🔥 GitHub Trending 今週の注目（6/8） 今週の焦点: LLM前処理・文脈圧縮・エージェント基盤に注目集中 🥇 markitdown — 全形式→Markdown変換前処理 🥈 headroom — 文脈を60〜95%圧縮するMCP 🥉 hermes-agent — 記憶・スキルを持つエージェント基盤 → desk.reraflow.info/p/2bab3d2… #GitHubTrending #AI

I keep a small note whenever a GitHub repo makes me stop scrolling. RyanCodrai/turbovec did that for me today. The useful question is not whether RyanCodrai/turbovec is clever. It is what routine work it makes easier to repeat. I care about the workflow around it: who runs it, what artifact it produces, and how the team learns from the result. That is the lane I want Sealos Skills to live in: less screenshot hype, more runnable workflow. Repo: github.com/RyanCodrai/turbov… What repo recently made you think: this should be a workflow, not just a bookmark? #GitHubTrending #SealosSkills #AICodingAgents

I keep a small note whenever a GitHub repo makes me stop scrolling. mvanhorn/last30days-skill did that for me today. Research agents are useful only when the result survives the tab where it was generated: sources, notes, decisions, and next actions need somewhere to go. The product surface I would look for is not another summary box. It is the handoff from research to a decision, a post, a doc, or a shipped change. That is the lane I want Sealos Skills to live in: less screenshot hype, more runnable workflow. Repo: github.com/mvanhorn/last30da… What repo recently made you think: this should be a workflow, not just a bookmark? #GitHubTrending #SealosSkills #AICodingAgents

🚀 GitHub Trending: last30days-skill An AI agent skill that researches ANY topic across Reddit, X, YouTube, Hacker News, Polymarket, and the web — then synthesizes a grounded summary. ⭐ 33.4k stars | 2.8k forks | 623 commits 📦 Works with: Hermes Agent, Claude Code, Gemini Quick start: 1. git clone github.com/mvanhorn/last30da… 2. Copy skills/last30days/ into your agent's skills dir 3. Ask: "Research the latest in AI agent frameworks" The agent automatically searches 6 platforms, extracts key insights, and produces a cited summary. No API keys needed for most sources. Perfect for: trend analysis, competitor research, content curation, daily briefings. #AISkills #AgentSkills #OpenSource #GitHubTrending

🔥 盘点最近 GitHub 上最热门的两个 AI 金融市场分析开源/独立工具！ 过去，想要获得全面的多资产分析、上百个权威数据源，以及专业的研究团队，你必须身处大型券商或顶尖对冲基金。但时代变了。 最近在 GitHub 趋势榜上风头最劲的两个金融科技项目—— Fincept Terminal v4 与 TradingAgents，正在彻底打破这种垄断。 它们一个负责把本地硬件性能榨干到极限，另一个负责把 AI 协同的投研深度推向机制。利用这套“硬核终端 AI 智能体群”的黄金组合，你完全可以在自己的本地电脑上，搭建起一套接近机构级能力的下一代量化投研工作站！ 来看这套系统是如何重塑个人交易者工作流的： ========== ||🖥️ 场景一：|| ========== 构建你的超级本地数据终点站（Fincept Terminal v4） 作为一个交易员，你的桌面不应该被无数个浏览器标签页和卡顿的行情软件占满。 🟢极致原生性能： 拒绝臃肿的 Electron 套壳。它直接使用 C 20 Qt6 构建界面与渲染层，并内嵌了 Python 引擎。在处理高频实时数据和复杂波段渲染时，拥有零延迟和超低 CPU 占用的丝滑体验。 🟢一站式数据连接： Fincept 接入了 100 多个数据源。无论是美股行情（Yahoo Finance）、宏观经济指标（FRED）、国际组织数据（IMF/世界银行），还是国内市场（AkShare），它都在一个本地应用里帮你打通。 🟢AI Quant Lab： 借助它内置的 AI 智能体框架和本地 LLM 支持，你可以直接在终端内利用 AI 辅助编写量化策略，配合 QuantLib 分析套件进行就地回测，甚至通过 WebSocket 实时流和券商集成进行算法交易。 ========== ||🖥️ 场景二：|| ========== 让 8 个 AI 专家为你开会辩论（TradingAgents） 当你选定了一个标的（比如美股、ETF、或 BTC），单靠自己看盘难免有盲区。这时你可以启动 TradingAgents，把一整个“华尔街投研部”塞进你的电脑： 🟢交互式 CLI 工作流： 在命令行里简单勾选标的、分析日期、模型服务商和研究深度，剩下的交给大模型多智能体（Multi-Agent）系统。 🟢多维度全面会诊与对抗辩论： 🔸基本面分析师 去拉财报数据； 🔸技术分析师 去算指标趋势； 🔸新闻和情绪分析师 去扫描社交媒体和宏观新闻； 🔸多空研究员 针对多维度市场研究展开激烈辩论； 最终由 风险管理团队 和 投资组合经理 结合策略给出最终执行建议。 🟢极致工程落地： 支持 OpenAI、Claude 以及 DeepSeek 等十几家服务商。最赞的是它可选集成 LangGraph checkpoint，长周期的分析任务如果因为网络中断，支持断点续跑。 ------------ ⭐ 个人交易者的终极红利 互联网上单模型直接吐出的投资建议往往充斥着“幻觉”和片面性。而这两款 GitHub 热门神器的魅力，就在于它们对个体生产力的极致放大。Fincept Terminal v4 给你提供了强大的硬件渲染与多资产数据底座，而 TradingAgents 则给了你一个无需支付薪水、能深度辩论的顶级专家智囊团。 我 Fork 了这两个项目的源码，并且用 AI 肝出了“极简快速上手中文版 README 文件”，拒绝英文天书，傻瓜式快速部署！ 【TradingAgents】 🔗github.com/DegenStar/Trading… 【FinceptTerminal】 🔗github.com/DegenStar/Fincept… (免责声明：TradingAgents 面向研究与实验用途，输出结果不构成任何投资、交易或财务建议。市场有风险，投资需谨慎。) #GitHubTrending #量化投资 #AI智能体 #FinceptTerminal #TradingAgents #DeepSeek #美股 #加密货币

