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The Four Caches in LLM Serving 

<p>As LLM applications grow more complex, inference cost and latency become increasingly important. A single request can contain thousands or even millions of tokens from system instructions, conversation history, retrieved documents, tool definitions, and user input. Reprocessing the same information again and again wastes both time and compute.&#160; Caching helps avoid this repeated work. But [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/four-caches-in-llm-serving/">The Four Caches in LLM Serving </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/8
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Getting Started with Grok Bot 

<p>Last week, a GitHub notification arrived while I was asleep: a bug had been reproduced in staging, documented with screenshots, and assigned to the right engineer. A bot I set up four days earlier did the work overnight. That is the gap Grok Bot is designed to close. Instead of suggesting what to do, it [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/grok-bot-automation-tutorial/">Getting Started with Grok Bot </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/7
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GPT-6 Astra: What’s Actually New in OpenAI’s New Frontier Model

<p>OpenAI has released GPT-6 Astra, its newest frontier model, less than a week after Anthropic&#8217;s Claude Fable 5.1. OpenAI calls Astra the world&#8217;s most intelligent and aligned model yet. What actually makes Astra different? The simplest way to put it is this: Astra is built to do more, not just answer more. It can use [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/gpt-6-astra-explained/">GPT-6 Astra: What&#8217;s Actually New in OpenAI&#8217;s New Frontier Model</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/5
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Top 10 GitHub Repositories Trending in August 2026 (AI, Agents & Dev Tooling Edition) 

<p>If you spent any time on GitHub Trending in August, you&#160;probably noticed&#160;the centre of gravity had shifted again. Models took a back seat to the machinery around them: agent harnesses, skills, memory layers, gateways, and document tooling. One repository alone gained more than 190,000 stars in four weeks.&#160; We tracked star growth, momentum, ecosystem impact, [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/top-github-repositories-august-2026/">Top 10 GitHub Repositories Trending in August 2026 (AI, Agents &#038; Dev Tooling Edition) </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/4
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OpenCode Explained: The Open-Source AI Coding Agent

<p>OpenCode is open source and works with any model, but those are no longer its most interesting features. Model choice is table stakes. What sets OpenCode apart is its architecture, and the trade-offs that come with it, especially if you are coming from Claude Code.  In this article, we look at what&#160;OpenCode&#160;is, what makes its architecture different, what [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/opencode-ai-explained/">OpenCode Explained: The Open-Source AI Coding Agent</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/3
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A Complete Guide to Decoding LLM Model Names

<p>If you have ever tried downloading a local LLM, you have probably seen model names that look like this:  Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound At first, it looks like meaningless technical shorthand.&#160; It&#160;isn&#8217;t!&#160; Every part of that name tells you something about the model: how large it is, how it is built, how much of it is used at a time, how [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/decoding-llm-model-names/">A Complete Guide to Decoding LLM Model Names</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/2
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5 Best Local LLMs You Can Run on a Mac mini in 2026

<p>Proprietary models are&#160;amazing! But sometimes what is of importance is configurability rather than raw power. This has led to the emergence of&#160;locally hosted models.&#160; The Mac mini has&#160;emerged&#160;as a surprisingly capable machine for running AI locally. With Apple Silicon, enough unified memory, and tools like&#160;Ollama&#160;and LM Studio, users can now run capable models entirely&#160;on-device.&#160; But [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/09/best-local-llms-mac-mini-2026/">5 Best Local LLMs You Can Run on a Mac mini in 2026</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/9/1
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Top 7 Free AI Automation Courses with Certificates

<p>You don’t need to know anything about AI automation to get started. There are plenty of free courses that can take you from the basics to building your own automations, even if you’re starting from zero. Some are completely beginner-friendly, while others are better once you’re comfortable with the basics. We’ve picked courses covering popular [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/08/free-ai-automation-courses-with-certificates/">Top 7 Free AI Automation Courses with Certificates</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/8/30
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10 Essential Agentic AI Concepts Explained Simply

<p>AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you&#160;understand the few core ideas that actually matter. Here are 10 agentic AI concepts [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/08/basic-agentic-ai-concepts/">10 Essential Agentic AI Concepts Explained Simply</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/8/27
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Mastering the AI Project Cycle: From Concept to Production

<p>In fact, AI projects are not built by simply choosing a model and feeding it data. Furthermore, a successful AI system goes through multiple stages, starting with identifying the right problem and ending with deployment, monitoring, and continuous improvement. This structured journey is known as the AI Project Cycle. It helps teams move from an [&#8230;]</p> <p>The post <a href="https://www.analyticsvidhya.com/blog/2026/08/ai-project-cycle-guide/">Mastering the AI Project Cycle: From Concept to Production</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>

2026/8/26
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