Dlog – Journaling and AI coach that learns what drives wellbeing (Mac)
Hi HN! I’m Johan. I built Dlog, a journaling app with an AI coach that tracks how your personality, daily experiences, and well-being connect over time. It’s based on my PhD research in entrepreneurial well-being. 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)…
In plain words
Dlog is a journaling app for Mac that uses an AI coach to help users understand what affects their well-being. Users write journal entries and set goals while the app analyzes sentiment and narrative patterns to build a personal model of their psychology. A built-in statistical model identifies which factors most influence their well-being week to week, then offers specific guidance based on those patterns. All data is stored locally on the user's device with no account required.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! I’m Johan. I built Dlog, a journaling app with an AI coach that tracks how your personality, daily experiences, and well-being connect over time. It’s based on my PhD research in entrepreneurial well-being. 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…
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