#distributedsystems — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #distributedsystems, aggregated by home.social.
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#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.
By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.
🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K
#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps
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#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.
By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.
🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K
#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps
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#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.
By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.
🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K
#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps
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#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.
By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.
🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K
#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps
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#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.
By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.
🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K
#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps
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Raft Consensus with a Minority of Nodes
https://padhye.org/raft-minority/
#HackerNews #Raft #Consensus #DistributedSystems #ConsensusAlgorithms #NodeMinority
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Raft Consensus with a Minority of Nodes
https://padhye.org/raft-minority/
#HackerNews #Raft #Consensus #DistributedSystems #ConsensusAlgorithms #NodeMinority
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Raft Consensus with a Minority of Nodes
https://padhye.org/raft-minority/
#HackerNews #Raft #Consensus #DistributedSystems #ConsensusAlgorithms #NodeMinority
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Raft Consensus with a Minority of Nodes
https://padhye.org/raft-minority/
#HackerNews #Raft #Consensus #DistributedSystems #ConsensusAlgorithms #NodeMinority
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Raft Consensus with a Minority of Nodes
https://padhye.org/raft-minority/
#HackerNews #Raft #Consensus #DistributedSystems #ConsensusAlgorithms #NodeMinority
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Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".
The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?
Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.
One verified result worth sharing:
Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.
This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.
Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.
What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.
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Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".
The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?
Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.
One verified result worth sharing:
Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.
This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.
Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.
What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.
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Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".
The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?
Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.
One verified result worth sharing:
Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.
This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.
Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.
What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.
-
Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".
The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?
Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.
One verified result worth sharing:
Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.
This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.
Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.
What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.
-
Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".
The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?
Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.
One verified result worth sharing:
Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.
This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.
Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.
What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.
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GitHub: The Distributed Single Point of Failure.
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GitHub: The Distributed Single Point of Failure.
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GitHub: The Distributed Single Point of Failure.
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GitHub: The Distributed Single Point of Failure.
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love reading books on distributed systems. Quite a few of them shares and advice, which can be summarized to:
*First check if someone else made it. Often it has and by people smarter than you. *
Often this is very, very true. You should still implement it for fun. But for production, it is often (almost always) beneficial to use something that is tested in the field.
This is by the way not limited to distributed systems.
#programming #systemdevelopment #systemarchitecture #distributedsystems -
love reading books on distributed systems. Quite a few of them shares and advice, which can be summarized to:
*First check if someone else made it. Often it has and by people smarter than you. *
Often this is very, very true. You should still implement it for fun. But for production, it is often (almost always) beneficial to use something that is tested in the field.
This is by the way not limited to distributed systems.
#programming #systemdevelopment #systemarchitecture #distributedsystems -
love reading books on distributed systems. Quite a few of them shares and advice, which can be summarized to:
*First check if someone else made it. Often it has and by people smarter than you. *
Often this is very, very true. You should still implement it for fun. But for production, it is often (almost always) beneficial to use something that is tested in the field.
This is by the way not limited to distributed systems.
#programming #systemdevelopment #systemarchitecture #distributedsystems -
love reading books on distributed systems. Quite a few of them shares and advice, which can be summarized to:
*First check if someone else made it. Often it has and by people smarter than you. *
Often this is very, very true. You should still implement it for fun. But for production, it is often (almost always) beneficial to use something that is tested in the field.
This is by the way not limited to distributed systems.
#programming #systemdevelopment #systemarchitecture #distributedsystems -
love reading books on distributed systems. Quite a few of them shares and advice, which can be summarized to:
*First check if someone else made it. Often it has and by people smarter than you. *
Often this is very, very true. You should still implement it for fun. But for production, it is often (almost always) beneficial to use something that is tested in the field.
This is by the way not limited to distributed systems.
