trainlog/docs/roadmap.md

Roadmap

This file is the canonical product roadmap for Trainlog.

Historical implementation detail belongs in Git history and docs/reviews. The published Trainlog JSON v1 compatibility boundary remains frozen unless a future feature explicitly introduces a new version.

Completed foundation

GATE_0=PASS
GATE_1=PASS
GATE_2=PASS

TRAINLOG_FORMAT_V1=FROZEN
DESKTOP_SCHEMA_V5=PASS
ANDROID_LOCAL_DATABASE_V4=PASS

DIRECT_MTP_TRANSPORT=PASS
BIDIRECTIONAL_SYNC_V1=PASS

VARIABLE_REPETITION_SETS=PASS
MEASURED_MAX_V1=PASS
BODY_ANALYTICS_V1=PASS
EXERCISE_EDIT_V1=PASS
ANDROID_BANNER_PARITY_V1=PASS

DESKTOP_TESTS=22/22 PASS
TUI_NOTCURSES_V1=PASS
NCURSESW_REMOVED_FROM_ACTIVE_TUI=PASS
NOTCURSES_TRUECOLOR_THEME=PASS

The current product baseline includes:

• SQLite persistence and explicit migrations;
• usable Notcurses desktop TUI;
• native Android capture client;
• exercise catalog;
• profile-aware set and continuous activity;
• heterogeneous repetition sets;
• session history and editing;
• body measurements and body history;
• measured-max sessions and measured-max analysis;
• desktop body analytics;
• direct USB/MTP transport;
• bidirectional Android/PC synchronization;
• shared synchronization engine and trainlog-syncd.

EXERCISE_EDIT_V1 is a completed capture correction: Android permits stable-ID renames, protects referenced profiles, and reconciles same-ID display metadata without duplicates. ANDROID_BANNER_PARITY_V1 is a presentation-only completed checkpoint: all Android screens share the Notcurses-derived compact header; it does not reorder the roadmap below.

Product boundary

ANDROID_SESSION_DRAFT_V1 is an implemented P0 capture-reliability correction: one durable active draft, Home resume, explicit discard and atomic completion. Host and device validation and the final tranche review pass. This repair does not introduce planning/templates or reorder the product roadmap below.

The intended split remains:

ANDROID
= fast capture during real use

DESKTOP TUI
= planning
+ catalog management
+ history
+ analytics
+ long-term decision support

Android must not become the main analytics surface unless this boundary is explicitly revised later.

Operational cursor — real data baseline

The immediate priority is not another implementation block.

Trainlog now needs real measurements and real training sessions so later analytics are based on useful data rather than fixtures.

CURRENT_OPERATIONAL_CURSOR=REAL_DATA_BASELINE_V1

Expected baseline:

• first complete real body observation;
• first real training sessions;
• Android used for capture;
• Android -> PC synchronization after training;
• measured max recorded only through explicit max_test sessions;
• no fictitious user data in the canonical databases.

Gate:

REAL_DATA_BASELINE_V1=PASS

Next feature — gym catalog v1

NEXT_FEATURE=GYM_CATALOG_V1

The catalog must be based on the equipment actually available in the user's gym rather than on a generic Internet exercise list.

The initial inventory will be built from a complete photo survey of the gym.

For each useful equipment item, record where applicable:

gym zone
equipment family
machine name
manufacturer/model if identifiable
useful duplicate count
supported exercise(s)
Trainlog recording profile
load semantics
primary muscles
secondary muscles

Important rule:

one photographed machine != one exercise

A single piece of equipment may support multiple exercises. Equipment and exercise identity must remain distinct concepts.

Gate:

GYM_CATALOG_V1=PASS

Exercise metadata v1

After the real gym inventory is normalized, enrich the exercise catalog with structured metadata needed by planning and analytics.

Candidate metadata:

equipment
primary_muscles
secondary_muscles
movement_family
body_region
laterality

The final schema must be designed before implementation. Do not encode these concepts into names or free-form notes as a substitute for a real model.

