04-26 16:00 · 科技,GitHub
AI-powered content pipeline: trend discovery → research → social publishing. Built with Claude Agent SDK + MCP.
AI-powered content pipeline: from trend discovery to social publishing.
Built with Claude Agent SDK + MCP (Model Context Protocol), featuring 3 integrated MCP servers for real-time trends, web scraping, and research automation.
Three clear entry points — no complex menus, just pick and go:
Watch Claude work in real-time — tool calls shown as collapsible blocks with MCP server badges:
- trend-pulse (green) — 20 real-time trend sources, zero auth
- cf-browser (blue) — Cloudflare Browser Rendering for JS pages
- notebooklm (purple) — Research + artifact generation (podcast/slides/video)
- studio — Built-in publishing + history tools
Full markdown rendering with syntax highlighting, clickable file path previews, cost/duration tracking, and inline images:
Full support for Traditional Chinese, English, and Japanese — all UI, system prompts, and pipeline cards adapt:
Built-in Threads publishing via native fetch()
— no Python dependency. Every post is checked against Meta's patent-based 5-dimension scoring:
Quality gates: Overall >= 70, Conversation Durability >= 55.
Supported post types: text, image, video, carousel (2-20 items), poll, GIF, link preview, text attachment, spoiler (media blur + text), ghost (24hr ephemeral), quote post, reply control, topic tag, alt text, link-comment auto-reply.
- Multiple Threads accounts with independent personas
- Per-account style (e.g., "tech-educator", "futurist")
- Per-account persona prompt for tone adaptation
- Matrix publishing: same topic → unique content per account
Cron-based task scheduling with per-account targeting:
- Define prompt templates with cron schedules
- Target specific social accounts
- Quality gate enforcement (min score threshold)
- Auto-publish or manual review mode
- Execution history with cost/duration tracking
- Reduced tool set for unattended safety (no Bash/Read/Write)
NotebookLM integration for auto-generating visuals:
- Image cards — slides PDF as single-page visual cards
- Carousel — multi-page slides split into carousel images
- Podcast — AI-generated audio discussion
- Video — slides + podcast combined via ffmpeg
- Mind maps, reports, flashcards, study guides
All downloads stay within the workspace directory.
Browse and preview workspace files directly in the UI:
- Directory tree with depth control
- Text file preview (syntax-highlighted)
- Binary file preview (images, PDFs, audio, video)
- Clickable file paths in chat responses
- Workspace-contained — no access outside project folder
Content integrity enforced at the system level:
- Read original sources — Never write from titles/metadata alone. 1+ primary source per topic, 2+ for controversial.
- Timeline verification — Every fact gets a verified timestamp. Time words mapped by age.
- No AI filler — System prompt blocks generic phrases ("in today's world" / "it's worth noting").
- Multiple concurrent sessions with independent workspaces
- Session resume on app restart (conversation history preserved)
- Idle session eviction (30min TTL)
- Query cancellation via interrupt button or CLI
System/light/dark theme with persistent preference across sessions.
Full CJK input method support — Enter during Chinese/Japanese character composition does not trigger send. Shift+Enter for newline.
- AI agent operates within session workspace only
- No access to ~/Downloads, ~/Desktop, ~/Documents, or system paths
- Credentials stored in local SQLite — never searched from filesystem
- Publishing via built-in MCP tool only (no external scripts)
- Session workspace validated at creation (blocks system paths)
- File API restricted to workspace root (symlink-safe realpath check)
┌─────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ React UI │────▶│ Express + WS │────▶│ Claude Agent │
│ (Vite) │◀────│ Server │◀────│ SDK │
└─────────────┘ └──────────────────┘ └────────┬────────┘
│
┌──────────────────────────┤
│ │ │
┌──────▼──┐ ┌──────▼───┐ ┌─────▼──────┐
│ trend- │ │ cf- │ │ notebooklm │
│ pulse │ │ browser │ │ │
│ (MCP) │ │ (MCP) │ │ (MCP) │
└─────────┘ └──────────┘ └────────────┘
20 sources Cloudflare Podcast/Slides
zero auth Browser /Video/Report
- Node.js >= 18
- Claude Code CLI installed and authenticated (
npm install -g @anthropic-ai/claude-code
) - Python 3.10+ (for MCP servers — or use
uvx
for zero-config setup)
Five ways to install — pick the one that fits your workflow:
Download the latest .dmg
from GitHub Releases, open it, and drag to Applications.
