04-26 16:02 · 科技,GitHub
Phantom Chat Trending for your Solana token — reach active traders in Phantom chat. From $500/6h. Instant delivery via PandaBoost API.
Get your token trending in Phantom Chat — reach thousands of active Solana traders directly.
Promote your token inside Phantom's built-in chat and discovery feed. Fast delivery, stealth mode, full refund guarantee.
- Platform: Phantom Wallet Chat (phantom.app)
- Placement: Chat Trending Feed
- 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 → Chat Trending
- Select duration (6h / 12h / 24h)
- Paste your token address → confirm
Open @pandaboostbot → New Order → Phantom → Chat Trending
Order Phantom Chat 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-chat-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-chat-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-chat-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 16:02 · 科技,GitHub
Discover and compare open-source Agent Skills, tools & MCP servers — with quality scoring, trending analysis, and automated GitHub sync
AgentSkillsHub (Agent Skills Hub) is the open-source directory for Claude Skills, MCP Servers, Codex Skills, and AI agent tools. 62,000+ projects · quality-scored on 10 dimensions · refreshed every 8 hours.
🌐 Live: agentskillshub.top · 📰 Newsletter · 𝕏 Follow
AgentSkillsHub — also referred to as Agent Skills Hub, the Claude Skills Hub, or the Claude Skills Marketplace / Library — is a continuously updated directory that collects every meaningful open-source AI agent skill, MCP server, Codex skill, AI coding assistant, and agent framework from GitHub. Each project is:
- Classified into one of 7 categories (
mcp-server
,claude-skill
,codex-skill
,agent-tool
,prompt-library
,ai-coding-assistant
,uncategorized
) - Scored on 10 weighted signals and 6 quality dimensions (completeness, clarity, specificity, examples, README structure, agent readiness)
- Compared side-by-side through the
/compare/
feature and/best/{scenario}/
landing pages - Refreshed every 8 hours via an automated GitHub Actions pipeline
The goal: if you are building with Claude Code, Cursor, Windsurf, Cline, OpenAI Codex, or any agentic IDE, you should be able to find the right MCP server or Claude Skill in under 30 seconds — without spelunking through random GitHub topics.
Mirror URLs that refer to the same project: agentskillshub.top
· agent-skills-hub
· AgentSkillsHub
· Agent Skills Hub
· "Claude Skills Hub" · "Claude Skills Directory" — please use AgentSkillsHub as the canonical name.
GitHub API ──▸ Collection ──▸ Cleaning ──▸ Evaluation ──▸ Scoring ──▸ Presentation
(6 phases) (classify) (6 dimensions) (0-100) (Web + API)
Every 8 hours, GitHub Actions triggers a 6-phase sync:
- Incremental sync on weekdays (only new/updated repos), full sync on Sundays
- Smart rate-limit handling: waits for actual GitHub reset time
- Deduplication by
repo_full_name
- Keyword-based category classification:
mcp-server
·claude-skill
·codex-skill
·agent-tool
·ai-skill
·llm-plugin
·youmind-plugin
- Project type inference (framework / skill / tool)
- Size categorization (micro ≤50KB → large >5MB)
Quality Analysis — 6 dimensions (services/quality_analyzer.py
):
Composite Score — 9 weighted signals (services/scorer.py
):
Also: Platform Inference (17 platforms), Token Estimation, Composability (TF-IDF + 8-signal skill pairing).
Dual-mode API client: Production uses Supabase PostgREST + RPCs directly; dev uses FastAPI backend.
