[HN Gopher] Show HN: Dlog - Journaling and AI coach that learns ...
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Show HN: Dlog - Journaling and AI coach that learns what drives
well-being (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) 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 johan@dlog.pro 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.
Author : dr-j
Score : 8 points
Date : 2025-10-27 17:14 UTC (5 hours ago)
(HTM) web link (dlog.pro)
(TXT) w3m dump (dlog.pro)
| kstrauser wrote:
| First comment: I freaking love your privacy policy. Seriously.
| Great job!
|
| Second: I haven't downloaded it yet because my itsatrap.gif
| warning bells are going off about pricing. On a scale of free to
| kidney, what are we looking at here? Is this going to be priced
| for end users, or will it look closer to an enterprisey kind of
| plan?
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