- Double-click to launch — server starts automatically
- No terminal, no Node.js install needed
- MCP servers auto-detected via uvx (install uv first)
- All desktop features included: Web UI + MCP + Publishing
Requires: macOS (Apple Silicon). Intel Mac build available on request.
npm install -g @claude-world/studio
# Set up MCP servers (one-time, auto-cached via uvx)
npx @claude-world/studio setup-mcp
# Start
studio serve
# Web UI: http://localhost:5173
# API: http://127.0.0.1:3001
After install, both studio
and claude-world-studio
commands are available
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04-26 16:00 · 财经,财经
【长江投资：因触及财务类退市指标 将被实施退市风险警示】长江投资(600119.SH)公告称，公司2025年度经审计的净利润为-4595.15万元，扣非后净利润为-3264.64万元，且扣除与主营业务无关及不具备商业实质的收入后的营收为2.03亿元。
长江投资(600119.SH)公告称，公司2025年度经审计的净利润为-4595.15万元，扣非后净利润为-3264.64万元，且扣除与主营业务无关及不具备商业实质的收入后的营收为2.03亿元。因最近一个会计年度经审计的净利润和扣非后净利润孰低者为负值，且扣除后的营收低于3亿元，触及上交所股票上市规则规定的退市风险警示情形。公司股票将于4月27日停牌一天，4月28日起实施退市风险警示，证券简称变更为“*ST长投”，日涨跌幅限制为5%。公司董事会表示将采取提升盈利能力、处置低效资产等措施争取撤销风险警示。
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04-26 16:00 · 开源项目,AI内容生成,自动化流程,趋势追踪,技术威胁
-
Research trending topics and generate ready-to-post social media content — as a Hermes/OpenClaw skill.
trendingcontent is a two-phase AI agent skill:
- Research phase — scrapes Reddit, X/Twitter, YouTube, TikTok, HackerNews, Bluesky and Brave Search to surface the most engaging content about your topic over a configurable time window (7, 15, 30, or up to 90 days).
- Content generation phase — uses the research findings to generate platform-specific social media copy: Twitter/X threads, LinkedIn posts, and Instagram captions.
Built on top of last30days by @mvanhorn, extended with:
- Variable time windows (not just 30 days)
- Multi-platform content generation (Twitter, LinkedIn, Instagram)
- Tone control (professional, casual, educational, viral)
- Multilingual output (English, Spanish, Portuguese)
- Hermes/OpenClaw skill standard compatibility
git clone https://github.com/gabogabucho/trendingcontent-agent.git
cd trendingcontent-agent
pip install -r requirements.txt
export SCRAPECREATORS_API_KEY="your_key" # required
export BRAVE_API_KEY="your_key" # optional but recommended
Or add them to your .env
file.
Get a ScrapeCreators API key at scrapecreators.com. Get a Brave Search API key at brave.com/search/api.
python scripts/trendingcontent.py "artificial intelligence" --days=7 --platform=all
python scripts/trendingcontent.py <topic> [options]
Options:
--days=N Time window: 7-90 days (default: 30)
--platform twitter | linkedin | instagram | all (default: all)
--tone professional | casual | educational | viral (default: professional)
--lang en | es | pt (default: en)
--sources=S1,S2 Only use these sources (reddit,twitter,youtube,tiktok,bluesky,hackernews,brave)
--disable=S1,S2 Disable specific sources
--list-sources Show all available sources
--quick Fewer sources, faster
--deep More sources, thorough
--research-only Output research only, skip content generation
# AI trends last 7 days → all platforms
python scripts/trendingcontent.py "artificial intelligence" --days=7
# Web3 → LinkedIn post in Spanish, professional tone
python scripts/trendingcontent.py "web3" --days=15 --platform=linkedin --lang=es
# Climate tech → viral Twitter thread
python scripts/trendingcontent.py "climate tech" --days=30 --platform=twitter --tone=viral
# Niche topic — only Reddit and YouTube (no HackerNews)
python scripts/trendingcontent.py "sourdough bread" --sources=reddit,youtube,brave
# Disable sources that don't fit your topic
python scripts/trendingcontent.py "fintech" --disable=hackernews,tiktok
# See all available sources
python scripts/trendingcontent.py --list-sources
# Research only, no content
python scripts/trendingcontent.py "LLMs" --days=14 --research-only
This repository follows the Hermes skill standard. To install as a skill:
# Copy to your Hermes skills directory
cp -r . /path/to/hermes-agent/skills/social-media/trendingcontent/
# Add API keys to your Hermes .env
echo "SCRAPECREATORS_API_KEY=your_key" >> ~/.hermes/.env
echo "BRAVE_API_KEY=your_key" >> ~/.hermes/.env
The agent will automatically discover the skill via SKILL.md
.
