Vibecode Cotypist
track this build5 steps, step by step0%KINDA · weekend project
price $6/moyou'd save $72/yrbuild time multi-daycategory ⚙️ productivity utilitiesreplaced by 0 people
The idea is simple: watch what you type, predict the rest, show it as ghost text, accept with Tab. The prediction part is genuinely easy now, a small local model or even a personal n-gram table over your own writing gets you most of the way. The hard part is everything around it: reading the current text field and caret position through the macOS Accessibility API, drawing an overlay that tracks a moving cursor, and not breaking in Electron apps, Chrome, terminals, password fields, or anything that reimplements text editing. You can build something that works in native AppKit fields in a couple of long sessions and feels magic. Getting it to work everywhere, at typing latency, without eating keystrokes or leaking into your bank login, is where the actual product lives.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Cotypist. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Cotypist indie build ## Goal Build the smallest trustworthy replacement for the core Cotypist workflow for one developer or a tiny team. ## Scope A menu bar app that watches keystrokes via the Accessibility API, asks a local model for the next few words, and paints an inline ghost suggestion you accept with Tab. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals - Latency polish: suggestions that arrive after you already typed the words are worse than nothing - Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work - Personalization that actually improves over months of your writing rather than a one-off corpus dump - Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates If those capabilities are essential, use Cotypist instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a macOS menu bar app called GhostType that shows inline text predictions as I type. Stack, no alternatives: Swift 5.9+, SwiftUI for the menu bar UI, AppKit for the overlay window, one Xcode project, no external services. Core loop: 1. Register a CGEventTap for keyDown events so we can observe typing and intercept Tab and Escape. 2. Use the Accessibility API (AXUIElement) to read the focused element's AXValue and AXSelectedTextRange, and AXBoundsForRange to get the caret rect in screen coordinates. 3. Take the last 200 characters before the caret as context and request a completion of up to 6 words. 4. Render the suggestion as dim gray ghost text in a borderless, non-activating NSPanel positioned at the caret rect. Never modify the target app's text until accepted. 5. Tab accepts: insert the suggestion by posting synthetic key events. Escape dismisses. Any other keystroke re-predicts after a 120ms debounce. Prediction engine: ship two interchangeable providers behind a protocol. - Provider A, default: an on-device n-gram model built from a folder of my own .txt and .md files at first launch, stored in a SQLite file in Application Support. - Provider B: a local llama.cpp or MLX server at a URL from .env, called with a strict token limit and a 250ms timeout that is cancelled on the next keystroke. Safety, non-negotiable: - Never predict when secure input mode is active, when the focused element role is AXSecureTextField, or when the frontmost app bundle id is in a user-editable deny list. - No network calls except to the local provider URL. No telemetry, no accounts, no crash reporting. In scope: native AppKit text fields and text views, a menu bar toggle, a deny list editor, a corpus folder picker. Out of scope: Electron and browser text fields, terminals, iOS, sync, cloud models, multi-language tuning. Detect unsupported contexts and stay silent rather than guessing. Deliverables: the Xcode project, a README with the exact Accessibility and Input Monitoring permissions to grant and where to click, .env.example, and a plain description of what fails in Chrome and VS Code and why. ## Required capabilities - macOS with Xcode and a Swift toolchain - Accessibility and Input Monitoring permissions granted manually - a small local model via llama.cpp or MLX, or a personal n-gram corpus built from your own text - Apple Silicon strongly preferred for latency ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Cotypist. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Cotypist indie build ## Goal Build the smallest trustworthy replacement for the core Cotypist workflow for one developer or a tiny team. ## Scope A menu bar app that watches keystrokes via the Accessibility API, asks a local model for the next few words, and paints an inline ghost suggestion you accept with Tab. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals - Latency polish: suggestions that arrive after you already typed the words are worse than nothing - Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work - Personalization that actually improves over months of your writing rather than a one-off corpus dump - Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates If those capabilities are essential, use Cotypist instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a macOS menu bar app called GhostType that shows inline text predictions as I type. Stack, no alternatives: Swift 5.9+, SwiftUI for the menu bar UI, AppKit for the overlay window, one Xcode project, no external services. Core loop: 1. Register a CGEventTap for keyDown events so we can observe typing and intercept Tab and Escape. 