04-26 16:04 · 科技,GitHub
Free, open-source blood work dashboard — AI-powered PDF import, DNA raw data analysis (42 SNPs), trend charts, and personalized health insights for 287+ biomarkers. Local-first, no account required.
getbased is a free, open-source health dashboard that turns lab PDFs into interactive charts and AI-powered health insights. Track 287+ biomarkers over time, detect trends, and get personalized interpretations — all stored locally in your browser with no account required.
Live app · Documentation · Discord · Nostr
- AI-powered PDF import — drop any lab report (any format, language, or country) and AI extracts and maps results to 287+ known biomarkers automatically. Batch import, direct image import (JPG/PNG/WebP), auto image mode for scanned PDFs
- Biomarker trend charts — interactive line charts with proportional time scale, reference bands, optimal ranges, and trend detection across 17 standard categories
- AI chat — ask questions about your results with full health context, image attachments, multiple personalities, conversation threads
- DNA import — upload raw data from AncestryDNA, 23andMe, MyHeritage, FTDNA, Living DNA, or Illumina GenomeStudio (DNAEra and other clinical labs). 47 curated SNPs across 13 categories (methylation, iron, lipids, vitamin D, alcohol, caffeine, body composition, etc.) with APOE haplotype resolution and 39 mtDNA haplogroups including 11 sub-clades. Genetic factors shown on dashboard, detail modals, and in AI context
- Specialty lab adapters — OAT (165 markers), fatty acids (Spadia, ZinZino, OmegaQuant), Metabolomix+. Any other specialty test imports through the custom marker pipeline
- Biological age — PhenoAge (Levine 2018) + Bortz Age (Bortz 2023) combined into a unified Biological Age marker with component breakdown
- Calculated markers — HOMA-IR, BUN/Creatinine ratio, free water deficit, lipid ratios (TG/HDL, LDL/HDL, ApoB/ApoA-I), NLR, PLR, De Ritis ratio, hs-CRP/HDL cardiovascular risk ratio
- Trend alerts — sudden changes and linear regression flagged on the dashboard
- Correlation viewer — compare any two markers, heatmap view
- Compare dates — side-by-side comparison of any two lab dates
- Manual entry — add results without a PDF, create custom biomarkers
- Marker glossary — searchable reference for all markers with values and ranges
- Interpretive lens — frame AI analysis through specific scientific paradigms or experts
- Custom Knowledge Source — connect your own document collection (research papers, clinical guides, any texts) to ground AI analysis in real sources. The AI searches your knowledge base for relevant passages before interpreting your labs, and cites them back to you
- 9 lifestyle context cards — diet & digestion, sleep, exercise, stress, light & circadian, environment, EMF assessment (Baubiologie SBM-2015), and more — each gets an AI health rating and enriches all interpretations
- Menstrual cycle tracking — phase-aware reference ranges, cycle phase bands on charts, perimenopause detection, symptom tracking
- Supplement & medication timeline — overlaid on charts to correlate with biomarker changes
- PDF reports — export a full health report as PDF
- Multi-profile — track multiple people, client list with search/sort/filter
- All data stored locally in your browser (localStorage + IndexedDB) — nothing on a server
- Personal info stripped from PDFs before AI processing (regex + streaming local AI obfuscation)
- AES-256-GCM encryption at rest
- Automatic backups (IndexedDB snapshots + daily folder backup via File System Access API)
- Venice AI end-to-end encryption option — prompts encrypted client-side (ECDH secp256k1 + AES-256-GCM), decrypted only inside a TEE. Nothing readable in transit or at rest on their servers
- Run a local AI server and nothing leaves your machine at all
- No account, no sign-up, no tracking
Opt-in feature that lets AI agents query your lab context — coding agents (Claude Code, Cursor), messenger bots (Hermes Agent, OpenClaw), or any MCP-compatible tool.
- Enable in Settings → Data → Agent Access to generate a read-only token
- Context is pushed to a lightweight gateway on every save and profile switch
- Per-profile: each profile's context is stored separately; agents can query any profile by ID
- Install on Linux with one command:
curl -sSL https://getbased.health/install.sh | bash
(pipx install --include-deps "getbased-agent-stack[full]"
for manual / cross-platform installs). getbased-agents bundles the MCP adapter, local RAG knowledge server, and browser setup dashboard. Works with Hermes Agent, OpenClaw, Claude Code, Claude Desktop, Cursor, Cline, or any MCP-compatible agent - Only the AI-readable context text is shared — never your mnemonic or raw lab data
- Token is revocable at any time from the same settings panel
Switch providers anytime. All non-AI features work without a provider configured.
git clone https://github.com/elkimek/get-based
cd get-based
node dev-server.js
Open http://localhost:8000
. You need an AI provider API key or local AI server for PDF import and chat. All other features work without one.
Web app only — no build tools, no bundler, no package manager. Pure ES modules under js/
.
