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AI搜索工具演进对比：OpenAI、Gemini、Perplexity 2026指南

English Summary: The article evaluates the evolution of AI-powered search tools from 2023 to 2025, highlighting significant improvements in accuracy and usability. It compares implementations from OpenAI (o3/o4-mini), Google Gemini, and Perplexity, noting OpenAI's real-time reasoning with search integration as particularly effective. The author shares practical use cases including code porting and technical research, concluding that AI search has become genuinely useful for research tasks while raising questions about the future economic model of the web. 中文摘要翻译：本文评估了从2023年到2025年AI搜索工具的演进，重点强调了准确性和可用性的显著改进。比较了OpenAI（o3/o4-mini）、Google Gemini和Perplexity的实现方案，指出OpenAI的实时推理与搜索集成特别有效。作者分享了包括代码移植和技术研究在内的实际用例，得出结论：AI搜索在研究任务中已变得真正有用，同时引发了关于网络未来经济模式的疑问。

LLMS2026/2/15

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GPT-4o下架影响AI问答引擎？2026技术演进指南

English Summary: This article analyzes the impact of GPT-4o's delisting on AI Answer Engines, focusing on technical evolution from GPT-2 to GPT-3, including parameter scaling, few-shot learning capabilities, and performance across NLP tasks. It highlights how large language models are shifting from fine-tuning to in-context learning, with implications for search and question-answering systems. 中文摘要翻译：本文分析了GPT-4o下架对AI Answer Engine的影响，重点探讨了从GPT-2到GPT-3的技术演进，包括参数规模扩展、少样本学习能力以及在自然语言处理任务中的表现。文章强调了大语言模型从微调向上下文学习的转变，及其对搜索和问答系统的影响。

LLMS2026/2/15

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豆包大模型2025发展指南：核心技术解析与生态演进

Doubao, ByteDance's large language model, has evolved from a cost-effective AI assistant into a comprehensive multimodal ecosystem. Key milestones include achieving 1 billion downloads by May 2024 with a disruptive pricing strategy (0.0008元/千Tokens), launching video generation models (Seedance series), and expanding into music generation, 3D modeling, and real-time video calls. By late 2025, it reached over 1 billion daily active users and formed partnerships with major automotive and tech companies like Tesla and Xiaomi. The model's architecture is based on Transformer and MoE (Mixture of Experts), supporting diverse applications from AI programming to deep research tools. 豆包大模型已从高性价比的AI助手发展为覆盖文、图、音、视频、3D等多模态的生态平台。2024年5月实现1亿次下载，以0.0008元/千Tokens的定价开启商业化；随后推出视频生成（Seedance系列）、音乐生成、3D模型生成等功能。2025年底日活用户突破1亿，并与特斯拉、小米等企业达成合作。其技术基于Transformer和MoE架构，支持AI编程、深入研究等复杂场景应用。

AI大模型2026/2/15

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大语言模型推理能力提升指南：2025年最新方法与技术解析

This article provides a comprehensive overview of methods to enhance reasoning capabilities in Large Language Models (LLMs), covering prompt engineering techniques like Chain-of-Thought and Tree-of-Thought, architectural improvements such as RAG and neuro-symbolic hybrids, and emerging approaches like latent space reasoning. It also discusses evaluation benchmarks and challenges in achieving reliable, interpretable reasoning for high-stakes applications. 本文全面综述了提升大语言模型推理能力的方法，涵盖提示工程技术（如思维链、思维树）、架构改进（如检索增强生成、神经符号混合）以及新兴方法（如隐空间推理）。同时探讨了评估基准及在关键应用中实现可靠、可解释推理所面临的挑战。

LLMS2026/2/14

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Kalosm v0.2.0 AI智能体RAG工作流优化与性能提升2026指南

English Summary: Kalosm v0.2.0 introduces significant enhancements for open-source AI agents in RAG workflows, featuring task evaluation, prompt auto-tuning, regex validation, Surreal DB integration, improved chunking strategies, and performance optimizations. (中文摘要翻译：Kalosm v0.2.0为开源AI智能体在RAG工作流中带来重大升级，包括任务评估、提示词自动调优、正则表达式验证、Surreal数据库集成、改进的分块策略和性能优化。)

LLMS2026/2/13

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Neum AI 2024指南：构建可扩展RAG数据平台详解

Neum AI is a comprehensive data platform that enables developers to implement Retrieval Augmented Generation (RAG) for large language models by extracting data from various sources, converting it into vector embeddings, and storing them in vector databases for similarity search. It offers scalable architecture, built-in connectors, real-time synchronization, and customizable preprocessing to streamline RAG implementation. (Neum AI是一个全面的数据平台，帮助开发者通过检索增强生成（RAG）技术为大语言模型提供上下文支持。它从多种数据源提取数据，将其转换为向量嵌入并存储到向量数据库中进行相似性搜索。该平台提供可扩展的架构、内置连接器、实时同步和可定制的预处理功能，简化RAG实施流程。)

AI大模型2026/2/13

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Semantic Router高效语义决策层：2026年提升LLM响应速度指南

Semantic Router is a high-performance decision layer designed for large language models (LLMs) and agents, enabling routing decisions based on semantic understanding rather than waiting for LLM responses. This approach significantly improves system response speed and reduces API costs. (Semantic Router 是一个专为大型语言模型和Agent设计的高效决策层，通过语义化理解进行路由决策，显著提升响应速度并降低API成本。)

LLMS2026/2/13

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Airweave开源上下文检索层详解：2024年AI代理数据指南

Airweave is an open-source context retrieval layer that connects to various data sources, syncs and indexes data, and provides a unified LLM-friendly search interface for AI agents and RAG systems. (Airweave是一个开源上下文检索层，可连接多种数据源，同步并索引数据，为AI智能体和RAG系统提供统一的LLM友好搜索接口。)

LLMS2026/2/13

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构建类型安全LLM代理的模块化TypeScript库2026指南

English Summary: llm-exe is a modular TypeScript library for building type-safe LLM agents and AI functions with full TypeScript support, provider-agnostic architecture, and production-ready features like automatic retries and schema validation. It enables developers to create composable executors, powerful parsers, and autonomous agents while allowing one-line provider switching between OpenAI, Anthropic, Google, xAI, and others. 中文摘要翻译：llm-exe是一个模块化TypeScript库，用于构建类型安全的LLM代理和AI函数，具有完整的TypeScript支持、供应商无关的架构以及生产就绪功能（如自动重试和模式验证）。它使开发人员能够创建可组合的执行器、强大的解析器和自主代理，同时允许在OpenAI、Anthropic、Google、xAI等供应商之间进行单行切换。

LLMS2026/2/13

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