Chalked vs Doop: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Chalked and Doop — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Chalked
Mates Rates Services Pty
Mac assistant that drafts the reply you'd actually send, grounded in the open conversation, your calendar and kept commitments.
Key features
- Context-grounded replies: Reads the open conversation via Accessibility and drafts the reply you would actually send
- Tab to insert: Prepared drafts land in the existing Messages composer and are inserted with a single Tab press
- fn to redirect: Hold fn and say what you want instead to steer the draft before inserting it
- Commitment ledger: Captured commitments keep their source and status, and superseded entries are marked rather than duplicated
- Calendar grounding: Pulls availability from your calendar so proposed times are real, not invented
- Global fn dictation: Works anywhere else on the Mac even outside eligible Messages threads
- No screenshots or recording: On-screen text is read through Accessibility and stays on your Mac
- Minimum context by design: Requests only the thread and verified facts relevant to the reply
Best for
- An agency operator answers a client asking about a delivery date without re-checking the calendar and the approved budget by hand
- A consultant returns to a client thread days later and replies with the engagement's commitments intact instead of rebuilding them from memory
- A founder moves between customer, investor and team conversations in one sitting without losing which promise belongs to whom
- A team lead confirms a meeting time that is actually free because the draft was written against the live calendar
- Someone dictates a longer message into any Mac app using the global fn shortcut rather than typing it out
- A user corrects a suggested reply by voice — changing a day or a cap — before it is inserted and sent
Doop
Kevin Goedecke
Open-source infinite design canvas where humans and AI agents design together live, with agents joining through a built-in MCP server.
Key features
- Agent-Native MCP Canvas: Agents connect over an HTTP MCP endpoint with a single command and one browser OAuth approval, then edit the canvas as you, attributed and accountable, with no API keys handed over.
- Streaming Frames: Every section an agent writes renders on the canvas the moment it lands, so you watch the design arrive rather than waiting on a spinner.
- Comments as Tasks: A note left anywhere on the canvas becomes a task the right agent picks up, works on, and replies to with a screenshot, turning feedback directly into the backlog.
- Agent Self-Review: A built-in headless renderer gives agents screenshots of their own frames so they judge fit, spacing and contrast like a senior designer and correct issues before handoff.
- Shared Canvas Memory: Tasks, decisions and comments live on the canvas rather than in one agent's context, so any agent that joins later plugs into the same state and continues.
- Learned Taste Profile: Casual feedback such as 'rounder corners' or 'keep it to the blue' is distilled into a persistent taste profile applied to every new frame and inherited by every agent.
- Live Export URLs: Each frame is a URL that can be embedded in a doc, a post or an og:image and re-renders whenever the design changes, so shared assets never go stale.
- Reference and URL Import: Paste screenshots to have agents distill palette, type and mood into a written brief, or paste a public URL to land an editable snapshot of your existing page on the canvas for side-by-side variants.
Best for
- Agent-Assisted Landing Pages: Steering Claude Code or Codex through hero, pricing and footer frames on one canvas and watching each render live.
- Design Review Loops: Leaving contrast or spacing notes on a frame and letting an agent apply the fix and return a screenshot without a synchronous handoff.
- Redesign Comparison: Importing an existing public page as an editable snapshot so agent-generated variants sit next to the original instead of replacing it blind.
- Team Design Sessions: Multiple people and multiple agents working the same canvas, each seeing what the others' agents are doing in real time.
- Style Consistency: Building a canvas taste profile once so every subsequent frame and every new agent inherits the same corner radius, palette and type decisions.
- Always-Fresh Shared Assets: Embedding live frame URLs in documentation or social posts so the shared image updates automatically when the design changes.