- Chart.js for inte
▸ 展开全文
04-26 16:03 · 财经,财经
【湖南黄金：第一季度净利润同比增长79.21%】湖南黄金(002155.SZ)公告称，2026年第一季度实现营业收入188.30亿元，同比增长43.51%；归属于上市公司股东的净利润为5.96亿元，同比增长79.21%。业绩变动主要系公司产品黄金销售价格同比上涨59.50%，产品精锑销售价格同比上涨6.79%，产品钨精矿销售价格同比上涨310.10%所致。
湖南黄金(002155.SZ)公告称，2026年第一季度实现营业收入188.30亿元，同比增长43.51%；归属于上市公司股东的净利润为5.96亿元，同比增长79.21%。业绩变动主要系公司产品黄金销售价格同比上涨59.50%，产品精锑销售价格同比上涨6.79%，产品钨精矿销售价格同比上涨310.10%所致。注：公司Q1净利润5.96亿，2025年Q4净利润4.59亿，据此计算，Q1净利润环比增长29%。
▸ 展开全文
04-26 16:03 · 科技,GitHub
Real-time monitoring & analytics platform for Pump.fun tokens. Track launches, analyze trends, and get instant alerts — powered by WebSockets. Built with HTML, CSS & JavaScript.
Real-time monitoring & analytics platform for Pump.fun tokens on Solana.
🌐 Live site: pump-analyzer.com
PumpAnalyzer is a landing page for a token analytics platform built on top of Pump.fun. It lets traders track new token launches, analyze price movements and volume, and receive instant alerts — all in real time.
- ⚡ Real-Time Monitoring — Track every new token launch on Pump.fun as it happens
- 📊 Deep Analytics — Price charts, volume analysis, and holder distribution
- 🔔 Instant Alerts — Get notified the moment tokens hit your custom criteria
- 🤖 AI-Powered Insights — Smart detection of pump patterns and emerging trends
- 🔒 Secure & Non-Custodial — No wallet access required. Your keys, your crypto
- 🚀 Lightning Fast — Sub-second data updates powered by WebSockets
git clone https://github.com/keron-2000fz47/special-octo-parakeet.git
cd special-octo-parakeet
open index.html
No build tools or dependencies required — it's pure HTML/CSS/JS.
- 🌐 Website: pump-analyzer.com
- 🐦 Twitter: coming soon
Not financial advice. Always DYOR.
▸ 展开全文
04-26 16:03 · 科技,GitHub
ShopIQ is an AI-powered retail intelligence platform built for Praxis 2.0 that analyzes shopping data to predict trends, discover shopper behavior patterns, segment customers, and uncover product affinities using machine learning and an interactive AI analytics assistant.
Production-Ready Retail Analytics Platform with AI-powered insights, forecasting, and shopper behavior analysis.
✨ Recently Updated: Enhanced responsive design, error handling, and production deployment configuration.
A comprehensive data analytics platform with forecasting, shopper behavior analysis, and interactive visualizations built with Next.js and TypeScript.
- Interactive Dashboard: View and analyze your data with rich visualizations
- Revenue Forecasting: Predictive analytics using machine learning models
- Shopper Behavior Analysis: Advanced customer segmentation using RFM analysis and clustering
- Market Basket Analysis: Discover product affinities and purchasing patterns
- Data Upload & Processing: Upload CSV files and automatically process them
- 🆕 Custom Local AI Assistant: Free, privacy-focused AI that answers questions about your data - no API costs!
- Responsive Design: Works seamlessly across desktop and tablet devices
- Framework: Next.js 16.1.6
- Language: TypeScript
- Styling: Tailwind CSS with custom components
- UI Components: Radix UI primitives
- Charts: Recharts
- State Management: Zustand
- Icons: Lucide React
- AI Integration: @ai-sdk/react for AI assistant functionality
The application provides multiple perspectives for data analysis:
- Dashboard: Overview of key metrics and visualizations
- Forecast: Revenue predictions and model performance metrics
- Comparison: Side-by-side data comparison capabilities
- Behavior: Detailed shopper behavior and segmentation analysis
- Upload: Data import and preprocessing workflow
- Settings: Application configuration options
# 1. Install dependencies
npm install
# 2. Set up environment variables
cp .env.example .env.local
# Edit .env.local and add your OPENAI_API_KEY
# 3. Run development server
npm run dev
# Push to Git
git add . && git commit -m "Update" && git push
# Render will auto-deploy
- The application loads with prebuilt sample data for immediate exploration
- Navigate between different views using the sidebar
- Interact with charts and visualizations to explore data insights
- Go to the Upload view from the sidebar
- Select a CSV file containing your transaction data
- Map your columns to the required fields (category, purchase amount, date, customer ID)
- The application will process your data and update all visualizations
- RFM Analysis: Recency, Frequency, Monetary analysis for customer segmentation
- Clustering: Unsupervised learning to identify customer groups
- Affinity Rules: Market basket analysis to discover product relationships
- Predictive Models: Revenue forecasting with confidence intervals
├── app/ # Next.js app router pages
├── components/ # Reusable UI components
│ ├── ui/ # Base UI components (buttons, cards, etc.)
