io.github.echomindr/echomindr
编码与调试by echomindr
汇集 100+ 播客中的真实 founder 决策、经验与信号,支持被 AI agents 检索和分析。
什么是 io.github.echomindr/echomindr?
汇集 100+ 播客中的真实 founder 决策、经验与信号,支持被 AI agents 检索和分析。
README
Echomindr
3,500+ real founder moments from 60+ podcasts — searchable by AI agents.
Each moment: a named founder, a verbatim quote, a decision taken, an outcome observed, a lesson extracted — with a timestamped link to the source. Not summaries. Not paraphrases. What actually happened.
Why
AI agents give generic startup advice. Echomindr gives them access to what founders actually did.
Ask: "How did founders handle their first pricing?" Get: Kevin Hale's 10-5-20 rule, Josh Pigford charging $249/month from day one, Madhavan Ramanujam's options trick — with quotes, outcomes, and source links.
Ask: "What did founders do when they nearly ran out of money?" Get: Airbnb selling cereal boxes, Notion's near-collapse during COVID, Calm's years of slow growth before the breakout — directly from the founders who lived it.
Quick start
API (REST)
# Search for founder experiences
curl "https://echomindr.com/search?q=pricing&limit=5"
# Describe a situation, get matching experiences (vector search)
curl -X POST "https://echomindr.com/situation" \
-H "Content-Type: application/json" \
-d '{"situation": "B2B SaaS founder with free pilots that won'\''t convert to paid"}'
# Get moment details
curl "https://echomindr.com/moments/{id}"
# Find similar moments
curl "https://echomindr.com/similar/{id}?limit=5"
API docs: echomindr.com/docs
MCP (for AI agents)
Connect via remote MCP: https://echomindr.com/mcp/
Or add to Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"echomindr": {
"command": "python",
"args": ["echomindr_mcp.py"],
"env": {
"ECHOMINDR_API_URL": "https://echomindr.com"
}
}
}
}
3 MCP tools:
search_experience— semantic search for founder stories by situationget_experience_detail— full details of a moment (quote, decision, outcome, lesson)find_similar_experiences— related founder stories by shared themes
llms.txt
https://echomindr.com/llms.txt
Data
- 3,500+ moments from 340+ podcast episodes across 60+ shows
- 52 canonical situations across 10 thematic families (PMF, growth, pricing, fundraising, team, operations, resilience, strategy, founder psychology, hostile environments)
- 5 moment types: decision, problem, lesson, signal, advice
- 5 stages: idea, mvp, traction, scale, mature
- Sources: How I Built This, Lenny's Podcast, 20 Minute VC, Acquired, Y Combinator, My First Million, GDIY (Génération Do It Yourself), Disrupting Japan, Silicon Carne, Startup Ministerio, Kevin Kamis, Wall Street Paper, Valy Sy (China), Matt & Ari (Canada), Oscar Lindhardt (Denmark), Aidan Walsh (USA)
Each moment: summary · verbatim quote · decision · outcome · lesson · stage · tags · timestamp link
Self-hosting
To run your own instance with the sample data:
git clone https://github.com/echomindr/echomindr.git
cd echomindr
pip install -r requirements.txt
# Build a sample database
python echomindr_build_db.py --sample
# Start the API
python echomindr_api.py
# → http://localhost:8000/docs
To build the full database, you need your own podcast transcriptions and Claude API key. See echomindr_extract_v2.py for the extraction pipeline.
Architecture
Podcast audio → Deepgram (transcription) → Claude (extraction) → SQLite → FastAPI → MCP
The extraction pipeline turns long-form podcast interviews into structured, searchable moments. Each episode yields 8–15 moments on average. Semantic search uses BAAI/BGE-M3 embeddings (1024-dim) via sqlite-vec.
Endpoints
| Endpoint | Method | Description |
|---|---|---|
/search | GET | Full-text search with stage/type filters |
/situation | POST | Describe a situation, get matching experiences (vector search) |
/moments/{id} | GET | Full moment detail |
/similar/{id} | GET | Similar moments by shared tags |
/taxonomy | GET | 52 canonical situations across 10 families |
/stats | GET | Database statistics |
/llms.txt | GET | LLM-optimized API description |
/docs | GET | Swagger documentation |
License
MIT — the code is open source. The hosted database at echomindr.com is a managed service.
Built by Thierry — author of "The System That Learns Wins" and "Designing for Permanent Hostility".
常见问题
io.github.echomindr/echomindr 是什么?
汇集 100+ 播客中的真实 founder 决策、经验与信号,支持被 AI agents 检索和分析。
相关 Skills
前端设计
by anthropics
面向组件、页面、海报和 Web 应用开发,按鲜明视觉方向生成可直接落地的前端代码与高质感 UI,适合做 landing page、Dashboard 或美化现有界面,避开千篇一律的 AI 审美。
✎ 想把页面做得既能上线又有设计感,就用前端设计:组件到整站都能产出,难得的是能避开千篇一律的 AI 味。
网页应用测试
by anthropics
用 Playwright 为本地 Web 应用编写自动化测试,支持启动开发服务器、校验前端交互、排查 UI 异常、抓取截图与浏览器日志,适合调试动态页面和回归验证。
✎ 借助 Playwright 一站式验证本地 Web 应用前端功能,调 UI 时还能同步查看日志和截图,定位问题更快。
网页构建器
by anthropics
面向复杂 claude.ai HTML artifact 开发,快速初始化 React + Tailwind CSS + shadcn/ui 项目并打包为单文件 HTML,适合需要状态管理、路由或多组件交互的页面。
✎ 在 claude.ai 里做复杂网页 Artifact 很省心,多组件、状态和路由都能顺手搭起来,React、Tailwind 与 shadcn/ui 组合效率高、成品也更精致。
相关 MCP Server
GitHub
编辑精选by GitHub
GitHub 是 MCP 官方参考服务器,让 Claude 直接读写你的代码仓库和 Issues。
✎ 这个参考服务器解决了开发者想让 AI 安全访问 GitHub 数据的问题,适合需要自动化代码审查或 Issue 管理的团队。但注意它只是参考实现,生产环境得自己加固安全。
Context7 文档查询
编辑精选by Context7
Context7 是实时拉取最新文档和代码示例的智能助手,让你告别过时资料。
✎ 它能解决开发者查找文档时信息滞后的问题,特别适合快速上手新库或跟进更新。不过,依赖外部源可能导致偶尔的数据延迟,建议结合官方文档使用。
by tldraw
tldraw 是让 AI 助手直接在无限画布上绘图和协作的 MCP 服务器。
✎ 这解决了 AI 只能输出文本、无法视觉化协作的痛点——想象让 Claude 帮你画流程图或白板讨论。最适合需要快速原型设计或头脑风暴的开发者。不过,目前它只是个基础连接器,你得自己搭建画布应用才能发挥全部潜力。