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@kala shared a link, 1 month, 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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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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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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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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Supply Chain Attack on Axios Pulls Malicious Dependency from npm

A supply chain attack on Axios introduced a malicious dependency, plain-crypto-js@4.2.1, published minutes earlier and absent from the project’s GitHub releases... 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
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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  

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Deployment strategies: Types, trade-offs, and how to choose

Deployment strategies control traffic shifts, rollback speed, and release risk. Options:canary,blue‑green,rolling,feature flags,shadow,immutable, andGitOps. Strategies trade production risk for setup cost. They pair withArgo Rollouts,Kayenta,ArgoCD/Flux, service meshes, and flag platforms. Pipelines.. read more  

Deployment strategies: Types, trade-offs, and how to choose
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Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fle

Cloudways scaled from a bootstrapped startup to a leading managed PHP hosting service, encountering challenges with growing support load. Early on, Cloudways recognized the opportunity to implement an AI-based SRE agent to reduce the burden on support teams and provide faster diagnosis and resolutio.. read more  

Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fle
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.