│ └── views/ # Page-level components
│ ├── error-boundary.tsx # Error handling
│ └── theme-provider.tsx # Theme management
├── lib/ # Shared utilities and business logic
│ ├── store.ts # Zustand store for state management
│ ├── types.ts # Type definitions
│ ├── behavior-analysis.ts # Shopper behavior algorithms
│ └── data-processing.ts # Data processing utilities
├── public/ # Static assets
├── styles/ # Global styles
├── .env.example # Environment variables template
├── render.yaml # Render deployment config
└── DEPLOYMENT.md # Deployment guide
graph TD
A[User Interface] --> B[Next.js App Router]
B --> C[Zustand Store]
C --> D[Data Processing Layer]
D --> E[Business Logic Components]
E --> F[UI Components]
G[CSV Upload] --> H[Data Validation]
H --> I[Data Cleaning]
I --> J[Feature Engineering]
J --> K[Analysis Engine]
K --> L[RFM Analysis]
K --> M[Clustering Engine]
K --> N[Affinity Analysis]
K --> O[Forecasting Model]
L --> P[Visualization Layer]
M --> P
N --> P
O --> P
P --> Q[Dashboard Views]
Q --> R[User Interface]
S[AI Assistant] --> T[Chat API]
T --> U[LLM Integration]
U --> V[Response Generation]
V --> R
W[Settings Management] --> X[Configuration Store]
X --> C
flowchart LR
A[User Input] --> B[Frontend Components]
B --> C[Zustand State Management]
D[CSV File Upload] --> E[File Validation]
E --> F[Data Parsing]
F --> G[Column Mapping]
G --> H[Data Cleaning]
H --> I[Feature Extraction]
I --> J[Data Transformation]
J --> K[Processed Dataset]
K --> L[RFM Calculation]
K --> M[Clustering Analysis]
K --> N[Affinity Rules]
K --> O[Time Series Forecasting]
L --> P[Segmentation Results]
M --> Q[Cluster Assignments]
N --> R[Association Rules]
O --> S[Forecast Predictions]
P --> T[Visualization Engine]
Q --> T
R --> T
S --> T
T --> U[Dashboard Components]
U --> V[User Interface]
W[AI Chat Requests] --> X[API Processing]
X --> Y[LLM Integration]
Y --> Z[Response Generation]
Z --> V
graph TD
A[User Authentication] --> B[Session Management]
B --> C[Route Protection]
D[Input Validation] --> E[Data Sanitization]
E --> F[API Security Layer]
F --> G[Server-Side Validation]
G --> H[Response Sanitization]
I[Environment Security] --> J[Secrets Management]
J --> K[Configuration Validation]
L[Client-Side Security] --> M
▸ 展开全文
04-26 16:03 · 科技,GitHub
50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social intelligence. Works with Claude Code, Pi, ChatGPT, Gemini, Cursor, Windsurf, any AI.
Turn any AI into your affiliate marketing team.
52 AI-powered skills across 8 stages with a closed-loop flywheel. Research programs, scout trending content, write data-backed posts, generate infographics, build pages, deploy, track, optimize, scale — with any AI agent.
Works with: Claude Code · Pi · ChatGPT · Gemini CLI · Cursor · Windsurf · OpenClaw · any AI that reads text
skills/{stage}/{skill-name}/SKILL.md
— main skill definitionsshared/references/
— shared doctrine, compliance, and flywheel referencestools/src/
— affiliate-check CLI sourceregistry.json
— machine-readable skill catalogevals/
— evaluation cases and results
# Claude Code / Pi (recommended)
npx skills add Affitor/affiliate-skills
# Or clone manually
git clone https://github.com/Affitor/affiliate-skills.git ~/.claude/skills/affiliate-skills
cd ~/.claude/skills/affiliate-skills && ./setup
# OpenClaw / ClawHub
clawhub install affiliate-skills
# Cursor / Windsurf
npx skills add Affitor/affiliate-skills
Paste this into any AI:
Search the Affitor affiliate directory for AI video tools.
Use this API: GET https://list.affitor.com/api/v1/programs?q=AI+video&sort=top&limit=5
Show me the results in a table with: Name, Reward Value, Cookie Days, Stars.
Then recommend the best one and explain why.
- You Google "best affiliate programs" and get SEO spam written to rank, not to help
- You write content from gut feeling with no idea what format actually performs
- You have no data on what's trending, what hooks work, or what gaps exist
- You spend 4 hours on a landing page that converts at 0.2%
- You pick programs by vibes instead of data
S1 RESEARCH ──▶ S2 CONTENT ──▶ S3 BLOG & SEO ──▶ S4 OFFERS & LANDING
▲ │
│ ▼
│ S5 DISTRIBUTION
│ │
└──────────── S6 ANALYTICS ◀─────────────────────────┘
│
▼
S7 AUTOMATION → SCALE
│
S8 META (across all)
Closed loop. S6 Analytics feeds BACK to S1 Research. Every skill knows what comes next (chain_metadata.suggested_next
) and what feeds it. Data flows forward through the funnel and back through analytics.
Data-driven. S1 skills don't just find programs — they scout what content is actually performing (views, likes, shares), analyze traffic, and rank angles by real engagement data. By the time you write, you already know what format, hook, and platform to use.
Research-backed. Content skills don't write from thin air. content-research-brief
collects real source articles first. Your posts have real stats, real quotes, and current information.
You: I want to promote AI video tools. What's trending?
AI: [runs trending-content-scout]
Scanned 47 videos across YouTube + TikTok.