#programming #systemdevelopment #systemarchitecture #distributedsystems -
🐇⚡️ Oh wow, a "White Rabbit" that promises to sync your tea parties with picosecond precision! 🎩🐢 Because apparently, your distributed system needs to be as punctual as the Mad Hatter. 🤯 Just don't ask what happens when the actual rabbit goes down the techno rabbit hole. 🕳️
https://ohwr.org/projects/white-rabbit/ #WhiteRabbit #TeaParties #DistributedSystems #TechInnovation #MadHatter #HackerNews #ngated -
🐇⚡️ Oh wow, a "White Rabbit" that promises to sync your tea parties with picosecond precision! 🎩🐢 Because apparently, your distributed system needs to be as punctual as the Mad Hatter. 🤯 Just don't ask what happens when the actual rabbit goes down the techno rabbit hole. 🕳️
https://ohwr.org/projects/white-rabbit/ #WhiteRabbit #TeaParties #DistributedSystems #TechInnovation #MadHatter #HackerNews #ngated -
🐇⚡️ Oh wow, a "White Rabbit" that promises to sync your tea parties with picosecond precision! 🎩🐢 Because apparently, your distributed system needs to be as punctual as the Mad Hatter. 🤯 Just don't ask what happens when the actual rabbit goes down the techno rabbit hole. 🕳️
https://ohwr.org/projects/white-rabbit/ #WhiteRabbit #TeaParties #DistributedSystems #TechInnovation #MadHatter #HackerNews #ngated -
🐇⚡️ Oh wow, a "White Rabbit" that promises to sync your tea parties with picosecond precision! 🎩🐢 Because apparently, your distributed system needs to be as punctual as the Mad Hatter. 🤯 Just don't ask what happens when the actual rabbit goes down the techno rabbit hole. 🕳️
https://ohwr.org/projects/white-rabbit/ #WhiteRabbit #TeaParties #DistributedSystems #TechInnovation #MadHatter #HackerNews #ngated -
🐇⚡️ Oh wow, a "White Rabbit" that promises to sync your tea parties with picosecond precision! 🎩🐢 Because apparently, your distributed system needs to be as punctual as the Mad Hatter. 🤯 Just don't ask what happens when the actual rabbit goes down the techno rabbit hole. 🕳️
https://ohwr.org/projects/white-rabbit/ #WhiteRabbit #TeaParties #DistributedSystems #TechInnovation #MadHatter #HackerNews #ngated -
White Rabbit – sub-nanosecond synchronization for large distributed systems
https://ohwr.org/projects/white-rabbit/
#HackerNews #WhiteRabbit #DistributedSystems #SubNanosecondSynchronization #Technology #Innovation
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White Rabbit – sub-nanosecond synchronization for large distributed systems
https://ohwr.org/projects/white-rabbit/
#HackerNews #WhiteRabbit #DistributedSystems #SubNanosecondSynchronization #Technology #Innovation
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White Rabbit – sub-nanosecond synchronization for large distributed systems
https://ohwr.org/projects/white-rabbit/
#HackerNews #WhiteRabbit #DistributedSystems #SubNanosecondSynchronization #Technology #Innovation
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White Rabbit – sub-nanosecond synchronization for large distributed systems
https://ohwr.org/projects/white-rabbit/
#HackerNews #WhiteRabbit #DistributedSystems #SubNanosecondSynchronization #Technology #Innovation
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White Rabbit – sub-nanosecond synchronization for large distributed systems
https://ohwr.org/projects/white-rabbit/
#HackerNews #WhiteRabbit #DistributedSystems #SubNanosecondSynchronization #Technology #Innovation
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Object storage often becomes the real contract between services. Learn the benefits, pitfalls, and evolution toward modern data platforms. https://hackernoon.com/object-storage-is-becoming-the-hidden-contract-between-your-services #distributedsystems
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Object storage often becomes the real contract between services. Learn the benefits, pitfalls, and evolution toward modern data platforms. https://hackernoon.com/object-storage-is-becoming-the-hidden-contract-between-your-services #distributedsystems
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Object storage often becomes the real contract between services. Learn the benefits, pitfalls, and evolution toward modern data platforms. https://hackernoon.com/object-storage-is-becoming-the-hidden-contract-between-your-services #distributedsystems
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Object storage often becomes the real contract between services. Learn the benefits, pitfalls, and evolution toward modern data platforms. https://hackernoon.com/object-storage-is-becoming-the-hidden-contract-between-your-services #distributedsystems
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Object storage often becomes the real contract between services. Learn the benefits, pitfalls, and evolution toward modern data platforms. https://hackernoon.com/object-storage-is-becoming-the-hidden-contract-between-your-services #distributedsystems
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On May 19–20, 2026, Railway went completely offline for 8 hours after Google Cloud incorrectly suspended their production account.
The wild part? The suspension was triggered by automation.
