Edit: here's a video demo so you can see it before downloading: https://www.youtube.com/watch?v=74C4P8I164M - it's unvarnished but I'm told that's how people like it here :)
How Dlog works
- Journal and set goals/projects; Dlog scores entries on-device (sentiment + narrative signals) and updates your personal model.
- A built-in structural equation model (SEM) estimates which factors actually move your well-being week to week.
- The Coach turns those findings into specific guidance (e.g., “protect 90 minutes after client calls; that’s when energy dips for you”).
- No account; your journals live locally (in your calendar). You decide what, if anything, leaves the device.
The problem
- Generic AI coaches give advice without understanding your personality or context.
- Traditional journaling is reflective but doesn’t surface causal patterns.
- Well-being apps rarely account for individual differences or test what works for you over time.
What my research found (plain English)
In my PhD I modeled how Personality, Character, Resources, and Well-Being interact over time. The key is latent relationships: for example, Autonomy can buffer the impact of low Extraversion on social drain, while time/energy constraints mediate whether “good advice” is actionable. These effects are person-specific and evolve—so you need a model that learns you, not averages.
The solution
Dlog pairs on-device journaling analytics with an SEM that updates weekly. You get a running estimate of “what moves the needle for me,” and the Coach translates that into concrete suggestions aligned with your goals and constraints.
Early stories (anonymized from pilot users)
- A founder saw energy dips clustered after external calls; moving deep work to mornings reduced “bad days” and improved weekly mood stability.
- A solo designer’s autonomy scores predicted well-being more than raw hours worked; small boundary changes (client comms windows) helped more than time-tracking tweaks.
Tech & security
- Platform: macOS (Swift/SwiftUI). Data: local storage + EventKit calendar for entries/timestamps.
- Analytics: on-device sentiment + narrative features; SEM computed locally; weekly updates compare to your baseline.
- AI Coach: uses an enterprise LLM API for reasoning on derived features/summaries. By default, raw journal text does not leave the device; you can opt-in per prompt if you want the Coach to read a specific passage.
- Why 61 baseline variables? The SEM needs multiple indicators per construct (Personality, Character, Resources, Well-Being) to estimate stable latent factors without overfitting; weekly check-ins refresh those signals.
What I’ve learned building this
- Users value clarity with depth: concise recommendations paired with focused dashboards, often 5–10 charts, to explain the “why” and trade-offs.
- Cold start matters: a solid baseline makes the first week of insights credibly useful.
- Privacy UX needs to be explicit: users want granular control over what the Coach can read, per request.
I’m looking for feedback on:
- Onboarding (baseline survey and first-week experience)
- Coach guidance clarity and usefulness
- Analytics accuracy vs. your lived experience
- Edge cases, bugs, and performance
Download: https://dlog.pro
If you hit token limits while testing, email me at [email protected]
Background
PhD (Hunter Center for Entrepreneurship, Strathclyde), MBA (Babson), BComm (UCD). I study solo self-employment and well-being, and built Dlog to bring that research into a tool practitioners can use.
Note: The Coach activates after your first scored entry. If you haven’t written one yet, you’ll see a hold state—add a quick journal entry and it unlocks.
Appearance: On a few Macs the initial theme can render darker than intended. If you see this, switch to Light Mode as a temporary workaround; a fix is incoming.
Edit: For general users it's free for 14 days with 10K free tokens; then its 1.99 per month at the moment. However, for HN readers that DM me or email me with the email they register with, I'll give a free perpetual license so there's no monthly fee; and add 1 million tokens.