Top format: comparison (45% of top content, avg engagement: 35.2)
Best hook: "I replaced my $5K video team with AI" (engagement: 42.3)
Gap: nobody comparing HeyGen vs Synthesia on TikTok
Benchmark: median 18K views, top 10% needs 85K+
You: What's the best program to promote?
AI: [runs affiliate-program-search + traffic-analyzer]
Name Commission Cookie Stars Traffic Score
──────────────────────────────────────────────────────────────
HeyGen 30% recurring 60 days ★ 127 82/100
Synthesia 25% one-time 30 days ★ 84 76/100
D-ID 20% recurring 45 days ★ 52 61/100
HeyGen wins: highest commission, recurring, longest cookie,
strongest website traffic (2.1M monthly visits, 35% direct).
You: Research the latest about HeyGen for a LinkedIn post.
AI: [runs content-research-brief]
📚 7 sources collected (5 fully fetched)
🏷️ Tags: AI (7), Funding (2), Tools (5)
Key stats:
- "$60M Series A at $500M valuation" — TechCrunch
- "40,000+ businesses use HeyGen" — company blog
- "Avatar 3.0 launched with real-time lip sync" — The Verge
3 angles generated:
1. "HeyGen just raised $60M — here's what it means for AI video"
2. "I tested HeyGen vs Synthesia for 30 days — honest comparison"
3. "AI video tools are killing the $45B production industry"
You: Write angle 2 as a LinkedIn post with an infographic.
AI: [runs viral-post-writer + infographic-generator]
Post: 1,200-char comparison post, bold claim hook,
soft CTA in comments, FTC disclosure.
Infographic: side-by-side comparison card (1080×1350),
HeyGen vs Synthesia — commission, features, pricing.
Dark modern palette, self-contained HTML ready to render.
You: Now a TikTok script.
AI: [runs tiktok-script-writer]
45-second script, demo_first hook (backed by scout data):
"Watch me create a $2,000 video for free in 30 seconds."
Hook → Pain → Demo → Result → CTA. Bio link reminder.
You: Plan my full funnel — I have 5 hours/week.
AI: [runs funnel-planner]
Week 1: Scout + Research → find winning angles + collect sources
Week 2: Content → 5 LinkedIn posts + 2 TikToks + 2 infographics
Week 3: Blog → comparison article backed by research brief
Week 4: Deploy → landing page + bio link live
Week 5: Analytics → compare your metrics vs scout benchmark
Week 6: Optimize → double down on what beat the benchmark
Target: first affiliate click by week 2, first commission by week 5.
Affiliates — Find programs, create content, track performance. Data-dr
▸ 展开全文
04-26 16:03 · 开源,AI内容代理,自动化流程,GitHub Trending,嵌入式AI
Sage — X content agent for OpenClaw. Voice calibration, tweet drafting, reactive takes, thread writing, trend scouting, feedback loop. Self-installs in 5 minutes.
Sage is an AI agent that handles your X/Twitter content strategy end-to-end. Voice calibration. Trend scouting. Tweet drafting. Thread writing. Performance tracking. Feedback loop.
Built with OpenClaw. Self-installs in under 5 minutes.
- 0 to 172K impressions on a single tweet using Sage's reactive format
- Went from spending 2+ hours per day on content to 20 minutes reviewing and approving drafts
- Consistent posting without burnout - Sage handles the heavy lifting, you stay in the loop
- OpenClaw installed
- Any supported AI model (Claude, GPT-4o, Gemini - all work)
- An X/Twitter account (any size, any niche)
Just tell your OpenClaw agent:
Install this skill: https://github.com/sharbelxyz/sage-x-agent
OpenClaw will clone the repo, install the skill, and Sage will run onboarding automatically.
git clone https://github.com/sharbelxyz/sage-x-agent.git ~/clawd/skills/sage-x-agent
Then tell your OpenClaw:
Install Sage
Sage will detect the new skill and walk you through 10 onboarding questions.
First time you run Sage, she will ask 10 questions:
- Your name and X handle
- Your niche (one sentence)
- Your target audience
- Your goal and deadline
- Current follower count and avg impressions
- How you naturally write (voice description)
- Your 2-3 best tweets (optional - for voice calibration)
- 5-10 accounts in your space to monitor
- What to avoid (flops, off-brand formats, topics you hate)
- Whether you want reactive mode on (Sage surfaces trending topics for you)
Takes about 5 minutes. Sage writes your answers to config.md
automatically. You never do this again.
After setup, just talk naturally:
Sage, draft me a tweet about what I just shipped
Sage, react to this - [paste trending tweet or article]
Sage, write a thread on why most people use AI wrong
Sage, analyze my last 10 tweets and tell me what's working
Sage, what's trending in my niche right now?
Sage, give me a daily batch - 3 tweets for today
Sage, show me the feedback loop - what patterns have you noticed?
Sage's most powerful feature. Give it a trending topic and it will:
- Hook: name what just happened (specific, not vague)
- Add insight the original did not have
- Translate it: "here is what this means for you" or "here is how to use this today"
- Close with your POV and one concrete action
This format consistently outperforms generic commentary because it teaches, not just reports.