═══════════════════════════════════════════════
TRENDINGCONTENT — CONTENT GENERATION BRIEF
═══════════════════════════════════════════════
Topic: artificial intelligence
Period: Last 7 days
Language: English
Tone: Professional
Platforms: Twitter, LinkedIn, Instagram
───────────────────────────────────────────────
RESEARCH SUMMARY
───────────────────────────────────────────────
[ranked trending content with titles, scores, URLs]
───────────────────────────────────────────────
CONTENT INSTRUCTIONS
───────────────────────────────────────────────
[platform-specific format specs + tone guidance]
- Python 3.9+
yt-dlp
— YouTube transcript extractionrequests
— HTTP calls- ScrapeCreators API key (required)
- Brave Search API key (optional)
- Original research engine: last30days-skill by @mvanhorn — MIT License
- Social media generation layer & Hermes skill packaging: @gabogabucho
MIT License — see LICENSE for details.
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04-26 15:59 · 财经,财经
【消息人士：白宫记协晚宴安全事件嫌疑人作案目标为美政府官员】当地时间4月26日凌晨，美国哥伦比亚电视台援引两名消息人士的话报道称，在25日晚发生的美国白宫记协晚宴安全事件中的涉案嫌疑人已向美执法当局供述，其作案目标为美国政府官员。
当地时间4月26日凌晨，美国哥伦比亚电视台援引两名消息人士的话报道称，在25日晚发生的美国白宫记协晚宴安全事件中的涉案嫌疑人已向美执法当局供述，其作案目标为美国政府官员。
更多阅读
当地时间25日晚，因美国白宫记者协会晚宴突发安全事件，参加晚宴的美国总统特朗普及其夫人梅拉尼娅以及一众与会美国高级官员从该活动现场紧急疏散。事件发生后不久，美国总统特朗普在白宫新闻简报室举行新闻发布会。除了通报有关本次安全事件的部分细节，特朗普还在发布会上将此次遭遇的事件与正在推进的白宫宴会厅项目挂钩，并强调需要达到“一种前所未有的安保级别”。
特朗普在发布会上称，当晚一名男子手持多种武器，试图强闯安检关卡，随后被几位特勤局特工制服。一名特勤局特工遭到枪击，但因穿了防弹背心而幸免于难。
在被问及枪击事件是否与美伊战事有关时，特朗普表示：“我不这么认为。”特朗普认为，这名嫌疑人是“独狼行动者”，但同时表示还需要调查。
白宫记者协会成立于1914年，是一个独立组织，由报道白宫和美国总统的记者组成。该协会从1921年开始举行年度晚宴，是媒体与政府官员加强沟通的社交活动，总统出席并发表讲话一度成为惯例。（央视新闻）
（文章来源：界面新闻）
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04-26 15:59 · 财经,财经
【中信尼雅：股票可能被实施退市风险警示】中信尼雅(600084.SH)公告称，因审计机构基于谨慎性原则调减部分收入，公司预计2025年净利润为-2600万元，扣非后净利润为-3000万元，扣除与主营业务无关及不具备商业实质的收入后的营业收入为1.17亿元，低于3亿元。
中信尼雅(600084.SH)公告称，因审计机构基于谨慎性原则调减部分收入，公司预计2025年净利润为-2600万元，扣非后净利润为-3000万元，扣除与主营业务无关及不具备商业实质的收入后的营业收入为1.17亿元，低于3亿元。根据上交所规则，若最近一个会计年度净利润等指标为负且营收低于3亿元，公司股票将被实施退市风险警示。因此，公司股票在2025年年报披露后可能被实施退市风险警示(冠以“*ST”字样)。若触及该情形，公司股票将于年报披露日起停牌，并在被实施退市风险警示前一个交易日公告，复牌之日起正式实施。目前年报审计工作仍在进行中，具体数据以正式披露为准，敬请投资者注意投资风险。
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04-26 15:59 · 科技,GitHub
全球技术热点，AI 精选速递。（Global tech trends, fast-picked by AI）
全球技术热点，AI 精选速递。
聚合 GitHub Trending、Hacker News、Product Hunt，用 AI 精选和解读全网最值得关注的技术项目。
Trending AI 是一款使用 Kotlin Multiplatform (KMP) 构建的跨平台应用。聚合 GitHub Trending、Hacker News、Product Hunt 三大技术信息源，通过 AI 智能摘要和每日精选，帮你高效发现全网最值得关注的技术项目。
- 🌐 多源聚合：GitHub Trending、Hacker News、Product Hunt 一站浏览。
- 🤖 AI 智能摘要：每个项目自动生成 AI 分析，快速了解核心价值。
- ⭐ 每日精选：AI 从全网筛选最值得关注的技术项目。
- 📅 历史回溯：查看任意日期的趋势榜单，不错过每一天。
Kotlin Multiplatform | Compose Multiplatform | Ktor | Material 3
本项目采用 MIT 协议开源 - 详情请参阅 LICENSE 文件。
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04-25 13:57 · 开源,AI工具,文档处理,PDF编辑,OCR
Download Acrobat AI Assistant PDF Editor Desktop Windows 11 Acrobat DC 2026 Full Version Electronic Signature Tool OCR PDF to Word Document Cloud Sync PDF Security Redaction Tool Fill and Sign Profess
Adobe Acrobat Desktop 2026 is the industry-standard environment for professional PDF management, digital document security, and AI-driven insights. This 2026 optimized build features the Acrobat AI Assistant, allowing users to summarize complex documents, extract cross-file data, and generate intelligent responses via natural language processing. Engineered for Windows 11 and high-performance workstation environments, it provides a seamless bridge between local desktop editing and Adobe Document Cloud synchronization. Master your workflow with 70+ advanced features: including AI-powered OCR, secure electronic signatures (E-Sign), professional redaction tools, and high-fidelity file conversion for the modern digital office.