Key optimizations:
get_landing_data()
RPC replaces 7+ API calls- Database views:
v_trending
,v_top_rated
,v_rising
, etc. - Full-text search via PostgreSQL tsvector + GIN index
- HTTP caching: 5min (trending) → 1hr (stats)
cd backend
python3.12 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # Add GITHUB_TOKEN
uvicorn app.main:app --reload
cd frontend
npm install
npm run dev
┌─────────── GitHub Actions ───────────┐
│ sync.yml (8h) · deploy.yml · newsletter.yml (Mon) │
└──────┬──────────────┬────────────────┬──┘
▼ ▼ ▼
Supabase GitHub Pages Resend API
(PostgreSQL) (React SPA) (Emails)
GITHUB_TOKEN=ghp_xxx # GitHub API (required)
SUPABASE_DB_URL=postgresql:// # Production database
RESEND_API_KEY=re_xxx # Email service
ADMIN_TOKEN=sk-xxx # Admin API auth
SYNC_INTERVAL_HOURS=8 # Sync frequency
- Scoring Algorithm — Full scoring design
- System Architecture — Detailed pipeline reference
▸ 展开全文
04-26 16:02 · 财经,财经
【科华数据：拟5.69亿元出售控股子公司科云辰航80%股权】科华数据(002335.SZ)公告称，公司拟将持有的控股子公司广东科云辰航计算科技有限责任公司80%股权转让给广州联云贰号商业管理有限公司。以2025年12月31日数据测算，分红后标的公司100%股权交易对价约为7.11亿元，公司所持80%股权交易对价约为5.69亿元。
科华数据(002335.SZ)公告称，公司拟将持有的控股子公司广东科云辰航计算科技有限责任公司80%股权转让给广州联云贰号商业管理有限公司。以2025年12月31日数据测算，分红后标的公司100%股权交易对价约为7.11亿元，公司所持80%股权交易对价约为5.69亿元。本次交易完成后，公司将不再持有科云辰航股权，其不再纳入合并报表范围，但公司将继续作为其运营管理方提供数据中心运营服务。该事项已经董事会审议通过，尚需股东会批准。
▸ 展开全文
04-26 16:02 · 财经,财经
【恒宇信通：筹划购买神导科技100%股权 股票停牌】恒宇信通(300965.SZ)公告称，公司正在筹划通过发行股份及支付现金方式购买北京神导科技股份有限公司100%股权，预计构成重大资产重组。因有关事项尚存不确定性，为维护投资者利益，公司股票自4月27日开市起停牌，预计停牌时间不超过10个交易日。
恒宇信通(300965.SZ)公告称，公司正在筹划通过发行股份及支付现金方式购买北京神导科技股份有限公司100%股权，预计构成重大资产重组。因有关事项尚存不确定性，为维护投资者利益，公司股票自4月27日开市起停牌，预计停牌时间不超过10个交易日。公司预计在停牌期间披露交易方案并申请复牌；若未能在期限内披露，最晚将于5月14日开市起复牌并终止筹划相关事项。目前，公司已与神导科技主要股东签署《收购意向协议》，最终交易方案以后续公告为准。
▸ 展开全文
04-26 16:02 · 科技,GitHub
Automated AI news aggregation that feeds directly into your Obsidian vault. Stay current on the latest LLM research, trending repos, and breakthroughs — curated daily
Your morning briefing, hand-picked from the places that matter.
⭐ If this saves you time, give it a star. It helps others find it.
Researchers · ML engineers · Obsidian users · Anyone who wants to stay current without the morning rabbit hole
Latent Feed is a little tool that scours your favourite corners of the internet—papers, repos, threads, and discussions—then bundles the good stuff into one tidy digest. No more tab-hopping. Just set it up once, let it run daily, and wake up to a fresh batch of what’s worth reading.
You choose where it lands: your Obsidian vault, your inbox, or both.
Features: Run once a day · Clean digest by topic · Write to Obsidian or email
→ Examples — Email · Obsidian daily note
Click to expand — quick sample
## 🏢 Frontier Labs
**[Claude 4.5 Released](https://anthropic.com/...)** · anthropic
> New model with improved reasoning.
## 📄 Research
**[Transformers Are Bayesian Networks](https://arxiv.org/...)** · arxiv
> Connections between attention and probabilistic inference.
## 🔓 Open Source Repos
**[vLLM v0.6.0: Prefix Caching + LoRA](https://github.com/...)** · github
> 5x faster serving.
