LLM

DeepWiki is an advanced AI-powered platform designed to revolutionize how developers and researchers interact with code repositories, particularly those hosted on GitHub. By leveraging state-of-the-art large language models (LLMs) and sophisticated code analysis techniques, DeepWiki automatically generates comprehensive, interactive, and dynamic documentation for software projects. It transforms complex codebases into

Quick answer. To create an llms.txt file: write a Markdown file whose only required element is an H1 with your site name, add a blockquote summary, group your best pages under H2 sections as Markdown links with one-line descriptions, put skippable content under ## Optional, and save it to your

Quick answer. To build an MCP server in .NET, install the ModelContextProtocol NuGet package in a .NET 10 console app, register tools and resources with the McpServerBuilder, expose them over stdio or SSE, and connect from Claude Desktop, Cursor, or any MCP client by adding the server binary to its

Quick answer. The correct filename is llms.txt — plural, with the s. llm.txt is a common misspelling and no spec, tool, or crawler looks for it. If you have already deployed /llm.txt, rename it to /llms.txt and nothing will be reading the old path, because nothing ever

Quick answer. An llms.txt generator crawls your site and emits a Markdown index of your pages. Firecrawl's is the best free option; Mintlify, GitBook, Yoast and AIOSEO build it into their platforms. Treat every generator's output as a first draft — Common Crawl found 68% of

Large Language Models (LLMs) such as GPT, LLaMA, and Falcon require substantial computational resources, particularly GPUs, for training, fine-tuning, and inference. Choosing the right cloud GPU depends on model size, workload type (training vs. inference), latency and throughput needs, and cost constraints. This guide explores the best cloud GPUs for

Small language models have quietly become one of the most useful tools a developer can keep on their own machine. You no longer need a rack of GPUs or a cloud bill to get a capable model answering questions, drafting code, and summarising documents entirely offline. The picture has changed

DeepSeek V4 is released. Compare V3 vs V4-Pro vs V4-Flash on confirmed specs, benchmarks, and API pricing — no speculation, only real data from the April 2026 launch.

Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG) constitute two distinct paradigms for augmenting large language models (LLMs) with external knowledge. While both frameworks are designed to enhance response fidelity and contextual relevance, they differ fundamentally in their architectural implementations, computational trade-offs, and optimal deployment scenarios. This article provides a rigorous

Quick answer. To do RAG over Excel, parse spreadsheets into structured chunks with LlamaIndex (or pandas + custom loaders), embed each row or sheet section into a vector store like Chroma or FAISS, then retrieve the top-k matching chunks at query time and pass them to an LLM. The output is

Large Language Models (LLMs) such as Ollama necessitate a structured installation and configuration process to ensure seamless execution in Ubuntu-based environments. This document delineates the essential procedures for system preparation, software installation, runtime execution, and optional UI configurations. Want the full picture? Read our continuously-updated Self-Hosting LLMs Complete Guide (2026)

Quick answer. Mochi 1 still installs cleanly on Apple Silicon via ComfyUI, but in 2026 Wan 2.2 and LTX-Video usually beat it on both quality and speed. Run Mochi 1 only if you specifically want its prompt adherence; pick Wan 2.2 otherwise. Expect minutes per short clip on

Explore DeepSeek R1’s open-source AI models, from lightweight 1.5B to enterprise-grade 671B MoE architectures. This guide analyzes each variant’s strengths, ideal use cases, and step-by-step RAG integration for domain-specific tasks.