1272 个 · 应用
通过 MegaQwen CUDA megakernel 加速 Qwen3-0.6B 推理，在 RTX 3090 上达到 531 tok/s decode，较 HuggingFace 提升 3.9×🚀 Achieve faster Qwen3-0.6B inference with the MegaQwen CUDA megakernel, delivering 531 tok/s decode on RTX 3090—3.9x faster than HuggingFace.
基于已验证决策构建的共享 intelligence 层。A shared intelligence layer built from verified decisions.
面向 Agentic Dynamics 的实验工具：衡量 AI Agent 如何行为、恢复并产出已验证的结果Experimental instrument for Agentic Dynamics: measuring how AI agents behave, recover, and produce verified outcomes
从学术数据库中采集学术元数据，用于组织文献检索与构建研究参考目录。Harvest scholarly metadata from academic databases to organize literature searches and build research bibliographies.
基于 PRISMA 2020 的 LLM 微调技术系统综述（2020–2025）— 84 项研究，涵盖 LoRA/QLoRA/RLHF/DPO，本科毕业论文（PUCE）。PRISMA 2020 systematic review of LLM fine-tuning techniques (2020-2025) - 84 studies, LoRA/QLoRA/RLHF/DPO, undergraduate thesis (PUCE).
基于 LangGraph、LangChain、Groq 和 Tavily 构建的多步骤研究 Agent。分解复杂的研究问题，执行迭代式网络研究，评估证据，并生成结构化的最终报告。Multi-Step Research Agent built with LangGraph, LangChain, Groq, and Tavily. Decomposes complex research questions, performs iterative web research, evaluates evidence, and generates a structured final report.
项目名称：用于自动化文献综述与研究空白发现的自适应 AI 研究智能平台。Project Title: Adaptive AI Research Intelligence Platform for Automated Literature Review and Research Gap Discovery
混合 RAG 系统，具备 reranking、基于引用的 grounded citations，以及可选 provider-backed 执行的可确定离线评估分级。A hybrid RAG system with reranking, grounded citations, and deterministic offline evaluation tiers with optional provider-backed execution.
有界研究 Agent：plan → retrieve → critique，具备步骤预算、执行轨迹、引用追溯与自由路径 UI，无需 API key。Bounded research agent: plan → retrieve → critique, step budgets, traces, citations, free-path UI. No API key.
面向中英双语生物医学研究的术语循证、主张强度与科学写作审计 skill。Evidence-aligned terminology, claim-strength, and scientific-writing audit skill for Chinese and English biomedical research.
培育持久化数字生命的开放协议：协议、课程、研究问题、治理An open protocol for raising persistent digital beings: protocol, curriculum, research questions, governance
Subconscious Memory 2026：基于混合语义搜索与 MCP 的 AI 编码上下文引擎。Subconscious Memory 2026: AI Coding Context Engine with Hybrid Semantic Search & MCP
插入到 MCP 服务器前方的安全与可信代理——策略拦截、rug-pull 检测、JIT 审批、哈希链审计。124 个测试。MCP 서버 앞에 끼어드는 보안·신뢰성 프록시 — 정책 차단, rug-pull 탐지, JIT 승인, 해시 체인 감사. 124 tests
一款 Specs AR 眼镜，可读取房间风水、进行评分、用 AI 重新渲染，并给出整改清单。基于 CLAD（Claude + Lens Studio MCP）构建。A Specs AR Lens that reads your room's feng shui, scores it, repaints it with AI, and gives you a checklist to fix it. Built with CLAD (Claude + Lens Studio MCP).
📰 Spec-driven daily briefings on Claude Code cloud Routines — git as the only state, zero servers, zero API keys | 用 Claude Code Routines 每天自动生成主题简报，git 即全部状态，零服务器零密钥
向 Mac 上的 AI 编码 agent 展示 iOS 应用的实际运行状态——屏幕画面、应用内部状态、网络流量与日志，让检查 UI 变更不再依赖手动点击和截图循环。Shows what your iOS app is actually doing — the screen, the app's own state, its network traffic and its logs — to an AI coding agent on your Mac, so checking a UI change stops being a manual tap-and-screenshot loop.
关于 AI Agent 系统与编排的定向研究与文献综述的最终交付成果Final deliverables for directed studies and literature review on AI Agent Systems and Orchestration
推进并系统评估 BV-BRC Copilot，从研究问题和已发表方法生成、执行并复现生物信息学工作流。Advancing and systematically evaluating BV-BRC Copilot for generating, executing, and reproducing bioinformatics workflows from research questions and published methods.
