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@kaptain shared a link, 2 months, 1 week ago
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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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@kaptain shared a link, 2 months, 1 week ago
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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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@kaptain shared a link, 2 months, 1 week ago
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Sandboxes: Run Agents in YOLO Mode, Safely

Over a quarterof production code is now AI-authored, with agents boosting pull requests by 60% when allowed to run autonomously in YOLO mode. Docker Sandboxes provide a safe boundary for agents, enabling fully autonomous operation without risking your machine or data... read more  

Sandboxes: Run Agents in YOLO Mode, Safely
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@kala shared a link, 2 months, 1 week 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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@kala shared a link, 2 months, 1 week ago
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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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@kala shared a link, 2 months, 1 week ago
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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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@kala shared a link, 2 months, 1 week ago
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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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@kala shared a link, 2 months, 1 week ago
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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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@devopslinks shared a link, 2 months, 1 week ago
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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
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@devopslinks shared a link, 2 months, 1 week ago
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Scaling a Monolith to 1M LOC: 113 Pragmatic Lessons from Tech Lead to CTO

The post discusses performance issues related to page counts, long cron-job reads, RAM pressure, and offloading work to background jobs. It also touches on common sources of front-end performance issues, the importance of running EXPLAIN on DB queries, and the benefits of cultivating a culture of op.. read more  

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.