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Kubernetes v1.36 Sneak Peek

Kubernetes v1.36, coming inApril 2026, will feature removals and deprecations, with enhancements that include retirement of the Ingress NGINX project and thedeprecation of .spec.externalIPs in Service.Additionally, the release will remove the gitRepo volume driver and introduce enhancements like fas.. read more  

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Broadcom Makes Its Pitch To Run Kubernetes On VMware VCF

Broadcom's $69 billion acquisition of virtualization pioneer VMware in late 2023 brought about significant price increases and a shift towards subscription-based licensing. The company aims to establish VMware Cloud Foundation (VCF) as the foundation for enterprise workloads gravitating towards priv.. read more  

Broadcom Makes Its Pitch To Run Kubernetes On VMware VCF
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Docker Offload now Generally Available: The Full Power of Docker, for Every Developer, Everywhere.

Docker Offload is a managed cloud service that moves the container engine to Docker’s secure cloud, allowing developers to run Docker from any environment without changing their workflows. With Docker Offload, developers can keep using the same commands and workflows they are accustomed to in Docker.. read more  

Docker Offload now Generally Available: The Full Power of Docker, for Every Developer, Everywhere.
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llm-d officially a CNCF Sandbox project

At Google Cloud, the llm-d project has been accepted as a Cloud Native Computing Foundation (CNCF) Sandbox project. This collaboration with industry leaders like Red Hat, IBM Research, CoreWeave, and NVIDIA aims to provide a framework for any model, accelerator, or cloud. The introduction of GKE Inf.. read more  

llm-d officially a CNCF Sandbox project
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@kala shared a link, 1 month ago
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From zero to a RAG system: successes and failures

An engineer spun up an internal chat with a localLLaMAmodel viaOllama, a PythonFlaskAPI, and aStreamlitfrontend. They moved off in-memoryLlamaIndexto batch ingestion intoChromaDB(SQLite). Checkpoints and tolerant parsing went in to stop RAM disasters. Indexing produced 738,470 vectors (~54 GB). They.. read more  

From zero to a RAG system: successes and failures
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Why we're rethinking cache for the AI era

Cloudflare data shows that 32% of network traffic originates from automated traffic, including AI assistants fetching data for responses. AI bots often issue high-volume requests and access rarely visited content, impacting cache efficiency. Cloudflare researchers propose AI-aware caching algorithms.. read more  

Why we're rethinking cache for the AI era
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Our most intelligent open models, built from Gemini 3 research and technology to maximize intelligence-per-parameter

Built from Gemini 3 research and technology, Gemma 4 offers maximum compute and memory efficiency for mobile and IoT devices. Develop autonomous agents, multimodal applications, and multilingual experiences with Gemma 4's unprecedented intelligence-per-parameter... read more  

Our most intelligent open models, built from Gemini 3 research and technology to maximize intelligence-per-parameter
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Qwen3.6-Plus: Towards Real World Agents

Qwen3.6-Plus, the latest release following Qwen3.5 series, offers enhanced agentic coding capabilities and sharper multimodal reasoning. The model excels in frontend web development and complex problem-solving, setting a new standard in the developer ecosystem. Qwen3.6-Plus is available via Alibaba .. read more  

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State of Context Engineering in 2026

Context engineering has evolved in the AI engineering field since mid-2025 with the introduction of patterns for managing context effectively. These patterns include progressive disclosure, compression, routing, retrieval strategies, and tool management, each addressing a different dimension of the .. read more  

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RAM is getting expensive, so squeeze the most from it

The Register contrastszramandzswap. It flags a patch that claims up to 50% fasterzramops. It notes Fedora enableszramby default. It details thatzramprovides compressed in‑RAM swap (LZ4).zswapcompresses pages before writing to disk and requires on‑disk swap... read more  

RAM is getting expensive, so squeeze the most from it
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.