1056 个
系统综述《Techniques for Adapting Large Language Models to Low-Resource, Morphologically Rich Languages》的补充材料。Supplementary materials for the systematic review: Techniques for Adapting Large Language Models to Low-Resource, Morphologically Rich Languages
面向 LLM agent 的 MCP server，用于加拿大调整后成本基础（ACB）与资本利得：支持平均成本法与 superficial-loss 检测。MCP server for Canadian adjusted cost base (ACB) and capital gains: average-cost, superficial-loss detection, for LLM agents.
适用于 DeepSeek、Qwen、GLM 及多种 AI 模型的 OpenAI 兼容 API 示例。OpenAI Compatible API examples for DeepSeek, Qwen, GLM and multiple AI models.
Local-first 可视化任务图，使人类与 AI Agent 在目标、进度、推理与下一步行动上保持对齐。Local-first visual task graph that keeps humans and AI agents aligned on goals, progress, reasoning, and the next action.
用于探索 LLM Security 新兴研究主题的研究路线图与文献综述仓库。A research roadmap and literature review repository for exploring emerging research topics in Large Language Model Security.
本地优先的研究论文工作空间与可视化 AI Agent 工作流构建器Local-first research paper workspace and visual AI agent workflow builder
本地优先的 CLI，用于评估 MCP server 的开销与能力，数据不离开本机。A local-first CLI that measures what an MCP server costs and what it can do. Nothing leaves your machine.
探索用于测试 ML、LLM 和 Agentic AI 系统的 AI 安全工具、论文与框架Explore AI security tools, papers, and frameworks for testing ML, LLM, and agentic AI systems
以问题为先、步骤可验证、可自我进化的 agent skill，用于系统综述与 Meta 分析。A question-first, step-verified, self-improving agent skill for systematic reviews and meta-analysis
🚀 使用强化学习优化半精度通用矩阵乘法（HGEMM）CUDA kernel，性能超越 cuBLAS 及其他基准。🚀 Optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels using reinforcement learning, surpassing cuBLAS and other benchmarks with superior performance.
Mielepiù 工程案例研究——一款 B2B 销售平台，其 AI Agent 基于真实数据作答并给出引用来源。Engineering case study of Mielepiù, a B2B sales platform whose AI agent answers on real data and cites its sources.
Athleta 工程案例研究——一款 AI 健身产品，采用每用户一个知识图谱的设计，由 Agent 提议、用户确认。Engineering case study of Athleta, an AI fitness product with a per-user knowledge graph where the agent proposes and the user confirms.
PostgreSQL MCP 2026：面向开发者的最佳 AI 数据库访问工具PostgreSQL MCP 2026 Best AI Database Access Tool for Developers
fold：企业级 MCP gateway——在任意 MCP 客户端与 MCP server 之间提供统一的受控端点，支持联邦、鉴权、策略、限速、缓存与审计。fold: the enterprise MCP gateway — one governed endpoint between every MCP client and every MCP server. Federation, auth, policy, rate limiting, caching, audit.
Islam West Africa Collection 的 RDF 词汇表：面向 Omeka S 的 AI 处理溯源与按模型键控的情感标注属性RDF vocabulary for the Islam West Africa Collection: AI processing provenance and model-keyed sentiment annotation properties for Omeka S
对伊斯兰西非文献集（IWAC）语料库情感分析的交互式可视化，对比 ChatGPT、Gemini 与 Mistral，支持多语言与高级筛选。Interactive visualization of sentiment analysis on the Islam West Africa Collection (IWAC) corpus, comparing ChatGPT, Gemini, and Mistral with multilingual support and advanced filtering.
自主规约驱动的开发，通过确定性工程循环编排隔离的 Oh My Pi 编程 Agent。Autonomous spec-driven development, orchestrating isolated Oh My Pi coding agents through deterministic engineering loops.
IntentCoding 官方实现：放大代码生成中的用户意图（ACL 2026）Official implementation of IntentCoding: Amplifying User Intent in Code Generation (ACL 2026)
使用 vision-language model 处理视觉与文本数据，执行多模态推理与图像理解任务。Process visual and textual data with this vision-language model for multimodal reasoning and image understanding tasks.
