Echomindr

io.github.echomindr由 GitHub 账号 echomindr 发布v1.0.0更新于 Nov 2, 2026

Real founder decisions, lessons and signals from 100+ podcasts — searchable by AI agents.

已验证SSE可网页运行知识与记忆商业与电商

概览

AI 生成的概览

Echomindr 让助手检索播客中真实创始人的经历,包含原话、决策、结果与来源链接。

可以做什么
  • 用情境描述做语义搜索,查找创始人经历,可按阶段和类型筛选
  • 查看单条经历的完整细节:原话、决策、结果、教训、阶段、标签与带时间戳的来源链接
  • 查找与某条经历主题相近的其他创始人故事
  • 浏览情境分类体系与数据库统计信息
适用场景
适合在用户询问创业或经营建议时,让助手用真实创始人的做法而非泛泛之谈来回答,例如定价、融资、增长、团队或逆境等问题。
运行要求
需要远程 MCP 端点 echomindr.com;README 另给出本地 Python 方式,使用 ECHOMINDR_API_URL 环境变量。清单未声明账号、API 密钥或请求头。需要网络访问。
安装前请注意
该服务是对第三方播客内容的只读检索,查询内容会发送到 echomindr.com。自建完整数据库需要自备播客转录文本和 Claude API 密钥,会产生调用费用。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Echomindr,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 SSE。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.echomindr%2Fechomindr/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "echomindr": {
      "type": "sse",
      "url": "https://echomindr.com/mcp/"
    }
  }
}

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)

bash
# Search for founder experiencescurl "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 detailscurl "https://echomindr.com/moments/{id}"
# Find similar momentscurl "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):

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 situation
  • get_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:

bash
git clone https://github.com/echomindr/echomindr.gitcd echomindrpip install -r requirements.txt
# Build a sample databasepython echomindr_build_db.py --sample
# Start the APIpython 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

EndpointMethodDescription
/searchGETFull-text search with stage/type filters
/situationPOSTDescribe a situation, get matching experiences (vector search)
/moments/{id}GETFull moment detail
/similar/{id}GETSimilar moments by shared tags
/taxonomyGET52 canonical situations across 10 families
/statsGETDatabase statistics
/llms.txtGETLLM-optimized API description
/docsGETSwagger 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".

来源:README.md,提交 2915af6

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版本历史

1
  1. v1.0.0最新Sep 16, 2026

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