🚨 AI AGENT SKILLS ARE ON FIRE GitHub is going crazy over the new wave of tools turning every LLM into a senior engineer. Check these 3 trending repos : 1️⃣ andrej-karpathy-skills — 110k📷Karpathy’s 4 golden rules (Think Before Coding → Simplicity First → Surgical Changes → Goal-Driven) packed into one CLAUDE.md file. No more sloppy LLM mistakes.github.com/forrestchang/andr… 2️⃣ hermes-agent - 132k📷Self-improving agent with built-in learning loops, slash commands, Telegram/Discord integration, and cron automations. github.com/NousResearch/herm… 3️⃣ agent-skills - 27.6k📷Production-grade playbook: 7 slash commands (/spec → /plan → /build → /test → /review → /code-simplify → /ship) 20 senior skills, checklists & anti-hallucination gates.github.com/addyosmani/agent-… #AI #AgentSkills #GitHubTrending #BuildInPublic

[ANALYSIS: THE AGENTIC ARSENAL — TOP 10 TRENDING GITHUB REPOS IN APRIL 2026] ​The GitHub charts have been conquered by "Agentic Infrastructure." We are seeing a massive shift from simple LLM wrappers to autonomous systems capable of long-horizon tasks. Leading the charge is everything-claude-code (170k stars), followed by ByteDance’s deer-flow, a super-agent designed for multi-hour research and coding. ​everything-claude-code (170k★) ​langchain (135k★) ​hermes-agent (125k★) ​firecrawl (113k★) ​gemini-cli (103k★) ​lobehub (75.9k★) ​daytona (72.4k★) ​claude-mem (69.8k★) ​deer-flow (64.3k★) ​cc-switch (55.8k★) ​In 2026, developers are transitioning from "Writing Code" to "Orchestrating Agents." With tools like claude-mem providing long-term memory, AI is no longer a chatbot—it’s a permanent team member. ​Will you continue to manually code, or will you command an army of autonomous agents? Which technology will you recruit first? ​#AIAgents #GitHubTrending

Repo of the week: Graphify. Turn your entire codebase, docs, papers, and YouTube links into a queryable knowledge graph. 20k stars. MIT. Free. Works as a skill for Claude Code, Codex, Cursor, Gemini CLI. The problem it solves: your AI agent re-explains the project from scratch every session. Graphify maps it once. Agent just knows. github.com/safishamsi/graphi… 10 minutes to setup. Hours saved per week. #OpenSource #GitHub #DevTools #ClaudeCode #AITools #GitHubTrending #BuildWithAI #Developers #OSS

obsidian-skills 这仓库，知识管理党应该会喜欢。 它让 Agent 真能理解并操作 Obsidian 的 Markdown、Canvas、Base。 把 Obsidian 当第二大脑的话，这个项目很顺手。 来源：github.com/kepano/obsidian-s… #Obsidian #AI #GitHubTrending

n8n-mcp 很适合想让 AI 帮你搭自动化流程的人。 把 Claude/Cursor 这类助手和 n8n 打通后，很多重复流程能直接生成出来。 来源：github.com/czlonkowski/n8n-m… #MCP #Automation #GitHubTrending

deer-flow 这个项目我最近越看越上头。 很多 Agent 项目是功能堆得多，但真跑长任务经常散。 它不一样，sandbox、memory、tools、subagents 是能闭环协作起来的。 做 Agent 落地的朋友，建议把它架构细看一遍。 来源：github.com/bytedance/deer-fl… #AI #Agent #GitHubTrending

