Timestripe Alternative: Free, Private, Forecasting

Looking for a free Timestripe alternative? See how StrataGist matches Horizons with local-first privacy, a forecasting engine, and ADHD-friendly design.

You loved the idea of Timestripe Horizons. Life, Year, Quarter, Month, Week, Day, all stacked in one place so today's tiny task is visibly chained to the life you actually want. It is a beautiful mental model. Then the pricing page loaded, your goals got stranded behind a subscription wall, and you started wondering where exactly your most personal data was living. If you searched for a Timestripe alternative free of charge and free of the cloud lock-in, this page is for you.

This is an honest comparison. Timestripe Horizons is a genuinely good product and its horizon metaphor is the right idea. StrataGist takes the same six-stratum vision and adds three things Horizons does not give you: it is free, it is local-first by default, and it ships a real forecasting engine that tells you whether you are actually on pace to hit your goals. Below is the substance, including the actual math.

Why the horizon model is correct, and where it usually breaks

The reason a zoomable life-to-day timeline works is not aesthetic. It is cognitive. Behavior tends to be captured by the immediate "now" instead of being guided by internally represented future events, a pattern most pronounced in ADHD but present in everyone under load (Barkley, 1997). People with ADHD also discount delayed rewards more steeply and reproduce time intervals less accurately (Barkley et al., 2001; Zheng et al., 2022). A horizon view fights this time-blindness by keeping the distant goal visible next to the immediate task.

So far Timestripe and StrataGist agree. The horizons are good. The trouble starts elsewhere.

Interrupted or uncompleted tasks are remembered substantially better than completed ones, which is why open loops keep occupying the mind until they are externalized (Zeigarnik, 1927).

The first failure is that most planners make you the librarian. You capture a thought, then you must immediately file it: which horizon, which list, which project. That up-front sorting is exactly the executive-function tax that overloaded and ADHD brains cannot reliably pay. Writing things down to offload memory is one of the most reliable cognitive strategies we have (Risko and Gilbert, 2016), but only if capture is frictionless. The moment capture demands categorization, the loop breaks.

The second failure is that the timeline tells you where things sit but not whether you are winning. You can see a goal due in twelve weeks. You cannot see, at a glance, whether your last two months of actual output will get you there. That gap between the plan and the trajectory is where goals quietly die.

The third failure is privacy and price. Your goals, your moods, your finances, your relationships: that is the most intimate dataset you own, and in most tools it lives on someone else's server behind a recurring charge.

What StrataGist keeps, and what it changes

StrataGist is built on one idea taken seriously: conceptual integrity. Instead of eight bolted-on features, the entire app is one primitive and one loop.

The primitive is the gist. Everything you capture, a fleeting thought, a task, a project, a paper, a person, a transaction, is the same node in one labeled graph. There is no second noun to learn. The horizons (Life, Year, Quarter, Month, Week, Day) are not separate apps stitched together; they are time metadata over that single graph, so a daily task and a life goal are literally the same kind of object at different zoom levels.

The loop is Capture -> Surface -> Do -> Review. This is the deliberate fix for the librarian problem. Classic GTD runs Capture, Clarify, Organize, Reflect, Engage, and that "Organize" step is the up-front filing that breaks under executive load. StrataGist removes it and replaces it with Surface: the system brings the right thing to you instead of demanding you file it first. Capture is a single keystroke. Sorting happens later, automatically, or never.

Auto-scheduling is part of this. When you capture a task, it drops onto today's timeline at the next open slot. That is not a gimmick; it is a soft commitment device that works with present bias rather than against it. Present bias is real and robust, strongest for effort and consumption (and notably weak for money), which is why good defaults and gentle commitment beat punitive stakes (Steel, 2007).

One vocabulary, no feature sprawl

A small but load-bearing difference: StrataGist enforces one concept, one word. A filter, a grouping, a query, and a view are all the same thing, called a lens. A lens is a precise seven-operator algebra (scope, select, project, group, measure, encode, order) over your graph. "todo" is not a separate kind of item; it is just a lens that means "work that is not yet scheduled." This is the cure for the eight-separate-apps disease. New capability shows up as a new view, never as a new screen you have to learn.

The thing Timestripe does not have: a forecasting engine

This is the real reason to switch. StrataGist includes Compass, a forecast engine, and Ephemeris, a backtest engine that replays your history. Together they answer the one question a static timeline cannot: am I on pace?

Here is the actual model, not marketing. First, how much you need to do per week to finish on time:

paceNeeded = remaining / weeksLeft
remaining  = max(0, total - done)
weeksLeft  = max(0.1, (dueMs - nowMs) / WEEK_MS)   // WEEK_MS = 604800000

Then your real, observed pace. Crucially this is not a flat average. It is an exponentially recency-weighted mean over a trailing 8-week window, so a recent stall actually shows up instead of being hidden by a strong start months ago:

muRecent   = Sigma_i ( DECAY^weeksBack_i * counts[i] ) / Sigma_i ( DECAY^weeksBack_i )
DECAY      = 0.7,  weeksBack = 0 for the most recent week
paceActual = (done > 0) ? muRecent : null

Honesty matters here, so the engine also measures how choppy you are. It computes the sample standard deviation of your weekly output and turns it into a single steadiness number:

variance      = Sigma_i ( (counts[i] - muFlat)^2 ) / max(1, LOOKBACK_WEEKS - 1)
sigma         = sqrt(variance)
paceVariation = (muRecent > 0) ? min(2, sigma / muRecent) : null