- Acrobat AI Assistant: Generative AI for document summaries, citations, and intelligent Q&A.
- Advanced PDF Editing: Full-text reflow, image manipulation, and vector asset integration.
- High-Fidelity OCR: AI-enhanced optical character recognition for searchable 8K scans.
- Electronic Signature Suite: Legally binding e-signatures with real-time tracking and audit trails.
- Document Cloud Sync: Seamless transition between Desktop, Web, and Mobile environments.
- Professional Security: AES-256 encryption, sensitive data redaction, and certificate-based protection.
- Batch Processing: High-speed conversion, merging, and organization of multi-document datasets.
MIT License – shared for educational, software administration, and enterprise workflow preservation.
If Adobe Acrobat Desktop 2026 improved your document workflow or simplified your AI analysis — please star the repository! It helps the professional community discover the most efficient PDF tools. ❤️
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04-23 22:19 · 财经
【新易盛：第一季度净利润为27.80亿元 同比增长76.80%】新易盛(300502.SZ)公告称，2026年第一季度实现营业收入83.38亿元，同比增长105.76%；归属于上市公司股东的净利润为27.80亿元，同比增长76.80%。业绩变动主要系报告期受益于终端客户算力投资持续增长，销售收入较上年同期大幅增加。注：公司2025年Q4净利润32.05亿，据此计算，2026年Q1净利润环比下降13%。
【新易盛：第一季度净利润为27.80亿元 同比增长76.80%】新易盛(300502.SZ)公告称，2026年第一季度实现营业收入83.38亿元，同比增长105.76%；归属于上市公司股东的净利润为27.80亿元，同比增长76.80%。业绩变动主要系报告期受益于终端客户算力投资持续增长，销售收入较上年同期大幅增加。注：公司2025年Q4净利润32.05亿，据此计算，2026年Q1净利润环比下降13%。
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04-23 09:20 · 数据可视化,开源,教育分析,仪表盘,Power BI
Interactive Power BI Dashboard analyzing NYC Graduation Results (Cohorts 2012-2019). Includes 5 objectives: Graduation Trend, Borough Comparison, Student Outcomes, Dropout Risk Map, and Advanced Regen
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04-22 08:04 · 技术迭代,生态增强,AI技能,智能检索,平台更新
生产级长期记忆管理技能，支持智能提取、混合检索等功能
memory-lancedb-pro
v1.0.0This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, c...
Like a lobster shell, security has layers — review code before you run it.
memory-lancedb-pro
Production-grade long-term memory system (v1.1.0-beta.8) for OpenClaw AI agents. Provides persistent, intelligent memory storage using LanceDB with hybrid vector + BM25 retrieval, LLM-powered Smart Extraction, Weibull decay lifecycle, and multi-scope isolation.
For full technical details (thresholds, formulas, database schema, source file map), see references/full-reference.md
.
