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Uber engineered native gRPC endpoints directly into OpenSearch to eliminate inefficient REST/JSON translation layers within their architecture. Their automated pipeline for syncing JSON APIs with Protobuf schemas, their internal integration strategy, and the resulting performance gains in production environments for high-throughput ingestion and vector search workloads.

- Uber implemented gRPC as an OpenSearch module
- To prevent divergence between REST and gRPC, Uber built a three-stage automated pipeline
- Removing the JSON-to-Protobuf translation layer reduced p99 index write latency by 60% for Uber’s M3 metrics system and decreased batch indexing job runtimes by 20-35%.
- Large vector searches, which serialize poorly in JSON, saw a 53% reduction in p50 latency and a 43% reduction in p95 latency.
- Combining gRPC with binary document formats like SMILE proved highly effective, executing 30% faster than REST JSON and 45% faster than gRPC JSON.

https://www.uber.com/us/en/blog/high-performance-grpc/
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It's time to update your kernel

An unprivileged local user can write 4 controlled bytes into the page cache of any readable file on a Linux system, and use that to gain root.

https://copy.fail/
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Any user with Argo CD application get permissions can extract real Kubernetes Secret values including service account tokens, TLS certificates, database credentials, and API keys. On Applications where IncludeMutationWebhook=true is already set, exploitation requires only read-only Argo CD access.

https://github.com/argoproj/argo-cd/security/advisories/GHSA-3v3m-wc6v-x4x3
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The article features an interview with Landon Clipp, who built a multi-tenant GPU-based CaaS platform.
- Bypassing the NVIDIA GPU Operator
- Why gVisor Fails for GPUs
- VM Boot Delays
- Firmware and Memory Security
- Ideal Workload

https://kube.fm/gpu-containers-as-a-service-landon
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The article explains that while Kubernetes excels at scheduling and isolating workloads, it lacks the context to secure Large Language Models (LLMs), which process untrusted natural language inputs. Highlighting four key risks from the OWASP Top 10 for LLMs, the author argues that security controls shouldn't live within the model runtime (like Ollama). Instead, organizations need a dedicated, LLM-aware policy layer (such as LiteLLM, Kong AI Gateway, or Portkey) in front of the model to enforce validation, filtering, and authorization.

https://www.cncf.io/blog/2026/03/30/llms-on-kubernetes-part-1-understanding-the-threat-model/
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Uber engineered an automated approach to migrate its massive Java monorepo (over 600,000 tests, 15 million lines of code) from the deprecated JUnit 4 to JUnit 5. Facing challenges like the lack of native JUnit 5 support in their Bazel build system and custom test configurations, they successfully migrated over 75,000 test classes and 1.25 million lines of code in just four months without disrupting developer workflows.

https://www.uber.com/us/en/blog/junit-migration/
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Claude Code gave me three "tickets" for a free week. You can grab them using this link: https://claude.ai/referral/NXtyf-cgbQ
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The observability market is shifting from volume-based data ingestion to a value-driven model due to the unsustainable costs of scaling cloud-native and AI workloads. Driven by innovations like Chronosphere’s "Logs 2.0" and its subsequent acquisition by Palo Alto Networks, the industry is prioritizing "signal discipline"—retaining only actionable telemetry—and integrating observability directly into broader AI and security platforms.

https://siliconangle.com/2026/02/05/observability-cost-ai-scale-chronosphere-opensourcesummit/
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Managing expenses in the cloud requires a strategic approach beyond just looking at bills. A senior engineer shares valuable insight into optimizing costs effectively in this detailed read.
https://medium.com/@razkevich8/cloud-cost-optimization-a-senior-engineers-guide-d49ed4606de1
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