2. Use the Accessibility API (AXUIElement) to read the focused element's AXValue and AXSelectedTextRange, and AXBoundsForRange to get the caret rect in screen coordinates. 3. Take the last 200 characters before the caret as context and request a completion of up to 6 words. 4. Render the suggestion as dim gray ghost text in a borderless, non-activating NSPanel positioned at the caret rect. Never modify the target app's text until accepted. 5. Tab accepts: insert the suggestion by posting synthetic key events. Escape dismisses. Any other keystroke re-predicts after a 120ms debounce. Prediction engine: ship two interchangeable providers behind a protocol. - Provider A, default: an on-device n-gram model built from a folder of my own .txt and .md files at first launch, stored in a SQLite file in Application Support. - Provider B: a local llama.cpp or MLX server at a URL from .env, called with a strict token limit and a 250ms timeout that is cancelled on the next keystroke. Safety, non-negotiable: - Never predict when secure input mode is active, when the focused element role is AXSecureTextField, or when the frontmost app bundle id is in a user-editable deny list. - No network calls except to the local provider URL. No telemetry, no accounts, no crash reporting. In scope: native AppKit text fields and text views, a menu bar toggle, a deny list editor, a corpus folder picker. Out of scope: Electron and browser text fields, terminals, iOS, sync, cloud models, multi-language tuning. Detect unsupported contexts and stay silent rather than guessing. Deliverables: the Xcode project, a README with the exact Accessibility and Input Monitoring permissions to grant and where to click, .env.example, and a plain description of what fails in Chrome and VS Code and why. ## Required capabilities - macOS with Xcode and a Swift toolchain - Accessibility and Input Monitoring permissions granted manually - a small local model via llama.cpp or MLX, or a personal n-gram corpus built from your own text - Apple Silicon strongly preferred for latency ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Cotypist. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Cotypist product brief ## Problem The idea is simple: watch what you type, predict the rest, show it as ghost text, accept with Tab. The prediction part is genuinely easy now, a small local model or even a personal n-gram table over your own writing gets you most of the way. The hard part is everything around it: reading the current text field and caret position through the macOS Accessibility API, drawing an overlay that tracks a moving cursor, and not breaking in Electron apps, Chrome, terminals, password fields, or anything that reimplements text editing. You can build something that works in native AppKit fields in a couple of long sessions and feels magic. Getting it to work everywhere, at typing latency, without eating keystrokes or leaking into your bank login, is where the actual product lives. ## Product outcome A menu bar app that watches keystrokes via the Accessibility API, asks a local model for the next few words, and paints an inline ghost suggestion you accept with Tab. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - macOS with Xcode and a Swift toolchain - Accessibility and Input Monitoring permissions granted manually - a small local model via llama.cpp or MLX, or a personal n-gram corpus built from your own text - Apple Silicon strongly preferred for latency ## Explicit non-goals for v1 - Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals - Latency polish: suggestions that arrive after you already typed the words are worse than nothing - Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work - Personalization that actually improves over months of your writing rather than a one-off corpus dump - Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a macOS menu bar app called GhostType that shows inline text predictions as I type. Stack, no alternatives: Swift 5.9+, SwiftUI for the menu bar UI, AppKit for the overlay window, one Xcode project, no external services. Core loop: 1. Register a CGEventTap for keyDown events so we can observe typing and intercept Tab and Escape. 2. Use the Accessibility API (AXUIElement) to read the focused element's AXValue and AXSelectedTextRange, and AXBoundsForRange to get the caret rect in screen coordinates. 3. Take the last 200 characters before the caret as context and request a completion of up to 6 words. 4. Render the suggestion as dim gray ghost text in a borderless, non-activating NSPanel positioned at the caret rect. Never modify the target app's text until accepted. 