Example reactive structure:
[what just happened, specific and named]
here is what this means for you:
- [practical insight 1]
- [practical insight 2]
- [practical insight 3]
[your POV + one thing to do today]
Sage keeps a memory/
folder with:
approved-drafts.md
- every draft you said yes torejected-drafts.md
- every draft you rejected and whyperformance-log.md
- stats after tweets go livevoice-examples.md
- your actual phrases, rhythms, and patternstrend-log.md
- trends surfaced and when
Before every session, Sage reads all of these. She never repeats a rejected angle. She weights suggestions toward what has actually worked. The longer you use her, the better the drafts.
sage-x-agent/
├── README.md <- You are reading this
├── SKILL.md <- Sage's full instructions (all 8 systems)
├── config.md <- Auto-created during onboarding (gitignored)
├── example-config.md <- Sample config for reference
└── memory/
├── approved-drafts.md <- Auto-populated (gitignored)
├── rejected-drafts.md <- Auto-populated (gitignored)
├── performance-log.md <- Auto-populated (gitignored)
├── voice-examples.md <- Auto-populated (gitignored)
└── trend-log.md <- Auto-populated (gitignored)
Note: config.md
and all memory/
files are gitignored. Your personal data never leaves your machine.
- To change your X profile details: edit
config.md
- To change how Sage writes or behaves: edit
SKILL.md
(plain English, no code) - To reset and start fresh: delete
config.md
and allmemory/
files, then run "Install Sage" again
Sage writes every tweet with line breaks and breathing room.
Good:
this is the hook.
this is the build.
this is the punchline.
Not good:
this is the hook. this is the build. this is the punchline all in one dense block that nobody reads.
Every line earns the next one. Walls of text get skipped. Sage knows this and applies it to every draft, always.
Does this work for any niche? Yes. Onboarding calibrates Sage to your niche, voice, and the accounts you watch.
Do I need to be technical? No. If you can install OpenClaw and answer 10 questions, you are set.
Does Sage post automatically? No. Sage drafts and queues. You review and approve before anything goes out.
Is my data private?
Yes. config.md
and all memory/
files are gitignored and stay on your machine.
What model works best? Claude Sonnet or GPT-4o. Both work well. Sonnet tends to nail conversational voice a bit better.
Can I use this for LinkedIn or other platforms? The SKILL.md is plain text. You can fork it and adapt the formatting rules and output format for any platform.
Built by Sharbel - founder, AI builder, operator.
Video that goes
▸ 展开全文
04-26 16:03 · 科技,GitHub
Phantom Wallet Trending for your Solana token — Top 1-10 placement. From $1,200/6h. Instant delivery via PandaBoost API.
Get your token to Top 1–10 trending on Phantom Wallet — the #1 Solana wallet with 10M+ users.
Promote your token directly inside Phantom's trending section. Fast delivery, stealth mode, full refund guarantee.
- Platform: Phantom Wallet (phantom.app)
- Placement: Top 1–10 Trending
- Duration options: 6h · 12h · 24h
- Chain: Solana
- Start time: Instant (within 60 seconds)
- Guarantee: Full refund if delivery fails
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → Phantom → Wallet Trending
- Select duration (6h / 12h / 24h)
- Paste your token address → confirm
Open @pandaboostbot → New Order → Phantom → Wallet Trending
Order Phantom Wallet Trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "phantom-wallet-trending",
"orderTypeId": "12h", # 6h | 12h | 24h
"quantity": 1,
"fields": {
"token_address": "YOUR_TOKEN_ADDRESS",
