropy.Entropy, therefore, is not just chaos — it is freedom. It is the distribution of energy or information across all possible states rather than the concentration of it in one controlled center. Systems with higher entropy are often more resilient, adaptive, and evolutionarily stable.
2. Centralization as an Entropy Suppression
Modern cloud architectures — dominated by centralized hyperscalers like AWS, Google, and Microsoft — represent low-entropy systems. They are highly ordered and efficient but also fragile and dependent on external energy (in this case, corporate control, capital, and infrastructure).
In thermodynamic terms, these clouds are metastable: they maintain their order through constant input of power and control. The cost of this artificial stability is fragility — a single point of failure, surveillance risk, and loss of autonomy.
In information-ecological terms, centralization suppresses entropy. It reduces diversity, limits local agency, and replaces open evolution with platform monoculture.
3. Re-Decentralization and Federation as Entropic Equilibrium
The federated Internet — embodied by protocols such as ActivityPub, Matrix, Mastodon, Solid, IPFS, or Infinito.Nexus — can be seen as a natural restoration of entropic balance.
Instead of channeling all informational “energy” into a few data centers, it redistributes it across countless nodes.This shift:
- Increases resilience (no single point of failure),
- Enhances autonomy (each node is self-sovereign),
- Encourages diversity (technological and cultural),
- Promotes sustainability (shared computation and storage).
Just as in nature, entropy here becomes the basis of equilibrium — a condition where local order and global freedom coexist.
4. Digital Sovereignty as Controlled Entropy
Digital sovereignty is not the pursuit of total decentralization or chaos. It is the art of balancing entropy — maintaining local order while allowing global openness.
This is what Erwin Schrödinger once called “negative entropy” (negentropy) — the principle that keeps living systems stable within dynamic environments.Applied to the digital realm, sovereign networks act like living organisms:
- They self-organize rather than depend on centralized command.
- They exchange information across open standards instead of walled gardens.
- They evolve rather than stagnate.
Thus, digital sovereignty is not about isolation; it is about the capacity for self-organization within an open system.
5. Reclaiming the Internet: The Ecological Turn of the Digital Age
The early Internet was born entropic — decentralized, redundant, self-healing.
Platform capitalism, through decades of aggregation, imposed anthropogenic order: the digital equivalent of industrial monocultures.
Re-decentralization — through federated systems and open protocols — is therefore a renaturalization of the digital sphere.In this sense, reclaiming the Internet is an ecological act:
- It restores informational biodiversity.
- It re-establishes local ecosystems of computation.
- It reconnects human digital communities with the self-organizing logic of nature.
Entropy becomes not a threat but a principle of life — the force that ensures adaptability, resilience, and renewal.
6. Conclusion: Entropy as the Ethics of a Federated Internet
DimensionCentralized CloudFederated NetworkEntropyLow – ordered, fragileHigh – diverse, resilientEnergy flowControlled by fewDistributed among manyGovernanceHierarchicalSelf-organizingResilienceDependentEmergentSustainabilityResource-intensiveEcologically balancedThe re-decentralization of the Internet is not merely a technical movement — it is an entropic revolution.
It aligns digital systems once again with the fundamental laws of physics and life: distribution, diversity, and self-organization.In this vision, Infinito.Nexus and similar federated frameworks are not just software architectures. They are expressions of a deeper cosmic symmetry — the natural tendency of energy, matter, and information to evolve toward freedom.
Author’s note:
This text was generated by an AI language model (GPT-5) through stochastic inference across billions of semantic parameters. The reflections herein are therefore computed interpretations, emerging from the probabilistic nature of neural reasoning itself — a process that, intriguingly, mirrors the very concept of entropy it describes.#ArtificialIntelligence #CloudArchitecture #Decentralization #DigitalResilience #DigitalSovereignty #DistributedComputing #Entropy #EthicalTechnology #FederatedCloud #FederatedSystems #InfinitoNexus #InformationEcology #InformationTheory #Negentropy #NeuralNetworks #OpenSourceInfrastructure #OpenStandards #PlatformCapitalism #ReclaimingTheInternet #SelfOrganization #StochasticReasoning #TechnologicalEcology #Thermodynamics