5. Tab accepts: insert the suggestion by posting synthetic key events. Escape dismisses. Any other keystroke re-predicts after a 120ms debounce. Prediction engine: ship two interchangeable providers behind a protocol. - Provider A, default: an on-device n-gram model built from a folder of my own .txt and .md files at first launch, stored in a SQLite file in Application Support. - Provider B: a local llama.cpp or MLX server at a URL from .env, called with a strict token limit and a 250ms timeout that is cancelled on the next keystroke. Safety, non-negotiable: - Never predict when secure input mode is active, when the focused element role is AXSecureTextField, or when the frontmost app bundle id is in a user-editable deny list. - No network calls except to the local provider URL. No telemetry, no accounts, no crash reporting. In scope: native AppKit text fields and text views, a menu bar toggle, a deny list editor, a corpus folder picker. Out of scope: Electron and browser text fields, terminals, iOS, sync, cloud models, multi-language tuning. Detect unsupported contexts and stay silent rather than guessing. Deliverables: the Xcode project, a README with the exact Accessibility and Input Monitoring permissions to grant and where to click, .env.example, and a plain description of what fails in Chrome and VS Code and why. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Cotypist capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Cotypist indie build ## Goal Build the smallest trustworthy replacement for the core Cotypist workflow for one developer or a tiny team. ## Scope A menu bar app that watches keystrokes via the Accessibility API, asks a local model for the next few words, and paints an inline ghost suggestion you accept with Tab. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals - Latency polish: suggestions that arrive after you already typed the words are worse than nothing - Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work - Personalization that actually improves over months of your writing rather than a one-off corpus dump - Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates If those capabilities are essential, use Cotypist instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a macOS menu bar app called GhostType that shows inline text predictions as I type. Stack, no alternatives: Swift 5.9+, SwiftUI for the menu bar UI, AppKit for the overlay window, one Xcode project, no external services. Core loop: 1. Register a CGEventTap for keyDown events so we can observe typing and intercept Tab and Escape. 2. Use the Accessibility API (AXUIElement) to read the focused element's AXValue and AXSelectedTextRange, and AXBoundsForRange to get the caret rect in screen coordinates. 3. Take the last 200 characters before the caret as context and request a completion of up to 6 words. 4. Render the suggestion as dim gray ghost text in a borderless, non-activating NSPanel positioned at the caret rect. Never modify the target app's text until accepted. 5. Tab accepts: insert the suggestion by posting synthetic key events. Escape dismisses. Any other keystroke re-predicts after a 120ms debounce. Prediction engine: ship two interchangeable providers behind a protocol. - Provider A, default: an on-device n-gram model built from a folder of my own .txt and .md files at first launch, stored in a SQLite file in Application Support. - Provider B: a local llama.cpp or MLX server at a URL from .env, called with a strict token limit and a 250ms timeout that is cancelled on the next keystroke. Safety, non-negotiable: - Never predict when secure input mode is active, when the focused element role is AXSecureTextField, or when the frontmost app bundle id is in a user-editable deny list. - No network calls except to the local provider URL. No telemetry, no accounts, no crash reporting. In scope: native AppKit text fields and text views, a menu bar toggle, a deny list editor, a corpus folder picker. Out of scope: Electron and browser text fields, terminals, iOS, sync, cloud models, multi-language tuning. Detect unsupported contexts and stay silent rather than guessing. Deliverables: the Xcode project, a README with the exact Accessibility and Input Monitoring permissions to grant and where to click, .env.example, and a plain description of what fails in Chrome and VS Code and why. ## Required capabilities - macOS with Xcode and a Swift toolchain - Accessibility and Input Monitoring permissions granted manually - a small local model via llama.cpp or MLX, or a personal n-gram corpus built from your own text - Apple Silicon strongly preferred for latency ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Cotypist product brief ## Problem The idea is simple: watch what you type, predict the rest, show it as ghost text, accept with Tab. The prediction part is genuinely easy now, a small local model or even a personal n-gram table over your own writing gets you most of the way. The hard part is everything around it: reading the current text field and caret position through the macOS Accessibility API, drawing an overlay that tracks a moving cursor, and not breaking in Electron apps, Chrome, terminals, password fields, or anything that reimplements text editing. You can build something that works in native AppKit fields in a couple of long sessions and feels magic. Getting it to work everywhere, at typing latency, without eating keystrokes or leaking into your bank login, is where the actual product lives. ## Product outcome A menu bar app that watches keystrokes via the Accessibility API, asks a local model for the next few words, and paints an inline ghost suggestion you accept with Tab. