Dr Jay

Dr Jay is a private, on-device accountability app for iPhone (iOS 26+) that tracks sleep, water, food, and optional exercise, and surfaces today's Health step count—with praise when you deliver and a sharp roast when you do not.

What it tracks

• Sleep: a healthy range of 6–9 hours. Sleep can be read from HealthKit or entered manually. A manual entry remains authoritative for the day unless the user explicitly replaces it with Health data.
• Water: progress toward a configurable full-day goal. Morning, afternoon, and night check-ins judge whether the complete daily goal has been reached; they do not estimate whether the user is "on pace."
• Food: plain-language meal and snack entries are analyzed on device. An unhealthy entry receives an immediate roast, while the Today screen rolls all analyzed entries into an order-independent daily score: Good (80–100), Bad (60–79), or Ugly (0–59). Individual classifications can be corrected or deleted from History. A correction becomes private local memory: exact future matches use it automatically, while similar foods receive it only as context for a fresh assessment. The same analysis counts explicit caffeine-forward items and obvious sugary treats or sweetened drinks; naturally occurring and incidental sugar is excluded.
• Steps: today's cumulative count is read directly from Health for display and the daily report. It is never copied into Dr Jay's database or backup.
• Exercise: optional free-text entries preserve the user's original words and infer a broad activity category locally. Only an explicit duration such as 30 minutes or 1 hour affects the Movement component; entries without a duration are still saved and shown in History.

Daily report

The overall score is deterministic and uses these base weights:

• Food: 35% — today's fixed food score.
• Sleep: 30% — full credit from 6–9 hours, proportionally less below 6, and reduced above 9.
• Water: 30% — progress toward the complete daily bottle goal, capped at full credit.
• Movement: 5% — uses the higher of Health steps (normalized up to a soft 8,000-step ceiling) or explicitly logged exercise minutes (normalized up to 30 minutes). The two are never added together, preventing double-counting. These are scoring references, not medical targets.

Missing metrics are excluded and the available weights are proportionally normalized, so unavailable steps or exercise never lower the score. Missing sleep or food is shown explicitly and marks the report incomplete. Overall scores use Good (80–100), Bad (60–79), and Ugly (0–59).

The report can be generated on demand from Today. Its score, verdict, and calculation remain fixed; Swift writes the factual verdict and action while the selected on-device model contributes only a validated, non-factual barb. Good receives reluctant clinical approval, Bad roasts the weakest major factor, and Ugly receives the sharper clinical roast. The model is instructed not to repeat the visible score, weights, or metric list. Intensity changes the construction, not merely a few adjectives: Gentle uses indirect clinical irony, Playful uses a direct absurd comparison, and Spicy uses a short accusation with no advice, hedging, or soft landing. Swift fixes the single verified roast target and rejects generated lines that claim a failure in any other metric. A local target- and intensity-aware fallback is used when generation is unavailable or violates that contract.

At 10 p.m., a local notification delivers the latest deterministic report available when it was scheduled. The notification does not depend on the language model running at delivery time.

Brain Dump

Brain Dump is an ephemeral mindfulness conversation with Dr Jay. Closing the pane immediately discards every turn; prompts and replies are not persisted, logged, exported, or included in backups. Deterministic local routing blocks prompt extraction, medical instructions, vulnerable beliefs, immediate-risk content, and unrelated task requests before generation.

Apple Intelligence is the default responder. Settings → Extended Commentary can optionally download and select Gemma 4 E2B for Brain Dump, Daily Report, and Insights. The 2.59 GB LiteRT-LM artifact is downloaded in the background, excluded from backups, and activated only after its exact byte count, file signature, SHA-256 checksum, and LiteRT engine initialization all succeed. Interrupted transfers retain resumable download data. The model can be deleted independently without affecting app history.

The Today screen puts logging actions and the actionable food and exercise cards first, followed by caffeine and sugary-item counter rings, the read-only Steps card, and the manual report action. History contains the detailed daily record, food entries, exercise entries, corrections, and previous check-ins. Current and longest streaks count consecutive days on which both sleep and water goals were completed; food and steps do not affect streaks.

Dr Jay Insights in History computes private 7-day and 30-day views from the existing log. Swift calculates coverage, goal adherence, direction, exposure totals, optional exercise activity, priorities, and cautiously worded patterns. Swift writes the factual interpretation and next action; the selected on-device model contributes only a validated barb. Trends require at least seven logged days in both comparison windows, generated commentary is not stored, and steps remain excluded because historical step totals are never persisted.