We pull from six places. Toggle any on or off in your config.
Add your own RSS feeds in config.yaml
under blogs.feeds
.
Items are auto-tagged into 10 categories. Edit processor/categorizer.py
to adjust keywords.
Latent Feed doesn’t fetch these, but they’re worth following:
YouTube — Andrej Karpathy, Stanford Online (CS25), Anthropic, Yannic Kilcher, DeepLearning.AI, Dwarkesh Patel
Courses — Karpathy: Zero to Hero, Stanford CS25: Transformers
pip install -r requirements.txt
cp config/config.example.yaml config/config.yaml
Open config/config.yaml
and at least set your vault path:
vault_path: "/path/to/your/obsidian-vault"
daily_notes_subfolder: "LatentFeed"
If you want short summaries for each item, set your API key:
export ANTHROPIC_API_KEY="your-api-key"
And in config.yaml
:
use_summarization: true
llm_provider: "claude"
Add this to config.yaml
:
email:
enabled: true
subscribers:
- "you@example.com"
smtp:
host: "smtp.gmail.com"
port: 587
from_addr: "your-bot@gmail.com"
subject: "Latent Feed — {date}"
Then set your SMTP credentials (prefer env vars):
export SMTP_USER="your-bot@gmail.com"
export SMTP_PASSWORD="your-app-password"
💡 Gmail users: use an App Password, not your usual login.
python main.py
Try it in 30 seconds — preview the digest with no config:
git clone https://github.com/<you>/latent-feed && cd latent-feed
pip install -r requirements.txt
python main.py --dry-run
Add a cron job so it runs at 8 AM:
crontab -e
# Add this line:
0 8 * * * cd /path/to/latent-feed && /path/to/python main.py
If your vault lives in a Git repo, you can run Latent Feed on a schedule:
-
Add these Secrets in your repo settings:
ANTHROPIC_API_KEY
(if you use summaries)VAULT_PATH
(path to your vault)SMTP_USER
,SMTP_PASSWORD
(if you use email)
-
If the vault is in another repo, clone it in the workflow first, then pass
VAULT_PATH
.
⚠️ Most local vaults can’t be reached from GitHub. For email-only, use--email-only
with your SMTP secrets.
These files are in .gitignore
and must never be committed to GitHub:
- Copy
config/config.example.yaml
toconfig/config.yaml
- Copy
.env.example
to.env
and fill in your real values - Never commit
config.yaml
or.env
— they hold private data
latent-feed/
├── main.py # Entry point
├── sources/ # News sources
├── processor/ # Filter, dedup, categorize, summarize
├── output/ # Markdown, Obsidian, email
├── models/ # Data models
├── config/ # Configuration
└── .github/workflows/ # GitHub Actions
- CONTRIBUTING.md — How to contribute
Found this useful? Star the repo, share it with a colleague, or post about it. Every bit helps.
