GhostWriter by MyHandler vs Reference: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GhostWriter by MyHandler and Reference — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
GhostWriter by MyHandler
MyHandler.ai
Windows writing assistant that reads the screen around your cursor on a hotkey double-tap and types a context-aware draft in your voice.
Key features
- Hotkey Double-Tap Drafting: No prompt or dictation is needed — a double-tap with the cursor in any text field triggers capture and drafting directly in place.
- Screen Context Capture: Reads the thread, form or partial sentence surrounding the cursor locally on the machine, using that on-screen context as the entire input.
- Writing Intent Classification: Determines whether the moment calls for a reply, a continuation, a filled-in answer or a fresh compose before writing a single word.
- Sender and Identity Mapping: Maps each message in a thread to its sender and works out which handle is the user's, so the draft answers the other party rather than the user's own words.
- Calendar Cross-Check: When a draft proposes a time, it is validated against the next two weeks of a connected calendar before the text appears.
- Selection Rewriting: Selecting existing text before the hotkey rewrites that selection instead of composing something new.
- Nothing Sent Automatically: The generated text appears at the cursor for the user to read, edit or delete; sending always remains a manual step.
- Local Vault with Zero-Retention Cloud: Capture runs against an encrypted vault on the user's own PC and the assembled context is processed by a zero-data-retention cloud model.
Best for
- Email Backlog: Clearing a queue of owed replies by drafting each one from the thread already on screen instead of retyping the same answer.
- Chat and Slack Replies: Answering a message in a team chat where the draft is grounded in who asked whom for what in the visible thread.
- Web Form Completion: Filling a blank answer box under a question on a web form or application without switching to a separate chat window.
- Sentence Continuation: Picking up a half-written paragraph exactly where it stops, without the assistant restating what was already typed.
- Meeting Scheduling Replies: Responding to a request for a time with a proposal that has already been checked against the user's calendar.
- Tone-Sensitive Rewrites: Selecting a blunt or rough draft and having it rewritten in the user's own voice before sending.
R
Reference
Rahul Thennarasu
Local, offline semantic search for your files and code — built for AI agents like Claude Code, with an MCP server.
Key features
- Local Semantic Index: Runs a local embedding model over your files and code so nothing leaves your machine.
- Live Auto-Reindexing: Updates the index as you save files, keeping search results current with the code you are actively writing.
- Code-Aware Chunking: Uses tree-sitter to chunk on functions and syntactic units, so results are cited down to the exact code region.
- Built-in MCP Server: Exposes /search, /explain, /find_similar and /check_doc_drift endpoints so Claude Code and other agents can query directly.
- Cited Answers: Every result points back to a file and function, replacing generic AI advice with grounded, verifiable references.
- Doc Drift Checks: /check_doc_drift flags places where documentation has fallen out of sync with the underlying code.
- Cross-Codebase Similarity: /find_similar surfaces analogous implementations elsewhere in the codebase for reuse and refactoring.
Best for
- Grounded Coding Q&A: Ask 'how did I implement rate limiting here' and get the actual function back, not a generic explanation.
- Claude Code Context Injection: Use the MCP server so Claude Code pulls precise cited snippets instead of running expensive grep loops.
- Refactoring Prep: Use /find_similar to locate analogous implementations across the repo before consolidating or standardizing them.
- Doc-Code Alignment: Run /check_doc_drift to catch documentation that no longer matches the code it describes.
- Offline / Air-Gapped Work: Semantic code search on machines that can't upload source to cloud embedding services.
- Cross-Project Recall: Reindex multiple repos locally to find prior solutions you already wrote in another project.