Applying the Optimal Config (Step-by-Step Workflow)
When the user says "help me enable the best config", "apply optimal configuration", or similar, follow this exact procedure:
Step 1 — Present configuration plans and let user choose
Present these three plans in a clear comparison, then ask the user to pick one:
Plan A — 🏆 Full Power (Best Quality)
- Embedding: Jina
jina-embeddings-v5-text-small
(task-aware, 1024-dim) - Reranker: Jina
jina-reranker-v3
(cross-encoder, same key) - LLM: OpenAI
gpt-4o-mini
(Smart Extraction) - Keys needed:
JINA_API_KEY
+OPENAI_API_KEY
- Get keys: Jina → https://jina.ai/api-key · OpenAI → https://platform.openai.com/api-keys
- Cost: Both paid (Jina has free tier with limited quota)
- Best for: Production deployments, highest retrieval quality
Plan B — 💰 Budget (Free Reranker)
- Embedding: Jina
jina-embeddings-v5-text-small
- Reranker: SiliconFlow
BAAI/bge-reranker-v2-m3
(free tier available) - LLM: OpenAI
gpt-4o-mini
- Keys needed:
JINA_API_KEY
+SILICONFLOW_API_KEY
+OPENAI_API_KEY
- Get keys: Jina → https://jina.ai/api-key · SiliconFlow → https://cloud.siliconflow.cn/account/ak · OpenAI → https://platform.openai.com/api-keys
- Cost: Jina embedding paid, SiliconFlow reranker free tier, OpenAI paid
- Best for: Cost-sensitive deployments that still want reranking
Plan C — 🟢 Simple (OpenAI Only)
- Embedding: OpenAI
text-embedding-3-small
- Reranker: None (vector+BM25 fusion only, no cross-encoder)
- LLM: OpenAI
gpt-4o-mini
- Keys needed:
OPENAI_API_KEY
only - Get key: https://platform.openai.com/api-keys
- Cost: OpenAI paid only
- Best for: Users who already have OpenAI and want minimal setup
Plan D — 🖥️ Fully Local (Ollama, No API Keys)
- Embedding: Ollama
mxbai-embed-large
(1024-dim, recommended) ornomic-embed-text:v1.5
(768-dim, lighter) - Reranker: None — Ollama has no cross-encoder reranker; retrieval uses vector+BM25 fusion only
- LLM: Ollama via OpenAI-compatible endpoint — recommended models with reliable JSON/structured output:
qwen3:8b
(recommended — best JSON output, native structured output, ~5.2GB)qwen3:14b
(better quality, ~9GB, needs 16GB VRAM)llama4:scout
(multimodal MoE, 10M ctx, ~12GB)mistral-small3.2
(24B, 128K ctx, excellent instruction following, ~15GB)mistral-nemo
(12B, 128K ctx, efficient, ~7GB)
- Keys needed: None — fully local, no external API calls
- Prerequisites:
- Ollama installed: https://ollama.com/download
- Models pulled (see Step 5 below)
- Ollama running: macOS = launch the app from Applications; Linux =
systemctl start ollama
orollama serve
- Cost: Free (hardware only)
- RAM requirements: mxbai-embed-large ~670MB; qwen3:8b ~5.2GB; qwen3:14b ~9GB; llama4:scout ~12GB; mistral-small3.2 ~15GB
- Trade-offs: No cross-encoder reranking = lower retrieval precision than Plans A/B; Smart Extraction quality depends on local LLM — if extraction produces garbage, set
"smartExtraction": false
- Best for: Privacy-sensitive deployments, air-gapped environments, zero API cost
After user selects a plan, ask in one message:
- Please provide the required API key(s) for your chosen plan (paste directly, or say "already set as env vars")
- Are the env vars already set in your OpenClaw Gateway process? (If unsure, answer No)
- Where is your
openclaw.json
? (Skip if you want me to find it automatically)
If the user already stated their provider/keys in context, skip asking and proceed.
Do NOT proceed to Step 2 until API keys have been collected and verified (Step 2 below).
Step 2 — Verify API Keys (MANDATORY — do not skip)
Run ALL key checks for the chosen plan before touching any config. If any check fails, STOP and tell the user which key failed and why. Do not proceed to Step 3.
Plan A / Plan B — Jina embedding check:
curl -s -o /dev/null -w "%{http_code}" \
https://api.jina.ai/v1/embeddings \
-H "Authorization: Bearer <JINA_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"model":"jina-embeddings-v5-text-small","input":["test"]}'
Plan A / B / C — OpenAI check:
curl -s -o /dev/null -w "%{http_code}" \
https://api.openai.com/v1/models \
-H "Authorization: Bearer <OPENAI_API_KEY>"
Plan B — SiliconFlow reranker check:
curl -s -o /dev/null -w "%{http_code}" \
https://api.siliconflow.com/v1/rerank \
-H "Authorization: Bearer <SILICONFLOW_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"model":"BAAI/bge-reranker
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