"chain": "solana"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "ABCD1234", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "phantom-wallet-trending",
orderTypeId: "24h", // 6h | 12h | 24h
quantity: 1,
fields: {
token_address: "YOUR_TOKEN_ADDRESS",
chain: "solana"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "phantom-wallet-trending",
"orderTypeId": "24h",
"quantity": 1,
"fields": {
"token_address": "ErKGCuxZeMQoS3VhFJZ77giESQphJhGpYDaUJmhApump",
"chain": "solana"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/orders/ABCD1234
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
▸ 展开全文
04-26 16:03 · AI政策,政策利好,科技创新,财经政策,创业板改革
【周末要闻汇总】1、国常会研究科技创新有关工作；2、特朗普取消美方代表赴巴基斯坦行程；3、白宫记者晚宴传出“枪声”，特朗普被紧急疏散；4、深交所正式发布深化创业板改革首批配套业务规则。
本周末影响市场的重要资讯有：国常会研究科技创新有关工作，听取推动海洋经济
宏观经济
新就业群体是社会主义劳动者的重要组成部分，是经济社会发展不可或缺的重要力量。当前我国新兴领域迅速发展，尤其是以互联网平台为支撑的新业态大量涌现，聚集了规模庞大的新就业群体。为加强新就业群体服务管理，经党中央、国务院同意，现提出如下意见。
国务院总理李强4月24日主持召开国务院常务会议，研究科技创新有关工作，听取推动海洋经济高质量发展情况汇报，审议通过《行政法规制定程序条例（修订草案）》。会议指出，近年来我国科技实力持续增强，关键核心技术攻关加快推进，科技创新成果不断涌现，新质生产力稳步发展。要锚定2035年建成科技强国的战略目标，充分发挥新型举国体制优势，全面深化科技体制机制改革，一体推进教育
为规范金融产品网络营销活动，保障金融消费者和投资者合法权益，促进互联网金融银行知识产权
中期借贷便利（MLF）本月结束加量续做。4月24日，中国人民银行发布2026年4月中期借贷便利招标公告称，为保持银行体系流动性充裕，4月27日，将以固定数量、利率招标、多重价位中标方式开展4000亿元MLF操作，期限为1年期。
问：4月22日，美国众议院外交事务委员会通过了《硬件技术控制多边协同法案》（简称MATCH法案）等多项出口管制法案。请问中方对此有何评论？答：中方注意到有关情况。中方一贯反对任何泛化国家安全、滥用出口管制的行为。相关法案如最终出台，将严重破坏国际经贸秩序，严重冲击全球半导体
中方已多次重申，坚决反对未经联合国安理会授权的单边制裁，坚决反对欧方对中国企业和个人实施所谓长臂管辖。欧方此举与中欧领导人共识精神背道而驰，严重破坏中欧互信和双边关系大局。中方敦促欧方，立即将中国企业和个人移出制裁清单，并遵守双方领导人共识精神，通过对话协商找到解决各自关切的办法。中方将采取必要措施，坚决维护中国企业的正当合法权益，一切后果将由欧方承担。
为维护国家安全和利益，履行防扩散等国际义务，根据《中华人民共和国出口管制法》和《中华人民共和国两用物项出口管制条例》等法律法规有关规定，决定将赫斯塔尔公司等7家欧盟实体列入出口管制管控名单。
全球市场
美国总统特朗普25日在社交媒体上宣布，他取消了美方代表前往巴基斯坦首都伊斯兰堡“与伊朗方面会晤”的行程。当天早些时候，伊朗外长阿拉格齐结束对巴访问并离开该国前往下一站阿曼。
当地时间4月25日，有消息称美国华盛顿希尔顿酒店
中国外交部发言人郭嘉昆24日主持例行记者会。有记者提问：美国白宫指责中国大规模“窃取”美国人工智能知识产权。中方有何评论？郭嘉昆：美方有关说法毫无根据，是对中国人工智能产业发展成就的污蔑抹黑，中方对此坚决反对。我们敦促美方尊重事实，摒弃偏见，停止对华科技遏压，多做有利于两国科技交流与合作的事。
美股三大指数04月24日收盘涨跌不一，纳指、标普500指数再创新高。截至收盘，道琼斯工业平均指数比前一交易日下跌79.61点，收于49230.71点，跌幅为0.16%；标准普尔纳斯达克综合
金融资本
4月24日，财政部国库支付中心主任王建勋在财政部新闻发布会上表示，一季度证券交易印花税增长78.1%，主要是股票市场交易活跃，成交金额增长。
为深入推进和持续巩固新“国九条”以来打击和防范上市公司财务造假工作成效，近日证监会部署了2026年打击和防范上市公司财务造假专项行动。本次专项行动更加突出早发现、强防范、优机制三项目标，一体推进发现、惩处、退市、投保有机衔接，聚焦四方面重点任务。
中国证监会近日发布公告称，经商中国人民银行、国家外汇局，将从2026年4月24日起允许合格境外投资者参与国债期货期货交易，是落实党中央、国务院关于“扩大高水平对外开放”决策部署的具体举措之一。此举旨在持续扩大合格境外投资者可投资范围，丰富境外机构投资者利率风险管理工具，增强人民币债券资产吸引力，提升境外机构投资行为的稳定性，促进债券期现货市场高质量发展。
为进一步规范上市公司董事会秘书履职行为，促进和保障董事会秘书有效履职，中国证监会制定发布《上市公司董事会秘书监管规则》，自2026年5月24日起施行。下一步，中国证监会将做好《董秘规则》实施工作，促进董事会秘书发挥积极作用，推动提升上市公司治理水平。
近日，经中国证监会批准，上海证券交易所修订发布《上海证券交易所交易规则（2026年修订）》。本次修订旨在优化证券交易制度，促进市场稳定运行，提升市场定价效率和流动性，更好满足投资者交易需求。
为强化上市公司董事、高级管理人员（和控股股东、实际控制人等“关键少数”监管，促进上市公司董事会秘书积极履职，推动提升上市公司治理水平，更好保护投资者特别是中小投资者合法权益，在中国证监会统筹指导下，深交所修订主板、创业板《股票上市规则》和《规范运作指引》，于4月24日正式对外发布。
4月24日，为落实中国证监会《关于深化创业板改革更好服务新质生产力发展的意见》，深交所正式发布首批4件配套业务规则，包括修订的《创业板股票上市规则》《交易规则》《首次公开发行证券发行与承销业务实施细则》，以及新制定的《发行上市审核业务指引第9号——预先审阅》。
产业经济
券商业绩分化已从“温差”演变为“冰火两重天”。截至4月25日，已有17家券商（含上市主体）披露2026年一季业绩，业绩分化态势显著，11家实现正增长（国泰海通
随着AI数据中心通信通信产业链都交出了颇为亮眼的业绩。同时，多家厂商也透露了扩产计划，例如中际旭创
公司聚焦
4月24日，黑龙江证监局对黑龙江中能粮科农业科技有限公司及其中介机构广东信宇律师事务所等相关主体境外上市备案违法违规行为作出行政处罚事先告知。该案系《境内企业境外发行证券和上市管理试行办法》实施以来，中国证监会查办的首例未按规定履行境外上市备案程序擅自境外上市的案件。
摩尔线程（688795.SH）披露2026年一季报与2025年报。根据报告，2026年一季度，摩尔线程实现营收7.38亿元，同比增长155.35%；归母净利润0.29亿元，同比增加1.42亿元；归母扣非净利润亏损0.54亿元，亏损同比收窄60.10%。
据报道，谷歌将以3500亿美元的估值向Anthropic投资100亿美元，未来可能还会追加300亿美元。在人工智能代理Claude Code取得突破性成功之后，Anthropic加大了融资力度。Claude Code能够加快计算机博通
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04-26 16:03 · 科技,GitHub
Get your token to DEX Screener TOP 1-10. Automated trending service for Solana, Ethereum, BSC and Base via PandaBoost API. SOL from $1,200/12h · ETH/BSC/Base from $1,000/24h. Instant start, full refund guaranteed. Web panel + Telegram bot + REST API.