Wenshu（文枢）— 面向人文社科研究的 AI 知识处理工作流：本地知识库 / RAG / 中文引文 / 理论谱系。AI knowledge workflow for humanities & social sciences: local knowledge base, RAG, citation (GB/T 7714), knowledge graph.
通过硬件 IP 核在硅层级抑制 LLM 幻觉，强制 LLM 输出的语义完整性Enforce semantic integrity in LLM outputs with a hardware IP core that suppresses hallucinations at the silicon level
基于 Agentic LLM 的系统综述数据提取。Agentic LLM-assisted data extraction for systematic reviews
截至 2026-08-10 的投机解码研究知识库：66 篇核心论文全文精读、方法谱系、系统比较与研究空白
开源、原生支持 Claude 的文献综述工作流工具：支持 arXiv/DOI/URL/PDF 论文导入、章节抽取，并通过 paper-qa 实现单篇论文问答Open-source, Claude-native literature-review workflow tool: arXiv/DOI/URL/PDF paper ingest, section extraction, single-paper Q&A via paper-qa.
Agentic RAG：回答前对每条引用与原文进行核对，证据不足时弃答。具备自纠正检索循环、确定性引用锚定以及 prompt injection 防御能力。Agentic RAG that verifies every citation against source text before answering, and abstains when the evidence does not hold. Self-correcting retrieval loop, deterministic citation grounding, prompt-injection defence.
即时评估课程资格，输出明确的通过/未通过结果以及定制化的入学测试。Instantly evaluate course eligibility with clear pass/fail results and tailored entry assessments.
基于 Whisper、Gemini、Streamlit、yt-dlp 与 FFmpeg 构建的 AI 应用，可即时转录并总结任意 YouTube 视频。Transcribe and summarize any YouTube video instantly with AI-powered app using Whisper, Gemini, Streamlit, yt-dlp & FFmpeg.
将 Google 文档当作本地文件操作的 MCP server——支持编辑、审阅建议、评论、标签页、多账号。MCP server to treat a Google Doc like a local file — edit, review suggestions, comments, tabs, multi-account.
RAMR —— 检索增强记忆可靠性：面向 Agentic-RAG / 记忆系统的抗污染合成基准（附方法与发现）。RAMR — Retrieval-Augmented Memory Reliability: a contamination-resistant synthetic benchmark for agentic-RAG / memory systems (findings + method)
基于 git 构建的可治理 multi-agent 框架。A governed multi-agent framework built on git
AI 编程 Agent 会话知识的无损压缩——先症状分诊、持久化捕获、冷启动验证Lossless compaction of session knowledge for AI coding agents — symptom-first triage, durable capture, cold-start verification
Syx 是一个面向长周期项目与编码 Agent 的实验性本地 AI 记忆系统，探索主动上下文、日记忆滚动回收、睡眠式记忆整合、长期 RAG 检索、类梦境综合以及 markdown 记忆制品Syx is an experimental local AI memory system for long-running projects and coding agents. It explores active context, daily memory rolloff, sleep consolidation, long-term RAG retrieval, dream-like synthesis, and markdown memory artifacts.
Namaste AI 🚀 由 Akshay Saini（NamasteDev 创始人）出品——笔记、经验与项目记录，探索 LLMs、RAG、AI agents、MCP 及 AI 驱动应用。#AI #LLM #RAG #MCPNamaste AI 🚀 by Akshay Saini (Founder of NamasteDev) — Notes, learnings, and projects — exploring LLMs, RAG, AI agents, MCP, and AI-powered applications. #AI #LLM #RAG #MCP
🚀 通过 autopack 简化 Hugging Face 模型的运行、分享与发布，自动完成量化与多格式导出🚀 Simplify running, sharing, and shipping Hugging Face models with autopack; it quantizes and exports to multiple formats effortlessly.
基于仿真的 MRI 调度策略分析，结合统计分析与离散事件仿真，以优化资源利用率、等待时间、加班时长与患者吞吐量。Simulation-based analysis of MRI scheduling policies, combining statistical analysis and discrete-event simulation to optimize resource utilization, waiting times, overtime, and patient throughput.
使用简洁的 markdown 工作空间（基于可检视的文件）管理持久化 agent 记忆，无需复杂框架。Manage durable agent memory using a simple markdown workspace with inspectable files instead of complex frameworks.