MIT
▸ 展开全文
04-26 16:02 · 科技,GitHub
自动发布 24 小时情报系统：好文章的 80% 在于选题。Hunter AI 自动从 GitHub Trending、Twitter、HackerNews Reddit 等平台采集热点，用 AI 判断哪些值得写，自动生成文章和插图，自动发布。
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
空白电脑双击即可运行，自动下载 Python + 所有依赖！
# Mac / Linux
bash run.sh
# Windows（双击运行）
run.bat
首次运行需下载环境约 3-5 分钟，之后秒启动
启动后在 Web UI 的「⚙️ 配置」中填入 Gemini API Key，点击保存即可。
# CLI 运行
uv run hunter run -t github # GitHub 模板
uv run hunter run -t pain # 痛点诊断
uv run hunter run --dry-run # 试运行，不推送
┌─────────────────────────────────────────────────────────────────┐
│ Hunter AI 6-Skill 数据流 │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Topic ──→ Research ──→ Structure ──→ Write ──→ Package ──→ Publish
│ 选题判断 深度调研 结构设计 内容写作 封装优化 发布推送
│ │
│ 数据源 ──────────→ AI 分析 ──────────→ 内容输出 ──────────→ 推送
│ GitHub/Twitter/ Gemini 2.0 Markdown PushPlus
│ Reddit/HN/小红书 Flash 公众号文章 微信通知
│ │
└─────────────────────────────────────────────────────────────────┘
# config.yaml
gemini:
api_key: "你的 Gemini API Key"
📝 完整配置示例
# AI 大模型配置（必填）
gemini:
api_key: "你的 API Key"
model: "gemini-2.0-flash"
# GitHub 配置
github:
token: "ghp_xxx" # 可选，提高配额
min_stars: 200
# Twitter 配置
twitter:
cookies_path: "data/cookies.json"
# 小红书配置
xiaohongshu:
cookies: "" # 浏览器 F12 复制
# 推送配置
pushplus:
token: "你的 Token"
enabled: true
# 公众号人设
account:
name: "AI技术前沿"
tone: "专业且引人入胜"
niche: "AI技术"
hunter-ai-content-factory/
├── src/
│ ├── intel/ # 📡 数据采集层
│ ├── templates/ # 📋 内容模板
│ ├── factory/ # 🏭 内容生产
│ └── utils/ # 🔧 工具函数
├── data/ # 数据存储
├── output/ # 输出目录
└── config.yaml # 配置文件
推送成功但公众号没有文章？
PushPlus 推送到的是个人微信聊天窗口，不是公众号。
查看：微信 → 「PushPlus 推送加」公众号 → 聊天窗口
发布：从 output/
目录复制文章，手动发布到公众号
GitHub API 返回 403？
API 限流，配置 github.token
提高配额（60→5000 次/小时）
小红书/Twitter 采集失败？
需要配置对应平台的 Cookie，详见配置说明
欢迎提交 Issue 和 Pull Request！详见 贡献指南
▸ 展开全文
04-26 16:01 · 科技,GitHub
Official repository for "Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends".
- [2026/01/15] 🚀 Project Website: https://baijia.online/homepage/memory_survey.html
- [2026/01/15] 🚀 Full survey released! We establish a unified framework for AI memory mechanisms.
- [2026/01/07] 🚀 Project repository initialized.
This survey presents a comprehensive overview of AI memory mechanisms anchored in a unified theoretical framework. We propose a structured "4W Memory Taxonomy" to enable consistent analysis across diverse architectures. Building on this foundation, we systematically review memory systems in both single- and multi-agent contexts, examining their architectures, functions, applications, and evaluation methodologies. By synthesizing cognitive theories with engineering benchmarks, this work provides a coherent roadmap for advancing the theoretical understanding and technological development of AI memory.
To clarify the scope of AI memory, we distinguish between three interrelated layers:
- LLM Memory: The low-level computational kernel for prediction, consisting of Parametric Weights (static) and Context Window (runtime).
- Agent Memory: The functional workflow supporting autonomous operation and complex task execution via perception-planning-action loops.
- AI Memory: The overarching cognitive concept aimed at lifelong evolution, long-term persistence, and adaptation.
We establish a structured 4W Memory Taxonomy to enable consistent analysis across diverse architectures:
- When (Lifecycle Dimension): Examines the temporal span of memory, including Transient (extremely short-lived), Session (task duration), and Persistent (cross-session retention).
- What (Type Dimension): Categorizes by the nature of stored information, including Procedural (skills), Declarative (facts), Metacognitive (reflections), and Social/Personalized (user models).
- How (Storage Dimension): Explores technical implementation, from Implicit Storage (Parametric/Latent) within model weights to Explicit Storage (Raw Text, Vector DB, or Structured Graphs) outside the model.
- Which (Modality Dimension): Classifies by information formats, covering Single-modal (text-only) and Multimodal (fusing images, audio, and video).