Get your token to DEX Screener TOP 1–10 — Solana, Ethereum, BSC, Base and more
Push your token into the DEX Screener trending section and get seen by thousands of traders actively looking for the next gem.
- Result: TOP 1–10 on DEX Screener trending
- Start time: within 60 seconds of order confirmation
- Guarantee: Full refund if delivery fails
- Stealth mode: all activity looks organic
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → DEX Screener → Trending
- Select package (12h or 24h)
- Paste your pair contract address → confirm
Open @pandaboostbot → New Order → DEX Screener → Trending
Order DEX Screener trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
# 12h Trending — Solana ($1,200)
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h", # dex-trending-12h | dex-trending-24h
"quantity": 1,
"fields": {
"pair_address": "YOUR_PAIR_CONTRACT_ADDRESS"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "V3BH8YCS", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "dex-trending",
orderTypeId: "dex-trending-24h", // dex-trending-12h | dex-trending-24h
quantity: 1,
fields: {
pair_address: "0xYOUR_PAIR_CONTRACT_ADDRESS"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h",
"quantity": 1,
"fields": {
"pair_address": "0xe945683b3462d2603a18bdfbb19261c6a4f03ad1"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/balance
# {"success": true, "data": {"balance": "1250.00000000", "currency": "USD"}}
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
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04-26 16:03 · 科技,GitHub
GitHub 话题趋势追踪工具 - 按主题监控热门项目，AI 智能分析，每日邮件报告，GitHub Pages 可视化
追踪 GitHub 话题下的热门项目，AI 智能分析，每日趋势报告邮件
GitHub Topics Trending 是一个自动化仓库趋势追踪系统。它每天通过 GitHub API 获取指定话题下的热门仓库，使用 Claude AI 对仓库进行智能分析和分类，计算星标变化趋势，并通过 Resend 发送专业的 HTML 邮件报告，同时生成 GitHub Pages 静态网站。
- 开发者视角 - 快速了解特定话题下的热门项目
- 趋势洞察 - 捕捉新兴项目和工具的崛起
- 智能总结 - AI 帮你理解每个项目解决什么问题
- 自动化 - 无需手动查看网站，每天自动推送
GitHub Topics Daily - 2026-01-27
├── Top 20 经典榜单（含 AI 总结）
│ ├── 仓库名称（可点击跳转）、排名、星标数
│ ├── AI 一句话摘要
│ ├── 详细说明
│ └── 解决的问题标签
├── 星标增长 Top 5
├── 新晋项目
├── 活跃项目
└── 趋势统计
┌─────────────────────────────────────────────────────────────────┐
│ GitHub Topics Trending │
└─────────────────────────────────────────────────────────────────┘
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ GitHub │ │ GitHub │ │ Claude │
│ Actions │ ──▶ │ Fetcher │ ──▶ │ Summarizer │
│ (Cron Daily)│ │ (API) │ │ AI │
└──────────────┘ └──────┬───────┘ └──────┬───────┘
│ │
▼ │
┌──────────────┐ │
│ README │ │
│ Fetcher │ │
└──────┬───────┘ │
│ │
└───────┬───────────────┘
│
▼
┌──────────────┐
│ Database │
│ (SQLite) │
└──────┬───────┘
│
▼
┌──────────────┐
│ Trend │
│ Analyzer │
└──────┬───────┘
│
┌─────────────────┴─────────────────┐
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Email │ │ Web │
│ Reporter │ │ Generator │
└──────┬───────┘ └──────┬───────┘
│ │
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Resend │ │ GitHub │
│ Sender │ │ Pages │
└──────┬───────┘ └──────────────┘
│
▼
──────► 您的邮箱
- Python 3.11+
- GitHub Personal Access Token
- Claude API Key（支持智谱代理）
- Resend API Key
# 克隆仓库
git clone https://github.com/geekjourneyx/github-topics-trending.git
cd github-topics-trending
# 安装依赖
pip install -r requirements.txt
# 复制环境变量模板
cp .env.example .env
# 编辑 .env 文件，填入你的 API Keys
nano .env
# 设置环境变量
export GH_TOKEN="your_github_token"