用于生成文献综述的全栈系统，基于导入的数据构建，用户可自由选择 LLM。A full stack system use to generate literature review base on dataa imported, users can choose between llms.
Yaroslav Vasylenko —— AI 系统、验证工程、可复现研究与 agentic workflows。Yaroslav Vasylenko — AI systems, verification engineering, reproducible research, and agentic workflows.
学术研究引用管理与论文工具——即时获取洞察，不打断你的专注。立即下载，5 分钟内即可上手运行。Academic Research Citation Manager And Paper — instant insights without breaking your focus. Download now and be running in under 5 minutes.
A/B 测试结果分析，涵盖整体指标评估与精细用户分群。项目结合了变更对结账漏斗各阶段影响的研究、统计假设检验以及不同群体的关键指标评估。Analysis of A/B testing results, covering both an overall assessment of metrics and detailed user segmentation. The project combines research into the impact of changes on the stages of the checkout funnel, testing of statistical hypotheses, and evaluation of key metrics across different groups
Adobe Experience Manager 的 W3C WebMCP 集成 — 通过 navigator.modelContext 让浏览器 AI agent 可发现并调用 AEM Core Components。W3C WebMCP integration for Adobe Experience Manager - makes AEM Core Components discoverable and callable by browser AI agents via navigator.modelContext
基于证据的论文检索与推荐 Agent，面向复杂研究问题。Evidence-grounded paper search and recommendation agent for complex research questions.
CELLO protocol 的官方客户端——提供 MCP server 及面向 OpenClaw、NanoClaw、IronClaw、ZeroClaw 等 agent 变体的原生适配器。The official client for the CELLO protocol — MCP server and native adapters for OpenClaw, NanoClaw, IronClaw, ZeroClaw, and other agent variants
基于多 Agent LLM 工作流的智能 AI 学术研究助手，可自动化完成学术论文发现、验证、文献分析、对比矩阵（含表格）、RAG 驱动的研究对话，并产出可直接用于稿件的文献综述An agentic AI academic research assistant that automates academic paper discovery, validation, literature analysis, comparison matrices with table, RAG-powered research chat, and manuscript-ready literature reviews using multi-agent LLM workflows.
完全在浏览器中通过 WebAssembly 实现文档转换、检查、OCR 与翻译。保留版式的翻译，全程本地、离线优先。Convert, inspect, OCR, and translate any document entirely in the browser via WebAssembly. Layout-preserving translation, fully local, offline-first.
基于 LangGraph、LangChain、Groq、ChromaDB 与 Streamlit 构建的多 Agent AI 研究助手，通过联网检索、获取 arXiv 论文、利用 RAG 索引 PDF 并生成有研究依据的回答，实现文献综述自动化Multi-agent AI Research Assistant built with LangGraph, LangChain, Groq, ChromaDB, and Streamlit. Automates literature review by searching the web, retrieving arXiv papers, indexing PDFs with RAG, and generating research-backed answers.
基于 n8n、Groq（Llama 模型）、Node.js、React 和 PostgreSQL 构建的多 Agent AI 研究流水线。用户提交研究问题后，由规划 Agent 拆解，由专业的研究/技术/成本 Agent 并行调查，由事实核查 Agent 验证，最终合成为带来源的结构化报告。A multi-agent AI research pipeline built with n8n, Groq (Llama models), Node.js, React, and PostgreSQL. A user submits a research question, which is broken down by a planning agent, investigated in parallel by specialized research/technical/cost agents, verified by a fact-checking agent, and synthesized into a structured report with sources.
面向 AI 饮食评估研究的 3 周入门计划：文献综述、分割、LLM 与 RAG。3-week onboarding plan for AI dietary assessment research: literature review, segmentation, LLMs, and RAG
面向长生命周期 Agent 与有状态负载的会话中心计算：暂停即保留记忆，按需恢复。Session-centric compute for long-lived agents and stateful workloads. Pause with memory intact; resume on demand.
面向有据可循的 Agent 工作流的 TypeScript 参考实现，支持混合检索、人工审核与可回放的审计追踪。TypeScript reference implementation for grounded agent workflows with hybrid retrieval, human review, and replayable audit trails.
NLP 案例研究NLP Case Study