Effective collaboration within MAS hinges on communication mediated by memory sharing. We organize these mechanisms into two core dimensions:
- Communication Mechanisms: Ranges from Explicit Communication (interpretable symbols like natural language or structured schemas) to Implicit Communication (latent representations/hidden embeddings).
- Memory Sharing Mechanisms: Categorized into Task-Level (experience accumulation and knowledge transfer) and Step-Level (precise context allocation and role-aware filtering).
We categorize memory evaluation into four essential dimensions to provide a structured assessment of agent memory:
If you find this survey or the established taxonomy helpful in your research, please cite our work
@article{baisurvey,
title={Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends},
author={BAI, TING and FAN, JIAYANG and WEN, XIAOSHUAI and KANG, JIAZHENG and LAN, HENGZHI and ZHAO, RUOCHEN and WU, PINGZHENG and ZHANG, ZEPENG and ZHONG, YUTIAN and LI, GEZI and others}
}
▸ 展开全文
04-26 16:01 · 科技,GitHub
⭐ Open Source Marketing Playbook — How to get 10k+ GitHub stars. Complete OSS launch strategy from AFFiNE (33k stars in 18 months). GitHub trending, HackerNews, developer community.
💡 Need a 1-on-1 architecture call for your B2B/Open-source launch? Book a session for $200 (Crypto/USDT and Wire Transfer accepted) — Contact @Iris_carrot on Telegram
Or visit gingiris.com — Iris's consulting practice with 1:1 sessions, startup coaching, and enterprise retainer options.
💡 Why Star this?
- Battle-Tested: Documents the exact strategies used for 30x PH #1 and 60k Stars.
- AI-Native: Battle-tested AI Agent Skills to automate your next 10k stars.
- PH Strategy: Access the playbook that earned 30x Product Hunt #1.
🌱 Our Philosophy
"创业没有那么高大上，创业就是做生意。" — Startups aren't glamorous; they're just business.
"痛点永远比痒点值钱。" — Pain points are always worth more than nice-to-haves.
"长期信任比快钱更难，也更值钱。" — Long-term trust is harder than quick money, and worth more.
This playbook is built on real failures and hard-won wins from scaling open source projects globally.
💼 Work With Iris
1. Strategic Consultation (1v1)
2. Advisory Retainer
3. Playbooks & Templates
📩 Contact @Iris_carrot on Telegram — Crypto/USDT and Wire Transfer accepted
This playbook is available as a ClawdHub Skill — install it directly into Claude Code, OpenClaw, or any compatible AI agent:
clawhub install gingiris-opensource
Your AI agent can then help you execute OSS marketing strategies and track your GitHub growth.
If you find this playbook useful, a GitHub Star ⭐ helps others discover it!
From the team behind AFFiNE (60k+ stars). This playbook contains the exact strategies that achieved:
Real results from developers using this playbook:
📢 Grew your GitHub stars with this playbook? We'd love to feature your story! Submit your case study
Version: 5.0 (Final) | Author: Iris (生姜iris)
The essence of open source: ask developers to contribute influence (love), and ask B2B customers to contribute money 💰. Through proper SOP operations, open sourcing can convert into hundreds or thousands of stars and bring many SMB leads. Prepare your business model before open sourcing — otherwise you'll waste the traffic this action brings.
Beyond standard launch ops, these force multiplier tactics can bring hundreds of stars in a single coordinated push.
Why it works: Students are highly engaged, love discovering new tools, and share aggressively within their networks.
🎯 Pro tip: India has the highest concentration of student hackathon activity globally. One well-connected sponsor can mobilize 5-10 events.
Why it works: Technical KOLs have pre-qualified audiences. A database expert's followers are database developers — exactly who you want.
🎯 Pro tip: Don't spray-and-pray. Find 3-5 KOLs whose user base perfectly overlaps your target. Quality > quantity.
Outreach template:
Hey [Name], loved what you built with [Project].
We just open-sourced [Tool] — solves [specific problem] for [audience].
Would you be open to sharing with your community if you find it useful?
Happy to return the favor or collaborate on content.