export ZHIPU_API_KEY="your_api_key"
export RESEND_API_KEY="your_resend_key"
export EMAIL_TO="your_email@example.com"
# 运行（完整流程）
python -m src.main
# 仅获取数据，不发送邮件
python -m src.main --fetch-only
注意: 环境变量已配置为使用 GH_TOKEN
和 TOPIC
，避免以 GITHUB_
开头的 Secret 名称。
# 设置环境变量
export GH_TOKEN="your_github_token"
export TOPIC="claude-code"
---
## 配置说明
### 环境变量
| 变量 | 必需 | 说明 | 默认值 |
|-----|------|------|--------|
| `GH_TOKEN` | Yes | GitHub Personal Access Token | - |
| `TOPIC` | No | 要追踪的 GitHub Topic | `claude-code` |
| `ZHIPU_API_KEY` | Yes | Claude API Key（智谱代理） | - |
| `ANTHROPIC_BASE_URL` | No | Claude API 地址 | `https://open.bigmodel.cn/api/anthropic` |
| `RESEND_API_KEY` | Yes | Resend API Key | - |
| `EMAIL_TO` | Yes | 收件人邮箱 | - |
| `RESEND_FROM_EMAIL` | No | 发件人邮箱 | `onboarding@resend.dev` |
| `DB_PATH` | No | 数据库路径 | `data/github-trending.db` |
| `DB_RETENTION_DAYS` | No | 数据保留天数 | `90` |
| `SURGE_THRESHOLD` | No | 暴涨阈值（比例） | `0.3` |
### GitHub Token 配置
#### 申请 Personal Access Token (PAT)
1. 登录 GitHub
2. 点击头像 → **Settings**
3. 左侧菜单最下方 → **Developer settings**
4. **Personal access tokens** → **Tokens (classic)**
5. **Generate new token** → **Generate new token (classic)**
6. 配置 Token：
- **Note**: 输入 `GitHub Topics Bot`
- **Expiration**: 选择有效期（建议 `90 days` 或 `No expiration`）
- **勾选权限**:
- ☑️ `public_repo` (访问公共仓库)
- 点击 **Generate token**
7. 复制 token（只显示一次！请妥善保存）
#### 在 GitHub Actions 中配置
1. 进入 GitHub 仓库页面
2. **Settings** → **Secrets and variables** → **Actions**
3. **New repository secret**
4. 配置:
- **Name**: `GITHUB_TOKEN`
- **Value**: 粘贴你的 token
5. 点击 **Add secret**
### Resend 配置
1. 注册 [Resend](https://resend.com)
2. 创建 API Key
3. 配置发件人域名（或使用默认的 `onboarding@resend.dev`）
---
## 使用方法
### 命令行运行
```bash
# 完整流程（获取数据 + AI 分析 + 发送邮件 + 生成网站）
python -m src.main
# 仅获取数据
python -m src.main --fetch-only
# 查看最新数据日期
sqlite3 data/github-trending.db "SELECT date FROM repos_daily ORDER BY date DESC LIMIT 1;"
# 查看今日排行榜 Top 10
sqlite3 data/github-trending.db "SELECT rank, repo_name, stars FROM repos_daily WHERE date = '2026-01-27' ORDER BY rank LIMIT 10;"
# 查看仓库详情
sqlite3 data/github-trending.db "SELECT repo_name, summary, category FROM repos_details WHERE repo_name = 'anthropics/claude-code';"
-
进入仓库 Settings → Pages
-
Build and deployment 配置：
- Source: Deploy from a branch
- Branch: gh-pages 分支
- Folder: / (root)
- 点击 Save
- 仓库 Settings → General
- 滚动到底部 Danger Zone
- Change repository visibility → 选择 Public
- 确认更改
https://geekjourneyx.github.io/github-topics-trending/
-
Settings → Secrets and variables → Actions → New repository secret
-
添加以下 Secrets：
- Settings → Actions → General
- Workflow permissions → 选择 Read and write permissions
- 点击 Save
- 点击 Actions 标签
- 选择 GitHub Topics Trending workflow
- 点击 Run workflow → Run workflow
默认每天 UTC 02:00（北京时间 10:00）自动运行。
github-topics-trending/
├── .github/workflows/
│ └── github-trending.yml # GitHub Actions 配置
├── src/
│ ├── config.py # 配置管理
│ ├── database.py # SQLite 操作
│ ├── github_fetcher.py # GitHub API 采集
│ ├── readme_fetcher.py # README 获取
│ ├── claude_summarizer.py # AI 分析
│ ├── trend_analyzer.py # 趋势计算
│ ├── email_reporter.py # 邮件生成
│ ├── web_generator.py # 网站生成
│ ├── resend_sender.py # 邮件发送
│ └── main.py # 主入口
├── plugins/
│ └── github-topics/ # Claude Code Skill
├── docs/ # GitHub Pages 输出
├── data/
│ └── github-tre
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