The unexamined life
is not worth living
Track your time. See your patterns. Level up your life.
Track your time. See your patterns. Level up your life.
Grow helps you track time across different domains to gain powerful insights into how you spend your most valuable resource
Build projects in four key life domains and track time in each area
Create specific and actionable tasks. Focus on measurable progress.
Read chapter on wave functions
Implement inheritance example
Track time across domains to optimize focus and gain powerful insights.
patterns, rhythms, and insights that help you work better — all generated automatically as you track
peak hour
10:00
active hours
5 of 24
define what matters and watch your streaks build over time
| Goal | Periods | Hit Rate |
|---|---|---|
Study 15h/week Study | 75% avg 85% | |
Complete 5 tasks/week Research Paper, Side Project | 67% avg 90% | |
Exercise 30min/day Habits | 83% avg 95% |
the more you track, the sharper your picture becomes
If you work X hours today, what's the expected average daily output over the next N days?
| Today | 1d | 2d avg | 7d avg | 14d avg | 30d avg | n |
|---|---|---|---|---|---|---|
| 0–2h | 2.6h | 2.8h | 2.9h | 3.2h | 3.1h | 116 |
| 2–4h | 3h | 3.6h | 3.5h | 3.3h | 3.4h | 50 |
| 4–6h | 3.8h | 3.5h | 3.5h | 3.4h | 3.5h | 132 |
| 6–8h | 4.9h | 4.6h | 4.4h | 4.2h | 4h | 63 |
| 8–10h | 3.9h | 4.9h | 5.2h | 5.3h | — | 3 |
Values above your overall average (3.5h/day) are highlighted. Each cell shows expected average daily hours over that horizon. n = days sampled.
Hours today vs hours tomorrow
r = +0.31
Each dot is one day. Dashed line = y=x (same hours both days). If fatigue were real, high-today dots would cluster below the line.
Mixed signal. The next-day column isn't strictly increasing — there may be some tapering at the extremes. Day-over-day correlation: +0.31. Overall, momentum still outweighs fatigue.
Your current productivity regime and what to expect next
2-5 hours/day (7d avg)
Persistence
94%
Expected tomorrow
3.3h
Overall average
3.5h
Low-day risk
Moderate (25%)
Time in each regime
Probability of moving between regimes day-to-day
| From / To | High | Active | Low | Off |
|---|---|---|---|---|
| High | 90% | 11% | 0% | 0% |
| Active(you) | 2% | 94% | 4% | 0% |
| Low | 0% | 31% | 57% | 11% |
| Off | 0% | 0% | 31% | 69% |
Each row sums to 100%. The diagonal shows persistence — how sticky each regime is. Your current regime row is highlighted.
How closely your patterns match the average user profile
Day-of-week match
72%
Hour-of-day match
69%
Variance ratio
0.88x
Days analyzed
249
Composite of how your day-of-week rhythm, hour-of-day schedule, and day-to-day consistency compare to the average across 40+ users. A lower score doesn't mean worse — it means your patterns are more unique.
see where you stand — everyone's on the board
| # | User | Streak | Hours / week |
|---|---|---|---|
AKAlex K. | 47 days | 32.5h | |
SRSam R. | 31 days | 28.1h | |
built for myself, shared with you. your data stays private — always.
Start to GrowJMJordan M. |
| 24 days |
| 25.8h |
| 4 | TPTaylor P. | 19 days | 22.3h |
| 5 | CLCasey L. | 12 days | 18.7h |
Track your time. See your patterns. Level up your life.
Grow helps you track time across different domains to gain powerful insights into how you spend your most valuable resource
Build projects in four key life domains and track time in each area
Create specific and actionable tasks. Focus on measurable progress.
Read chapter on wave functions
Implement inheritance example
Track time across domains to optimize focus and gain powerful insights.
patterns, rhythms, and insights that help you work better — all generated automatically as you track
peak hour
10:00
active hours
5 of 24
define what matters and watch your streaks build over time
| Goal | Periods | Hit Rate |
|---|---|---|
Study 15h/week Study | 75% avg 85% | |
Complete 5 tasks/week Research Paper, Side Project | 67% avg 90% | |
Exercise 30min/day Habits | 83% avg 95% |
the more you track, the sharper your picture becomes
If you work X hours today, what's the expected average daily output over the next N days?
| Today | 1d | 2d avg | 7d avg | 14d avg | 30d avg | n |
|---|---|---|---|---|---|---|
| 0–2h | 2.6h | 2.8h | 2.9h | 3.2h | 3.1h | 116 |
| 2–4h | 3h | 3.6h | 3.5h | 3.3h | 3.4h | 50 |
| 4–6h | 3.8h | 3.5h | 3.5h | 3.4h | 3.5h | 132 |
| 6–8h | 4.9h | 4.6h | 4.4h | 4.2h | 4h | 63 |
| 8–10h | 3.9h | 4.9h | 5.2h | 5.3h | — | 3 |
Values above your overall average (3.5h/day) are highlighted. Each cell shows expected average daily hours over that horizon. n = days sampled.
Hours today vs hours tomorrow
r = +0.31
Each dot is one day. Dashed line = y=x (same hours both days). If fatigue were real, high-today dots would cluster below the line.
Mixed signal. The next-day column isn't strictly increasing — there may be some tapering at the extremes. Day-over-day correlation: +0.31. Overall, momentum still outweighs fatigue.
Your current productivity regime and what to expect next
2-5 hours/day (7d avg)
Persistence
94%
Expected tomorrow
3.3h
Overall average
3.5h
Low-day risk
Moderate (25%)
Time in each regime
Probability of moving between regimes day-to-day
| From / To | High | Active | Low | Off |
|---|---|---|---|---|
| High | 90% | 11% | 0% | 0% |
| Active(you) | 2% | 94% | 4% | 0% |
| Low | 0% | 31% | 57% | 11% |
| Off | 0% | 0% | 31% | 69% |
Each row sums to 100%. The diagonal shows persistence — how sticky each regime is. Your current regime row is highlighted.
How closely your patterns match the average user profile
Day-of-week match
72%
Hour-of-day match
69%
Variance ratio
0.88x
Days analyzed
249
Composite of how your day-of-week rhythm, hour-of-day schedule, and day-to-day consistency compare to the average across 40+ users. A lower score doesn't mean worse — it means your patterns are more unique.
see where you stand — everyone's on the board
| # | User | Streak | Hours / week |
|---|---|---|---|
AKAlex K. | 47 days | 32.5h | |
SRSam R. | 31 days | 28.1h | |
built for myself, shared with you. your data stays private — always.
Start to GrowJMJordan M. |
| 24 days |
| 25.8h |
| 4 | TPTaylor P. | 19 days | 22.3h |
| 5 | CLCasey L. | 12 days | 18.7h |