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A collection of pro tips for mastering Long-Term Memory in Pieces—unlock smarter workflows, richer context, and more powerful AI assistance.

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Pro Tips: Becoming an Expert at Tapping into Your Artificial Memories in Pieces

A collection of pro tips for making the most out of Pieces—unlock smarter workflows, richer context, and more powerful AI assistance with Long-Term Memory, Workstream Activity, and efficient navigation.

What is Long-Term Memory?

Pieces Copilot uses Long-Term Memory (LTM) to remember your work throughout the day. As you work across different applications, Pieces passively captures context from what you're doing—creating a searchable memory you can query anytime using natural language.

Think of it like having a perfect memory of everything you've worked on, researched, or discussed—ready to help you find exactly what you need, when you need it.

🎉 Latest Release

Released: December 23, 2025 | Desktop: 5.0.0 | PiecesOS: 12.3.4

Our most significant update yet! Discover the new features:

  • 🏡 A New Home Base — Unified navigation and consistent UI experience
  • 🔍 Browse, Converse, Generate — Powerful in-place filtering without leaving your flow
  • 👤 Personalization & Disambiguation — Smarter understanding of your work and team dynamics
  • ☀️ Single-Click Summaries — Morning Brief, Day Recap, Standup, and more
  • ⚙️ Behind the Scenes — Core engine improvements for better performance and accuracy

Read the full release notes →

What's in This Repository

This repository contains practical guides and examples to help you get the most out of LTM queries, Workstream Activity, and navigation in Pieces:

Tested, practical examples organized by use case:

  • 5 Daily Work Queries: Standup generators, command line power user patterns, context restoration
  • 3 Reflective Queries: Week in review, work pattern analysis, weekly highlights
  • 2 Analytical Queries: Time efficiency audits, code pattern identification
  • Query construction patterns and best practices
  • Usage recommendations for integrating LTM into your workflow

A comprehensive guide covering:

  • The 5 Keys to Great LTM Queries: Time, Source, Gestures, Topic, and People
  • Detailed strategies for each query element with real-world examples
  • Combining strategies for maximum effectiveness
  • Troubleshooting tips when queries don't return what you need
  • Quick reference cheat sheets for common patterns

A guide to your automatic work journal:

  • How Workstream Activity works: Event capture and 20-minute summary generation
  • Timeline view: Browse, search, and interact with your work history
  • Interactive summaries: View, search, start chats, and share
  • Pro tips for maximizing value from your workstream summaries
  • Common workflows for context restoration and daily planning

Navigate efficiently in Pieces:

  • What the Power Menu is and how to access it
  • Quick navigation between Workstream Activity and Pieces Copilot
  • Keyboard shortcuts and search tips
  • Common workflows for seamless context switching
  • Pro tips for staying in the flow

A simple 4-step process to update Pieces on Linux:

  • Step 1 - Shutdown: Gracefully quit both Desktop App and PiecesOS
  • Step 2 - Check Versions: Compare installed versions (optional)
  • Step 3 - Update: Refresh Snap packages
  • Step 4 - Launch: Restart PiecesOS, then Desktop App
  • Troubleshooting tips for common issues

Quick Start

  1. Ready to practice? Start with the 10 example queries and try them yourself
  2. New to LTM? Dive into the comprehensive guide to understand the fundamentals
  3. Want to get the most out of Workstream Activity? Learn how your automatic work journal captures and summarizes your work
  4. Need to navigate faster? Learn the Power Menu to jump between views seamlessly
  5. Want to become a power user? Experiment with combining different query elements and refine your approach

Key Principles

The best LTM queries combine these elements:

  • Time — When did it happen? ("yesterday", "last week", "in August")
  • 📱 Source — Where did it happen? ("in VS Code", "from Teams", "in Chrome")
  • Gestures — What were you doing? ("copied", "searched", "created")
  • 🎯 Topic — What project or theme? ("customer portal authentication", "cloud security")
  • 👥 People — Who were you working with? ("Sarah", "the security team")

Remember: Write naturally, like you're asking a colleague. You don't need all five elements—even one or two will get you great results.

What LTM Captures

LTM automatically captures context from:

  • Code editors and IDEs (VS Code, IntelliJ, etc.)
  • Web browsers (Chrome, Firefox, Safari)
  • Communication tools (Teams, Slack, Discord)
  • Documentation platforms (Notion, Confluence, GitHub)
  • Terminal and command line

All of this becomes searchable through natural language queries—no need to remember exact details or file names.

Getting Started

The best way to get good at LTM queries? Just start asking. Try different phrasings, experiment with time references, and see what works. You'll get the hang of it quickly.

For questions or support, reach out to your Pieces administrator or check out the Pieces documentation.


Unlock the full potential of your work with Pieces—learn LTM queries, Workstream Activity, and efficient navigation.

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A collection of pro tips for mastering Long-Term Memory in Pieces—unlock smarter workflows, richer context, and more powerful AI assistance.

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