capture

Created By
Bert D3 months ago
Give Claude and Cursor persistent memory of everything you've captured — emails, decisions, web research.
Overview

capture is a memory layer that ingests emails, Slack threads, web pages, LinkedIn posts, and documents, then makes them queryable by any MCP-compatible AI assistant. Connect your MCP server URL to Claude, Cursor, or any agent and your entire captured knowledge base becomes available in context — instantly. Capture also extracts decisions, action items, and project history so agents can get up to speed without interrupting your team. No extra workflow required. Forward an email, save a page, and your AI tools get smarter.

Tools exposed:

get_project_summary Get a summary of a project including recent decisions, top topics, and active stakeholders.

recall_decision_history Retrieve the decision history for a specific topic or keyword. Returns the 'Why' behind past choices.

identify_stakeholders Return a 'who-to-talk-to' map for a given topic — people who have made or been named in decisions about it.

fetch_warmup_dossier Generate a 2-minute project briefing covering recent decisions, open questions, and key stakeholders. Use this to get up to speed on a project fast.

search_captures Search through your captures by keyword or topic

get_recent_captures Get your most recent captures

get_action_items Get your pending action items from captures

get_focus_areas Get your current focus areas

get_interests Get your inferred interests

get_morning_briefs Get your recent morning briefs with AI-curated action items, insights, and patterns

More at: https://captureapp.me

Project Info
Created At
3 months ago
Updated At
3 months ago
Author Name
Bert D
Star
-
Language
-
License
-
Category

Recommend Servers

View All
Docwand

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

14 hours ago