DoneThat vs Pieces
Developer tool for saving code snippets, context, and AI assistance.
AI Memory: side-by-side
Tools to archive your digital life and make it available for later use by eg AIs.
| Tool | Input data | Platform support | Data location | API |
|---|---|---|---|---|
Privacy-first work tracker and AI coach Best for: Calendar-like timeline of your work. | Screen, audio, apps | macOS, Windows, Linux | Cloud | Yes |
Developer tool for saving code snippets, context, and AI assistance. Best for: Developers who want to save and reuse code snippets with AI help. | Code snippets, context | macOS, Windows, Linux | Local/cloud | Yes |
Pieces is best for
Developers who want to save and reuse code snippets with AI help.
DoneThat is best for
Calendar-like timeline of your work.
See the full ai memory comparison or visit Pieces.
AI-generated comparison
Prompt
Compare https://donethat.ai/ and https://pieces.app/ honestly. Check both websites yourself, then summarize where DoneThat is stronger and where Pieces is stronger. Do not rank them globally.
Result
DoneThat and Pieces both passively capture what you do on your computer, but they aim at different buyers: DoneThat positions itself as an automated time tracker and AI productivity coach for founders, PMs, consultants, and freelancers, while Pieces positions itself as a long-term memory layer that feeds context into LLMs and dev tools for developers at larger companies.
DoneThat stronger
- DoneThat publishes concrete pricing tiers ($4, $8, $16/month) and a 14-day trial right on the home page, so a buyer can self-qualify in seconds.
- DoneThat's outcome pitch is specific and quantified (double productivity, ~23 hours/week saved across six categories) rather than abstract second-brain language.
- DoneThat targets a broader knowledge-worker audience (founders, PMs, consultants, freelancers) instead of being scoped to developers, which is a better fit if you're not writing code all day.
- DoneThat surfaces an explicit AI coach persona ('Don') with daily/weekly reviews and goal-setting, making the behavior-change angle more tangible than Pieces' memory-retrieval framing.
- DoneThat highlights workflow automation hooks (API, MCP, Zapier, n8n, CSV export) that make it easier to push tracked time into existing billing, reporting, or ops stacks.
Pieces stronger
- Pieces is clearly built for developers, with first-class plugins for VS Code, Chrome, GitHub Copilot, Claude, and Cursor that DoneThat doesn't advertise.
- Pieces leans on a named proprietary engine (LTM-2) and a 9-month memory window, which reads as a more defensible technical moat for context-heavy work.
- Pieces' core use case, feeding accurate personal context into LLMs across tools, is a sharper fit if your main goal is better AI answers rather than time accounting.
- Pieces emphasizes local-first, on-device processing as the default, which will land better with security-sensitive enterprise buyers evaluating an always-on capture tool.
- Pieces covers Windows, macOS, and Linux on the home page, giving Linux-based developer teams an option DoneThat doesn't visibly call out.
Explore related pages
Productivity Coaching
Meet Don, the AI coach inside DoneThat that turns your work patterns into practical coaching, goals, and next steps.
ExploreFeature Overview
Explore DoneThat features for AI time tracking, automatic summaries, productivity coaching, privacy controls, and team visibility.
ExplorePrivacy
Learn how DoneThat protects your data with privacy controls, secure architecture, local capture options, and AI data handling.
ExploreReady to try DoneThat?
Fully automated AI time tracking and coaching. 14-day free trial.