Documents and notes decision
Pieces
Build a narrow personal-memory replacement is realistic (capture, local store, embeddings, summaries), but matching Pieces' breadth of native OS capture, many polished connectors, and enterprise features would require significantly more engineering and ops investment; keep paying for full-feature parity unless you only need a personal subset.
Visit website↗No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
Code Pieces publishes itself
Not a way out of the subscription — these are the vendor’s own repositories. Worth a look for how they build, and for anything you would have to integrate with.
- pieces-app/documentation★646 GitHub starsOfficial documentation for Pieces for Developers↗
- pieces-app/opensource★233 GitHub starsMITMain repo for tracking open source projects, issues, conversations, and links to each open source project.↗
- pieces-app/genai-101★216 GitHub starsThis repo hosts sample code for GenAI 101 with Pieces workshop↗
- pieces-app/obsidian-pieces★214 GitHub starsMITPieces' powerful extension for Obsidian-MD that allows users to access their code snippets directly within the Obsidian workspace↗
What a replacement has to do
- Continuously capture user context from apps, persist and index it locally, embed and search that history, and generate summaries / answers using an LLM.
What it still won’t have
- Production-grade native installers and cross-platform OS integrations (stable background agent)
- Out-of-the-box connectors to dozens of apps and enterprise capture policies
- A polished timeline UI and long-term reliability/tuning for capture frequency
- Enterprise MCP server with assistant integrations and org-wide key management
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Pieces does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal 'Pieces-like' personal memory desktop app using Electron + Node backend, SQLite for on-device storage, FAISS (or SQLite vector extension) for embeddings index, and OpenAI embeddings + LLMs for search and summarization. In scope: (1) background agent capturing focused-window metadata and clipboard events every few seconds and batching into event records; (2) Gmail connector via OAuth that incrementally fetches threads and writes metadata/content to SQLite; (3) embeddings pipeline to compute and store embeddings for events and a vector search endpoint; (4) simple timeline UI with time/source filters and an 'ask your history' chat that runs a vector search and then calls the LLM to produce summaries; (5) basic privacy controls: pause capture, delete time ranges, and per-source toggle; (6) tests for capture, ingestion, indexing, and query flows and error handling for network/credential failures. Out of scope: audio capture/transcription, enterprise MCP server, multi-user sync, and dozens of third-party connectors. Deliver runnable build instructions, automated tests, and simple CI that runs the test suite.
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- 3/3 assessment runs agreed+4
- Evidence score57
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 2
Every page the run actually retrieved.
- official productPieces — The memory layer for modern work
- official pricingPieces — Pricing
Integrity checks
What held up, and what did not.