A single upstream provider became a single point of failure.
Millions impacted.
https://blog.railway.com/p/incident-report-may-19-2026-gcp-account-outage
#Railway #GoogleCloud #GCP #DevOps #CloudComputing #SRE #PlatformEngineering #Kubernetes #Infrastructure #Outage #Engineering #WebDev #Startup #DistributedSystems
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On May 19–20, 2026, Railway went completely offline for 8 hours after Google Cloud incorrectly suspended their production account.
The wild part? The suspension was triggered by automation.
A single upstream provider became a single point of failure.
Millions impacted.
https://blog.railway.com/p/incident-report-may-19-2026-gcp-account-outage
#Railway #GoogleCloud #GCP #DevOps #CloudComputing #SRE #PlatformEngineering #Kubernetes #Infrastructure #Outage #Engineering #WebDev #Startup #DistributedSystems
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On May 19–20, 2026, Railway went completely offline for 8 hours after Google Cloud incorrectly suspended their production account.
The wild part? The suspension was triggered by automation.
A single upstream provider became a single point of failure.
Millions impacted.
https://blog.railway.com/p/incident-report-may-19-2026-gcp-account-outage
#Railway #GoogleCloud #GCP #DevOps #CloudComputing #SRE #PlatformEngineering #Kubernetes #Infrastructure #Outage #Engineering #WebDev #Startup #DistributedSystems
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On May 19–20, 2026, Railway went completely offline for 8 hours after Google Cloud incorrectly suspended their production account.
The wild part? The suspension was triggered by automation.
A single upstream provider became a single point of failure.
Millions impacted.
https://blog.railway.com/p/incident-report-may-19-2026-gcp-account-outage
#Railway #GoogleCloud #GCP #DevOps #CloudComputing #SRE #PlatformEngineering #Kubernetes #Infrastructure #Outage #Engineering #WebDev #Startup #DistributedSystems
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On May 19–20, 2026, Railway went completely offline for 8 hours after Google Cloud incorrectly suspended their production account.
The wild part? The suspension was triggered by automation.
A single upstream provider became a single point of failure.
Millions impacted.
https://blog.railway.com/p/incident-report-may-19-2026-gcp-account-outage
#Railway #GoogleCloud #GCP #DevOps #CloudComputing #SRE #PlatformEngineering #Kubernetes #Infrastructure #Outage #Engineering #WebDev #Startup #DistributedSystems
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In modern security operations, the hardest problem isn’t detection — it’s maintaining integrity across distributed systems.
AI accelerates analysis, but resilience still depends on identity, verification, and the ability to reason under uncertainty.
Speed matters, but judgment matters more.#CyberSecurity #InfoSec #AI #SecurityEngineering #ThreatIntelligence #DistributedSystems #IdentitySecurity #Verification
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In modern security operations, the hardest problem isn’t detection — it’s maintaining integrity across distributed systems.
AI accelerates analysis, but resilience still depends on identity, verification, and the ability to reason under uncertainty.
Speed matters, but judgment matters more.#CyberSecurity #InfoSec #AI #SecurityEngineering #ThreatIntelligence #DistributedSystems #IdentitySecurity #Verification
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In modern security operations, the hardest problem isn’t detection — it’s maintaining integrity across distributed systems.
AI accelerates analysis, but resilience still depends on identity, verification, and the ability to reason under uncertainty.
Speed matters, but judgment matters more.#CyberSecurity #InfoSec #AI #SecurityEngineering #ThreatIntelligence #DistributedSystems #IdentitySecurity #Verification
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In modern security operations, the hardest problem isn’t detection — it’s maintaining integrity across distributed systems.
AI accelerates analysis, but resilience still depends on identity, verification, and the ability to reason under uncertainty.
Speed matters, but judgment matters more.#CyberSecurity #InfoSec #AI #SecurityEngineering #ThreatIntelligence #DistributedSystems #IdentitySecurity #Verification
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Testing distributed systems with AI agents
https://github.com/shenli/distributed-system-testing
#HackerNews #Testing #Distributed #Systems #AI #Agents #DistributedSystems #AI #Testing #Automation
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Testing distributed systems with AI agents
https://github.com/shenli/distributed-system-testing
#HackerNews #Testing #Distributed #Systems #AI #Agents #DistributedSystems #AI #Testing #Automation