Gate:

EXERCISE_METADATA_V1=PASS

Session planner v1

The desktop TUI becomes the canonical session-planning surface.

A planned session should allow:

• selecting exercises from the real gym catalog;
• ordering exercises;
• planned sets/repetitions or duration;
• planned rest;
• optional planned load where semantically valid;
• synchronization to Android.

Android should then open a prepared session and require only actual performance entry during training.

Target workflow:

Desktop TUI
-> prepare session
-> sync

Android
-> open planned session
-> enter actual values
-> save
-> sync

Desktop TUI
-> history and analytics

The feature should also make later support possible for:

duplicate previous session
reuse a planned session
session templates

Gates:

SESSION_PLANNER_V1=PASS
ANDROID_PLANNED_SESSION_ENTRY=PASS

Session templates v1

Templates are built from the real exercise catalog and planning model.

Candidate examples:

glutes + arms
back + shoulders
chest + arms
legs
full body
cardio

Templates are conveniences, not rigid training prescriptions.

Gate:

SESSION_TEMPLATES_V1=PASS

Training analytics v1

Implement only after enough real sessions exist to make the output meaningful.

Candidate views:

7 days
30 days
90 days
since baseline

Candidate metrics:

• training frequency;
• sets;
• repetitions;
• duration;
• external load;
• assistance;
• measured-max progression;
• useful volume metrics where semantically valid;
• progression by exercise.

Once exercise metadata exists, aggregate by muscle group or movement family to help evaluate training balance.

Gate:

TRAINING_ANALYTICS_V1=PASS

Body analytics v2

Implement after multiple real body observations exist.

Possible additions:

• baseline comparison;
• previous-observation comparison;
• 7/30/90-day trends;
• moving-average weight trend;
• waist trend;
• anthropometric estimate trend;
• left/right asymmetry trend;
• obvious measurement-outlier warnings.

Permanent semantic rule:

DIRECT_MEASUREMENT != ESTIMATE

Derived body-composition values remain explicitly labelled estimates.

Gate:

BODY_ANALYTICS_V2=PASS

Progression assist v1

Use accumulated history to produce conservative training suggestions.

Examples:

external load:
stable completed work
-> suggest a small load increase

assistance:
stable completed work
-> suggest a small assistance decrease

The system proposes. It does not silently change planned training.

Measured max and estimated performance must remain distinct concepts.

Gate:

PROGRESSION_ASSIST_V1=PASS

Objectives v1

Support explicit user-defined objectives such as:

body weight
waist circumference
plank duration
exercise load
repetitions
measured max
weekly training frequency

Display current value, target, remaining difference, and trend where the data supports it.

Avoid mandatory gamification.

Gate:

OBJECTIVES_V1=PASS

Backup and export v1

SQLite remains canonical.

Provide durable user-controlled export and backup:

SQLite
-> dated backup
-> complete JSON export
-> CSV export where useful
-> period-limited export

Gate:

BACKUP_EXPORT_V1=PASS

Canonical implementation order

REAL_DATA_BASELINE_V1
        |
        v
GYM_CATALOG_V1
        |
        v
EXERCISE_METADATA_V1
        |
        v
SESSION_PLANNER_V1
        |
        v
SESSION_TEMPLATES_V1
        |
        v
TRAINING_ANALYTICS_V1
        |
        v
BODY_ANALYTICS_V2
        |
        v
PROGRESSION_ASSIST_V1
        |
        v
OBJECTIVES_V1
        |
        v
BACKUP_EXPORT_V1

This order is canonical until explicitly revised.

Permanent constraints

Do not regress to:

SQLite file synchronization
mandatory mounted Android filesystem
exercise-name identity heuristics
fake performed sets for continuous activity
fake uniform targets for heterogeneous actual sets
ordinary training promoted to measured max
assistance interpreted as external load
body-composition estimates presented as direct measurements
incompatible changes to Trainlog JSON v1 without a new format version