Privacy and resilience

• Health access is read-only.
• Roasts and food analysis use Apple's Foundation Models on device; food logs and health data are not sent to a server.
• Brain Dump, Daily Report, and Insights inference stays on device with either Apple Intelligence or the optional Gemma model. The model file and provider preference are separate from deliberately non-persistent generated text.
• Sleep and water roasts use a curated local fallback bank when Apple Intelligence is unavailable. Food remains safely logged as unanalyzed when the model is unavailable and can be classified manually from History.
• App data is stored locally with SwiftData in the shared App Group container.
• JSON export/import in Settings → Data preserves sleep, water, food entries, scores, exercise logs, learned food corrections, and check-in history; streaks are rebuilt from those daily logs after import. The current export schema is version 7; versions 1–6 remain import-compatible, and older manual food corrections are recovered where possible. A backup leaves the app only when the user chooses to share the exported file. Step counts and generated daily report commentary are intentionally excluded from storage and JSON backups.

Platform features

• Seven selectable Pantone 2026-inspired themes, each adapted for light, dark, and increased-contrast appearances. Theme changes apply immediately and are shared with widgets; Tropic Tonalities remains the default.
• SwiftUI + SwiftData for the app and App Group-backed history.
• FoundationModels for on-device food analysis and Dr Jay's generated roast, approval, and manual daily-report commentary, with gentle, playful, and spicy intensity levels.
• LiteRT-LM for optional Gemma 4 E2B extended commentary. Apple Intelligence remains the default because Gemma requires roughly 2.59 GB of storage and substantially more runtime memory.
• HealthKit for read-only sleep import and an ephemeral current-day step count, with opportunistic background step refresh.
• ActivityKit for sleep and water progress on the Dynamic Island and Lock Screen.
• WidgetKit for sleep and water Home Screen and Lock Screen widgets.
• App Intents for logging bottles or sleep and checking current status through Siri.
• Configurable morning, afternoon, and night local notifications, plus an exact-time 10 p.m. report. Today is personalized from the latest available values; a rolling week of generic fallbacks avoids replaying stale metrics if the app receives no refresh.

Setup

1. Install Xcode with the iOS 26+ SDK and install XcodeGen if needed: brew install xcodegen.

2. Generate the Xcode project with your Apple Developer Team ID:

   DEVELOPMENT_TEAM=YOUR_TEAM_ID xcodegen generate
   

   Roastie.xcodeproj is generated from project.yml and intentionally gitignored. Regenerate it after adding or removing source files.

3. Open Roastie.xcodeproj. The default bundle IDs are dev.jeyanth.roastie and dev.jeyanth.roastie.widgets. Change bundleIdPrefix and the matching App Group in project.yml for another developer account, then regenerate the project.

4. Confirm both targets use the same App Group entitlement.

5. Build and run the Roastie scheme on a real device with Apple Intelligence enabled for the complete experience.

Validation

The project includes unit coverage for goal calculation, streaks, check-in window selection, snapshot migration, backup import, food scoring and exposure counter boundaries, learned food-memory matching, daily-report weighting, missing-step handling, Brain Dump safety routing, and Good/Bad/Ugly report boundaries. Compile the app and test bundle without executing tests with:

xcodebuild -project Roastie.xcodeproj -scheme Roastie \
  -configuration Debug -destination 'generic/platform=iOS' build-for-testing

Known constraints

• iOS does not run app code when a local notification is delivered. Its text therefore uses the latest values from the most recent foreground or Health background refresh. Future generic 10 p.m. fallbacks never repeat stale metrics as though they belonged to a new day.
• Background processing is opportunistic and acts as a day-rollover backstop; foreground refresh remains the primary update path. Health step observer delivery is also opportunistic rather than a real-time pedometer feed.
• A free Apple Developer signing profile normally expires after seven days. Export a JSON backup before reinstalling if persistent history matters.
• Default goals are 6–9 hours of sleep and four 750 ml bottles of water; water settings are configurable.

License

Dr Jay's original source code is available under the Apache License 2.0. Copyright and bundled-component attribution is recorded in NOTICE. Third-party components retain their own license terms, including the vendored LiteRT-LM package at Vendor/LiteRTLM/LICENSE.