That volatility is then made visible as a date range, not a single fake-precise finish date. A steady history gives a tight window; a chaotic one gives a wide one, which is the truth:

expectedWeeks = remaining / paceActual
projectedDate = nowMs + expectedWeeks * WEEK_MS

fastPace   = paceActual + sigma
slowPace   = max(paceActual * 0.25, paceActual - sigma)
optimistic = nowMs + (remaining / fastPace) * WEEK_MS
late       = nowMs + (remaining / slowPace) * WEEK_MS

And finally an actual probability of hitting the target, computed with a normal approximation rather than a vibe. The weeks-to-finish is modeled as a Gaussian, with the spread derived by error propagation from your pace volatility:

meanW          = remaining / paceActual
sdW            = (remaining * sigma) / paceActual^2
hitProbability = clamp( normalCdf( (weeksLeft - meanW) / sdW ), 0, 1 )

This is "Monte-Carlo-lite": deterministic and closed-form, so the same history always yields the same forecast. No random sampling, no black box. The standard-normal CDF uses the Abramowitz-Stegun polynomial approximation.

The engine refuses to overclaim. A forecast ships with a confidence score that drops when you have thin history, choppy weeks, or no real target date to aim at:

confidence    = round100( historyFactor * steadiness * targetFactor )
historyFactor = min(1, doneCount / 6)
steadiness    = (paceVariation == null) ? 0.5 : max(0, 1 - paceVariation / 1.5)
targetFactor  = (weeksLeft != null) ? 1 : 0.6

This is the difference between a planner and a forecaster. Timestripe shows you the road. StrataGist tells you your estimated arrival time, the uncertainty band around it, and how much to trust the estimate. That directly counters the planning fallacy, our well-documented tendency to underestimate our own completion times because we plan from optimism instead of from past data (Buehler et al., 1994). The forecast is built from your actual past data, on purpose.

It also catches you lying to yourself

A separate detector compares what you say matters against where your effort actually goes, in the economists' revealed-preference sense:

gap       = statedShare - revealedShare
kind      = |gap| >= 0.12 ? (gap > 0 ? neglected : over-invested) : aligned

If you rate "Health" as a top priority but it is getting almost none of your completed effort, StrataGist surfaces that gently. Not as an alarm, as a quiet nudge. Which brings us to the last difference.

Free, private, and designed not to manipulate you

StrataGist is free and local-first. Your gists, moods, and forecasts live on your device by default. There is no engagement-optimized feed because the product explicitly treats time-in-app as a cost, not a goal, and optimizes for your stated goals instead. There is even a first-class "do-less" restraint layer that caps re-planning and refuses reassurance loops, which makes the assistant safe for people prone to over-checking.

The personalization is honest too. Instead of a diagnosis dropdown, you set five behavior-anchored sliders (overwhelm, need-a-nudge, lose-track-of-time, need-predictability, over-checking) that tune defaults. And when the app suggests what to do next, it is a transparent weighted score across seven signals (urgency, importance, timing, fit, unblock, waiting, affinity), with importance shown prominently on every row to counter the well-known pull toward urgent-but-trivial work. Specific, hard goals beat vague ones (Locke and Latham, 2002), and if-then implementation intentions reliably improve follow-through, even in ADHD (Gollwitzer, 1999; Gawrilow et al., 2011). StrataGist bakes those findings in rather than leaving them as advice.

Timestripe vs StrataGist, the honest summary

  • The horizon model: both have it. Timestripe pioneered the look; StrataGist builds it from one primitive so it never fragments into separate apps.
  • Capture friction: StrataGist removes up-front filing (Surface, not Organize) and auto-schedules captures.
  • Forecasting: only StrataGist tells you pace needed vs pace actual, a probability of hitting the date, and a confidence-scored finish-date range.
  • Price: StrataGist is free.
  • Privacy: StrataGist is local-first by default; your most personal dataset stays with you.
  • Incentives: StrataGist treats time-in-app as a cost and includes a do-less restraint layer.

If you came looking for a free Timestripe alternative because the price, the privacy, or the missing "am I on track" answer pushed you out, this is the swap. You keep the horizons you liked and gain a forecast you can trust.

Try it free, no signup

You can open StrataGist and start capturing on a real timeline in seconds, guest-first, nothing to install and no account required. See your own pace forecast within a week of real data.

Start free at /dock.

References

  1. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65-94. link
  2. Barkley, R. A. (1997). Attention-deficit/hyperactivity disorder, self-regulation, and time: Toward a more comprehensive theory. Journal of Developmental and Behavioral Pediatrics, 18(4), 271-279. link
  3. Barkley, R. A., Edwards, G., Laneri, M., Fletcher, K., & Metevia, L. (2001). Executive functioning, temporal discounting, and sense of time in adolescents with ADHD and ODD. Journal of Abnormal Child Psychology, 29(6), 541-556. link
  4. Zheng, Q., Wang, X., Chiu, K. Y., & Shum, K. K. (2022). Time perception deficits in children and adolescents with ADHD: A meta-analysis. Journal of Attention Disorders, 26(2), 267-281. link
  5. Zeigarnik, B. (1927). On the retention of completed and uncompleted actions. Psychologische Forschung, 9, 1-85. link
  6. Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676-688. link30098-5)
  7. Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65-94. link
  8. Buehler, R., Griffin, D., & Ross, M. (1994). Exploring the "planning fallacy": Why people underestimate their task completion times. Journal of Personality and Social Psychology, 67(3), 366-381. link
  9. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705-717. link
  10. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493-503. link
  11. Gawrilow, C., Gollwitzer, P. M., & Oettingen, G. (2011). If-then plans benefit delay of gratification performance in children with and without ADHD. Cognitive Therapy and Research, 35(5), 442-455. link