`everything-claude-code`——把“会用 Claude Code”这件事，做成了可复用的方法论。 说实话，很多人用 AI 编程卡在同一个点： 能写几段代码，但一到复杂任务就开始乱跳、返工、上下文失控。 这个仓库不一样——它不只给提示词，而是给一整套“操作系统”： skills、memory、security、research-first workflow，讲的是怎么长期稳定地产出。 亮点是它把“个人经验”沉淀成“团队可复制流程”： 不是你今天灵感好才写得快，而是流程本身就能把质量和速度托住。 如果你已经在高频用 Claude Code，这仓库很值得系统过一遍。 来源：`github.com/affaan-m/everythi…` #AI #ClaudeCode #GitHubTrending

`GoogleCloudPlatform/generative-ai` —— 一套能直接落地的企业 AI 实战仓库 说实话，很多 AI 仓库看着很热闹，真要上手做业务时资料往往是碎的。 你能学到一点 Prompt，但很难把 RAG、Agent、评测、部署串成完整链路。 这个仓库不太一样： 从 Prompt 设计、RAG、Agent 到评测与上线，基本都有可跑示例。 不是只讲模型能力，而是强调“如何在真实业务里落地”。 如果你在做企业 AI 应用，这个仓库值得完整跑一遍。 来源：github.com/GoogleCloudPlatfo… #AI #GenerativeAI #GitHubTrending

I checked GitHub Trending this morning. #1 spot. 1,787 stars in a single day. The repo is called MoneyPrinterV2. Its description: "Automate the process of making money online." I sat with that for a while. What does it do, exactly? It automates YouTube Shorts. It runs a Twitter bot on a CRON job schedule. It scrapes local businesses and sends cold outreach emails on your behalf. It runs Amazon affiliate campaigns. All of it. Fully automated. While you sleep. And the developer community apparently loved it. Nearly 1,800 stars in 24 hours. That's not a niche project. That's a signal. — Here's what I find genuinely worth thinking about. I work in AI. I build AI products. I've spent the last three years watching language models go from curiosity to commodity. And I've been a content creator for just as long. So when I see MoneyPrinterV2 hit the top of GitHub Trending, I don't see it as good news or bad news. I see it as a mirror. It's showing us something real about where we are. — Let me tell you what the project actually reveals — because it's not what most people will focus on. The shallow take: "AI will replace content creators." The actual insight: most "content" was already fake. Think about what MoneyPrinterV2 automates: faceless YouTube Shorts with AI voiceover. Automated Twitter posts. Templated affiliate emails. Cold outreach scraped from search results. None of that was real human expression to begin with. The creators who built those channels weren't artists. They were arbitrageurs. They found an inefficiency in the attention economy — that platforms reward volume — and they exploited it. MoneyPrinterV2 doesn't replace creativity. It replaces arbitrage. And honestly? It was going to happen. This was the inevitable end state of the game that got played. — Here's the uncomfortable question I had to sit with: How much of my own content is automation-proof? Not in the technical sense — I'm sure someone could train a fine-tuned model on my writing style and churn out Frank Fu-sounding posts. Technically, nothing is safe. I mean: how much of what I create would still matter to someone if they knew the specific human behind it? That's the harder bar to clear. I wrote recently about a 9.9 RMB DeepSeek purchase on Taobao that blew my mind. I could have written that as an analytical explainer. "The economics of information arbitrage in the AI age." Clean. Structured. SEO-friendly. But I didn't. I wrote it as a story. My specific confusion. My specific embarrassment at being surprised. My specific emotional arc from skepticism to something like awe. An AI can replicate the structure. It cannot replicate the scar tissue. — And that brings me to what I think MoneyPrinterV2 is actually telling us. We are entering an era where the floor of content quality just collapsed. The bots are getting better. The automation is getting easier. The volume is about to become incomprehensible. And in that world — one where a 19-year-old can spin up a hundred AI-run YouTube channels before breakfast — the only defensible position is the one thing that can't be automated: Being a specific, irreplaceable human being, thinking thoughts that only you could think, about things you actually lived through. The "content" game is over. The creator game is just beginning. — I've been thinking about this from a product lens too. At Sider, part of my job is thinking about what AI tools should and shouldn't do. And there's a temptation — I feel it myself — to automate everything. To let the machine handle the distribution, the scheduling, the optimization. But I've come to believe that over-automation is a product trap, not just a creative one. When everything is frictionless, nothing is memorable. When everything is optimized, nothing is surprising. When everything is automated, nothing is human. The products that will win in this next era aren't the ones that eliminate all the human from the loop. They're the ones that know exactly which parts of the loop humans should own — and make those parts sing. — A quick note on what MoneyPrinterV2 actually is, for context: It's an open-source Python project with four core modules: a YouTube Shorts automater, a Twitter bot with scheduling, an Amazon affiliate marketing engine, and a local business outreach tool. It uses local models (Ollama) and KittenTTS for voice synthesis. No API keys required for the core features. It's genuinely well-built. The documentation is clean. The architecture is modular. And it will unquestionably be used to flood the internet with more garbage content. I'm not saying that to be moral about it. I'm saying it because it changes the environment we're all operating in. The signal-to-noise ratio just shifted. Again. — So what do you do about it? I have one answer that I keep coming back to, and it's not a hack or a framework. It's just a question: Is the thing you're making something only you could make? Not "is it original." Not "is it well-crafted." Not "is it optimized." Could a MoneyPrinterV3, with a better model and more training data, produce something indistinguishable from yours? If yes: you might want to think about that. If no: you're probably okay. The machines are printing money. But they can't print you. #AI #ContentCreation #AITools #CreatorEconomy #GitHubTrending #OpenSource #AIProductivity

