FSM Benchmark for Evaluating Network AI Agents
FSM benchmark network configuration: how NetAgentBench reveals failures of LLM agents in dynamic network scenarios and multi-turn behavior.
FSM benchmark network configuration: how NetAgentBench reveals failures of LLM agents in dynamic network scenarios and multi-turn behavior.
Hive federation in a data warehouse: how to move from a monolith to a distributed architecture without downtime or loss of data consistency.
Edge-cloud multi-agent architecture with decentralized management: how to reduce latency, traffic, and enhance resilience in mobile automation. –>
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How to design low-latency systems: controlling communication, Disruptor, Aeron, and the trade-offs between speed and architecture.
CPU-free LLM inference: how to remove the CPU from the critical path and stabilize latency in LLM serving architectures.
How an agentic system manages the context window through Journal, Review, and Timeline, reducing latency and improving consistency in multi-agent reasoning.
KV cache optimization in multi-LoRA serving: how ForkKV reduces memory consumption and increases throughput of LLM inference.
Root cause analysis (RCA) hinges on scale and the human factor. Meta’s approach with DrP demonstrates how to turn debugging into a reproducible engineering process. The problem does not manifest immediately — until the system reaches organizational scale. Incidents begin to recur, but each time they are investigated anew. Knowledge of where to look for … Read more
Platform Program split became a key step for Uber when the growth of the team began to hinder development. This decision changed both the architecture and the organization simultaneously. The problem manifested not at the code level, but at the level of team interaction. When Uber’s engineering organization grew to about 100 people, the division … Read more
P2P model distribution in Kubernetes with Dragonfly: how to reduce traffic to the origin and accelerate the delivery of large models from Hugging Face and ModelScope.
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