dr-j
Joined 1 year ago
Joined 1 year ago
Responding to concerns regarding "long and rambling".
J.
- New ANONYMIZER in Coach; toggle on to anonymize names in entries sent to coach AI API.
- Fixed Bug in Four Rings where comparisons were miscalculated when swapped.
- Fixed Dataset load failed … Dlog.(unknown context)…LoadError. The error had occurred when no entries have been scored.
- Fixed Bug where 1 million tokens were not being applied upon purchase.
- Other general improvements.
Also, dlog now has a bluesky @dlog.bsky.social where I'll be posting all updates and news. Also email me [email protected] to get the perpetual license for ShowHN users.
Thanks for all the support and energy in the comments!!
Why that matters? multiple indicators per construct (the 61 baseline items) reduce overfitting and let the model recover latent traits (Personality, Character, Resources, Well-Being) rather than relying on noisy single measures. AND, its not just the SEM, its mixed methods: dlog model's coefficients are linked to narrative evidence from your entries, giving both quantitative explanation and concrete journal quotes you can inspect.
I’m planning on adding a technical appendix and a white paper when I launch the Dlog Research edition for universities at some point next year. Meanwhile you can read the model summary here:: https://dlog.pro/#dlogModel and download an example mixed-methods report you can make in the Dlog Labs https://updates.dlog.pro/AnonDoe.pdf.
Thanks again for the interest; if you do register with Dlog email me at [email protected] and I'll give you a free perpetual license and 1 million tokens to test out Dlog.
Dlog takes the opposite approach: • Agency first: The Coach proposes; you decide. No phone trees, no task assignments, no social mechanics. You can ignore, not use or set a low-cadence to the guidance. • Explainability over prescriptions: Suggestions come with a brief “why” based on your SEM reports (which factors moved and by how much) plus charts so you can sanity-check against lived experience. • Local-first privacy: Journals live on-device (EventKit). Scoring + SEM run locally. By default no raw text leaves the device unless you use the coach, this is optional, but there is a bit of a leap of faith here with OpenAI; until I enable on device LLMs in due course. • No hidden incentives: No affiliate nudges, upsells, or growth hacks. It doesn’t decide what you wear/eat or route calls to you; it surfaces patterns (e.g., “energy dips after external calls”) so you can choose actions that fit your constraints.
If that story raised a specific worry: loss of autonomy, privacy creep, or community spam, then does the above address it? I’m especially interested in whether the “why” behind recommendations is clear enough or needs to be tighter.
Thanks for pushing on clarity—fixes are in motion.
Thanks for the nudge.
https://dlog.pro/
Responding to concerns regarding "long and rambling".
J.