Disclosure: I am the founder of deariary. I built a system that connects to your existing tools (Google Calendar, Slack, GitHub, Todoist, Discord, Steam, Bluesky) and uses an LLM to compose a diary entry overnight. You wake up and yesterday is already written. I expected to learn about API integrations and prompt engineering. Instead I learned something about journaling, about memory, and about what LLMs actually are. Three things I did not anticipate.
- The point of journaling is not writing. It is re-reading. I assumed that building an automatic diary meant removing the core of the journaling experience. If you do not write, are you even journaling? After months of using it, I think the answer is yes, and that the traditional model has the emphasis wrong. Journaling looks like one activity. It is actually three separate steps: recall what happened, write it down, reflect on what it means. Most journaling advice focuses on the writing and the reflection. Use prompts. Write shorter entries. Try gratitude lists. Nobody talks about the first step, and the first step is where everything breaks. What did you do yesterday at 2 PM? Most people cannot answer with confidence, even though the day ended only hours ago. By the time you sit down to journal at night, your day is already a highlight reel. Your brain kept the emotional spikes and discarded the rest. The meeting that ran long, the errand between calls, the quiet hour after lunch: gone. And the recall itself is miserable work. At 10 PM, after a full day of decisions, you are asking your depleted brain to reconstruct eight hours of fragmented experience. This is why journaling habits die. Not because people lack discipline for writing, but because remembering your own day is an unreasonable demand at the end of it. When I removed both recall and writing from the equation, what remained was reflection, and it turned out that reflection was always the part that mattered. Opening an entry weeks later and thinking: “I forgot about that conversation.” “Three meetings before noon, no wonder I was short with everyone.” “I complained about the same thing three Tuesdays in a row.” That is the self-awareness people are chasing when they start a journal. It does not require you to hold the pen.
- The days you would never write about are the most valuable entries. This one surprised me the most. When I journaled by hand, I wrote about days that felt noteworthy. A trip, a milestone, a bad day worth processing. Ordinary days got skipped because there was “nothing to write about.” An automatic diary does not skip. It generates an entry for every day, including the ones where “nothing happened.” And those turned out to be the entries I valued most when re-reading months later. A quiet Thursday with a morning standup, some code review, lunch alone, an afternoon of focused work. I would never have written about it. But reading it four months later, I recognized a version of my life that no longer exists. The team has since reorganized. The project shipped. The cafe where I ate lunch closed. That unremarkable Thursday is now a window into a specific period of my life, and I would have lost it entirely. Your busiest days vanish first from memory: every task gets a slice of your attention, but nothing gets enough to stick. But those same days leave the richest data trail. Every meeting left a calendar event, every task left a checkbox, every message left a timestamp. The relationship is inverted: the less you remember, the more raw material exists. But it is the quiet days that hit hardest on re-reading. Not because anything dramatic happened, but because they captured the texture of ordinary life, the texture that your brain categorizes as “unremarkable” and discards, and that no manual journal would ever preserve.
- LLMs are not intelligence. They are translators. That is exactly what this needed. We talk about LLMs in terms of reasoning, intelligence, understanding. Maybe they will get there eventually. But what they do right now, reliably and consistently, is translate between representations. Summarize this document. Rewrite this in a different tone. Convert this structured data into prose. At the core, the thing you can count on is probabilistic translation and summarization. For diary generation, this is not a limitation. It is a perfect fit. The task is literally: take structured data from multiple APIs (calendar events, task completions, chat messages, git commits) and translate it into natural language prose that reads like a diary entry. Not reasoning. Not planning. Translation from machine-readable formats into human-readable narrative. When I tried to push beyond translation, it failed. Early versions attempted to infer emotions from activity patterns: “you must have been frustrated after three back-to-back meetings.” Users hated it. The model was being asked to interpret rather than convert, and it got it wrong in ways that felt invasive. Maybe future models will handle this well. Current ones do not, at least not reliably enough for something as personal as a diary. The version that works treats the LLM as a strict translator. Data in, prose out. It describes what happened. It does not tell you how you felt. The composition is non-trivial (making API events read like a diary requires narrative structure, transitions, proportional weight), but it is still fundamentally a translation task. And translation is what LLMs do best today. I think there is a lesson here for building AI products right now. Instead of pushing LLMs toward capabilities they are still developing, find problems where their current strengths, translation and summarization, are exactly what you need. There are more of those problems than people realize. Honest limitations It tells you what happened, not what it meant. Emotional nuance comes from you. The AI voice is not your voice. People who journal for the craft of writing will not find what they want here. Conversational data makes a huge difference. A calendar-only diary reads like a schedule. Add Slack or Discord and the entry suddenly has texture, because conversations carry context that structured data does not. The product is live at deariary.com (free tier: one integration). Happy to discuss the translation framing, LLM composition challenges, or how you think about matching AI capabilities to product problems. submitted by /u/LeoCraft6
Originally posted by u/LeoCraft6 on r/ArtificialInteligence
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