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - macOS with Xcode and a Swift toolchain - Accessibility and Input Monitoring permissions granted manually - a small local model via llama.cpp or MLX, or a personal n-gram corpus built from your own text - Apple Silicon strongly preferred for latency ## Explicit non-goals for v1 - Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals - Latency polish: suggestions that arrive after you already typed the words are worse than nothing - Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work - Personalization that actually improves over months of your writing rather than a one-off corpus dump - Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a macOS menu bar app called GhostType that shows inline text predictions as I type. Stack, no alternatives: Swift 5.9+, SwiftUI for the menu bar UI, AppKit for the overlay window, one Xcode project, no external services. Core loop: 1. Register a CGEventTap for keyDown events so we can observe typing and intercept Tab and Escape. 2. Use the Accessibility API (AXUIElement) to read the focused element's AXValue and AXSelectedTextRange, and AXBoundsForRange to get the caret rect in screen coordinates. 3. Take the last 200 characters before the caret as context and request a completion of up to 6 words. 4. Render the suggestion as dim gray ghost text in a borderless, non-activating NSPanel positioned at the caret rect. Never modify the target app's text until accepted. 5. Tab accepts: insert the suggestion by posting synthetic key events. Escape dismisses. Any other keystroke re-predicts after a 120ms debounce. Prediction engine: ship two interchangeable providers behind a protocol. - Provider A, default: an on-device n-gram model built from a folder of my own .txt and .md files at first launch, stored in a SQLite file in Application Support. - Provider B: a local llama.cpp or MLX server at a URL from .env, called with a strict token limit and a 250ms timeout that is cancelled on the next keystroke. Safety, non-negotiable: - Never predict when secure input mode is active, when the focused element role is AXSecureTextField, or when the frontmost app bundle id is in a user-editable deny list. - No network calls except to the local provider URL. No telemetry, no accounts, no crash reporting. In scope: native AppKit text fields and text views, a menu bar toggle, a deny list editor, a corpus folder picker. Out of scope: Electron and browser text fields, terminals, iOS, sync, cloud models, multi-language tuning. Detect unsupported contexts and stay silent rather than guessing. Deliverables: the Xcode project, a README with the exact Accessibility and Input Monitoring permissions to grant and where to click, .env.example, and a plain description of what fails in Chrome and VS Code and why. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Cotypist capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Because a text predictor that works in nine apps and glitches in the tenth is a text predictor you turn off. The value here is invisible reliability across an entire OS, which means quietly handling every app that draws its own caret, every accessibility API quirk, and every macOS release that changes the rules. That is maintenance work, not a build, and it is worth a small license fee to hand off.
xCoverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals
xLatency polish: suggestions that arrive after you already typed the words are worse than nothing
xSafety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work
xPersonalization that actually improves over months of your writing rather than a one-off corpus dump
xSigned, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Cotypist ($6/mo) with one prompt"
Vibecode Cotypist
Kinda. The core of Cotypist is buildable in a weekend with the prompt on this page, but there are real gaps: Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals, Latency polish: suggestions that arrive after you already typed the words are worse than nothing. Read the honest list above before committing.
How much does Cotypist cost?
Cotypist costs about $6/month (Plus, checked 2026-08-18), which is $72 per year.
What do I lose by replacing Cotypist?
Honestly: Coverage: your build will work in native text fields and fail or misbehave in Electron apps, browsers, and terminals; Latency polish: suggestions that arrive after you already typed the words are worse than nothing; Safety rails: skipping password fields, secure input mode, and sensitive apps takes deliberate work; Personalization that actually improves over months of your writing rather than a one-off corpus dump; Signed, notarized, auto-updating distribution and the permission onboarding that makes it survive OS updates. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Cotypist?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.