Chalked vs dif.sh: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Chalked and dif.sh — 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- Commitment ledger: Captured commitments keep their source and status, and superseded entries are marked rather than duplicated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
dif.sh
Dif
Feature flags and A/B tests defined as markdown files in your repo, with a generated context file coding agents read on session start.
Key features
- Markdown-Defined Experiments: One .md file per flag or test holds id, status, owner, surface, hypothesis, audience, variants, metrics and guardrails in frontmatter, with the brief and rationale written below it.
- Agent Context File: Every dif build regenerates dif/context.json listing active flags, experiments, variants and recent learnings, which coding agents pick up on session start.
- Build-Time Conflict Detection: The build resolves an exclusion graph and refuses to compile when two live tests would bucket one user into both, so clashes break in CI instead of production.
- One Shape for Four Concepts: Feature flags, A/B tests, holdouts and staged rollouts are the same file format with different frontmatter, reducing the number of concepts and failure modes.
- Generated Typed Client: dif build emits a small typed client you import once at boot, then call per flag with control and variant branches — supported for web server, React and Svelte.
- Privacy-Preserving Targeting: Audience attributes such as country, plan or returning_visitor are declared in config.yaml while values arrive at runtime from your app's user context, so no customer data is committed.
- Structured Conclusion Workflow: dif conclude archives a finished experiment, drafts its Decision block and appends a line to the surface log so the next test on that screen starts informed.
- Flexible Result Routing: Add a Dif Cloud key and dif.track() computes lift with no join code, or run dif init --events custom to own the handlers that forward events to Segment, Amplitude or your warehouse.
Best for
- Keeping Experiments in Code Review: Ship flag and experiment changes through the same pull request flow as the code they gate, using git history as the audit trail.
- Giving Coding Agents Experiment Context: Let an AI coding agent see which experiments are live on a surface and what prior tests taught before it edits that screen.
- Preventing Overlapping Tests: Use exclusion groups to guarantee no user is bucketed into two conflicting experiments, caught at build time rather than discovered in the results.
- Running Staged Rollouts: Ramp a new feature to a growing share of traffic using the same file format as an A/B test, without learning a separate rollout tool.
- Instrumenting Without a Vendor Lock-In: Forward exposure and result events to an existing analytics warehouse instead of adopting a hosted experimentation database.
- Small-Team Experimentation on a Budget: Use the free CLI and SDK with self-owned event handlers before deciding whether the hosted metrics layer is worth paying for.