Echomindr

io.github.echomindr由 GitHub 帳號 echomindr 發布v1.0.0更新於 Nov 2, 2026

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

已驗證SSE可網頁執行知識與記憶商業與電商

概覽

AI 產生的概覽

Echomindr 讓助理檢索播客中真實創辦人的經歷,包含原話、決策、結果與來源連結。

可以做什麼
  • 以情境描述做語意搜尋,尋找創辦人經歷,可依階段與類型篩選
  • 查看單筆經歷的完整細節:原話、決策、結果、教訓、階段、標籤與含時間戳的來源連結
  • 找出與某筆經歷主題相近的其他創辦人故事
  • 瀏覽情境分類體系與資料庫統計資訊
適用情境
適合在使用者詢問創業或經營建議時,讓助理以真實創辦人的做法而非泛泛之談來回答,例如定價、募資、成長、團隊或逆境等問題。
執行需求
需要遠端 MCP 端點 echomindr.com;README 另提供本機 Python 方式,使用 ECHOMINDR_API_URL 環境變數。清單未宣告帳號、API 金鑰或請求標頭。需要網路連線。
安裝前請注意
此服務是對第三方播客內容的唯讀檢索,查詢內容會傳送到 echomindr.com。自行架設完整資料庫需自備播客逐字稿與 Claude API 金鑰,會產生呼叫費用。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Echomindr,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 SSE。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.echomindr%2Fechomindr/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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".

來源:README.md,提交 2915af6

工具

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SourceWeft 尚未讀取此伺服器的工具。

版本歷史

1
  1. v1.0.0最新Sep 16, 2026

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