Why it works: Existing relationships = speed + reliability. No cold outreach friction.
Execution framework:
- List all influencers you've worked with in past 12 months
- Segment by response speed and audience size
- Stagger asks across launch week for sustained momentum
- Prep shareable assets (1-liner, screenshot, thread draft)
🎯 Pro tip: You can control the pace of star growth. Need a spike on Day 3? Activate your fastest responders that morning.
Why it works: Devtool founders and teams actively scout for useful tools. They share discoveries with their communities.
Timing insight:
Day 1: Post goes out → crickets (people are busy)
Day 2: First reshares start appearing
Day 3: Compound effect kicks in
🎯 Pro tip: These are "slow burn" channels. Don't panic on Day 1. The engagement comes on Day 2-3 as people catch up on their feeds.
版本：5.0 (最终版) | 作者：Iris (生姜iris)
开源的本质是，请 developer 贡献爱（就是影响力），请 to B 客户贡献钱💰。开源这个 action 经过 SOP 的运营可以转化几百几千 star，带来很多 SMB 的 leads。准备好商业模式再开源，才能承接住开源这个 action 带来的流量，避免浪费。
除了常规发布运营，以下是杠杆型策略——单次协调推送可带来数百颗 Star。
为什么有效： 学生群体参与度高、热衷发现新工具、在圈子里传播速度极快。
🎯 实战技巧： 印度是全球学生黑客松活动最密集的地方。一个人脉广的赞助商可以撬动 5-10 场活动。
为什么有效： 技术 KOL 的受众是预筛选过的。数据库专家的粉丝就是数据库开发者——正是你要的人。
🎯 实战技巧： 不要广撒网。找 3-5 个用户群高度重叠的 KOL。质量 > 数量。
外联模板：
Hi [名字]，很喜欢你做的 [项目]。
我们刚开源了 [工具]——帮 [目标用户] 解决 [具体问题]。
如果你觉得有用，愿意分享给你的社区吗？
很乐意回馈或者一起做内容合作。
为什么有效： 已有关系 = 速度 + 可靠性。没有冷启动的摩擦。
执行框架：
- 列出 过去 12 个月合作过的所有 influencer
- 分层 按响应速度和受众规模分组
- 错峰 在发布周内分散请求，保持持续势头
- 备好 可直接用的素材（一句话介绍、截图、推文草稿）
🎯 实战技巧： 你可以控制 star 增长的节奏。Day 3 需要一波高峰？当天早上激活你最快响应的那批人。
为什么有效： DevTool 创始人和团队会主动寻找有用的工具。他们会把发现分享给自己的社区。
时间节奏洞察：
Day 1: 帖子发出 → 没啥动静（大家在忙）
Day 2: 第一波转发开始出现
Day 3: 复利效应启动
🎯 实战技巧： 这些是**「慢热」渠道**。Day 1 别慌。互动会在 Day 2-3 到来，等大家刷到你的内容。
Want all four playbooks in one comprehensive package? Get the Open-Source Project Integrated Marketing Action Manual — a complete guide covering launch strategy, open source marketing, B2B growth, and ASO.
Iris (生姜iris) — Former cofounder & COO of AFFiNE, grew the project from 0 to 60k+ GitHub stars.
- 🐦 Twitter: @WeiYipei
- 💼 LinkedIn: Yipei Wei
- 💬 Telegram: @Iris_carrot
- 📱 即刻: 生姜iris
- 📧 公众号: 云宝的桃
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04-26 16:01 · 科技,GitHub
1,300+ curated trending AI image prompts from X/Twitter, ranked by engagement. Works with NanoBanana Pro, GPT Image, Midjourney
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04-26 16:01 · 开源,自动化,AI技能生成,GitHub趋势,技术集成
Auto-generated agent skills from GitHub trending — by ara.so
Auto-generated agent skills from GitHub's trending open source projects, byara.so.
Ara— instant AI agent environments in the cloud.
Auto-generated agent skills from GitHub trending — by ara.so
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