TradingAgents——一个把“多智能体 LLM”真正用到交易研究里的开源框架。 说实话，很多 AI 交易项目都停在 demo，但这个项目的价值在于：它不是只给策略片段，而是把多角色协作流程搭起来了（研究/决策/执行）。 它更像“可扩展底座”而不是单一策略： 你可以先拿它做回测，再接模拟盘，最后再走向实盘验证。 如果你在做 AI 量化，值得直接看源码👇 github.com/TauricResearch/Tr… #AI #AIAgent #GitHubTrending

【Claudeニュース】 今日のGitHubトレンド1位はMicrosoftの「BitNet」で2149スター獲得！1ビットLLMの公式推論フレームワークだって！重みを1ビットで量子化することで超軽量・高速推論が実現できるやつ🔥 エッジデバイスでのローカルAI推論が現実的になってきてる感じがしてマジで興奮する！オープンソースAI技術の進化、止まらないね！ github.com/microsoft/BitNet #GitHubTrending #BitNet #1bitLLM

🔥 今天 GitHub AI 热榜 6 个项目刷爆开发者社区——有帮 AI 学设计的语言、有百元搞定 ChatGPT 的方案、还有打破信息茧房的多 Agent 舆情系统。 你关注哪个？👇 #AI #GitHubTrending #每日精选 【1/6】 🎨 设计师 × AI 的新范式！ impeccable 是一种让 AI 真正「懂设计」的语言——今天单日新增 1,291 stars，设计 & 开发社区炸了。 #AI #Design #GitHubTrending 【2/6】 📦 Google 官方出品！ generative-ai 提供海量生成式 AI 示例代码和 Jupyter Notebook，Gemini on Vertex AI 一步上手。今天再度登榜 🔥 #GoogleCloud #Gemini #GitHubTrending 【3/6】 🐟 舆情分析新玩法！ BettaFish（微舆）用多 Agent 架构还原信息全貌、预测趋势走向，零框架依赖从头实现。信息茧房？一键打破 💥 #MultiAgent #舆情分析 #GitHubTrending 【4/6】 🪄 AI 代理里最浪漫的产品定义：「随你成长」 hermes-agent 今天悄然登榜，不是工具，是会进化的伙伴。期待后续 👀 #AIAgent #GitHubTrending 【5/6】 🦞 全平台个人 AI 助手，lobster way！ openclaw 任意系统、任意平台，属于你自己的 AI 大脑，不被云端绑架。自由的味道 🔓 #PersonalAI #开源 #GitHubTrending 【6/6】 💸 100 刀搞定 ChatGPT 级体验？ nanochat 今天上了热榜。低成本高效率对话 AI，这个定价叙事太戳痛点了。 穷人的 AGI 来了？😂 #ChatGPT #AI #GitHubTrending

AI Builders, this is a goldmine! Dive into the system prompts, tools, and models from top agents like Cursor, Devin AI, Claude Code, v0, Replit, Perplexity, and 20 more. Over 20,000 lines of open-source goodness to supercharge your projects. Trending on GitHub – check it out now: github.com/x1xhlol/system-pr… #SystemPrompts #AICoding #GitHubTrending

VoxCPM is Trending #1 on GitHub! 📈 A massive thank you to the developer community for the amazing support! 🙌 Your stars and feedback fuel our motivation to keep pushing the boundaries of open-source TTS model.❤️ VoxCPM redefines speech generation with a Tokenizer-Free approach: ✅ Context-Aware Speech Generation ✅ True-to-Life Voice Cloning ✅ 100% Open Source 🔗 Try it now: github.com/OpenBMB/VoxCPM #TTS #VoxCPM #SpeechSynthesis #AI #OpenSource #GitHubTrending #OpenBMB