Core Architecture

My calendar app is a static, multi-page application with a fixed release cycle (annual deployment). It features a hard-coded UI with immutable data — the dates and layout cannot be modified without manual intervention. It’s essentially a read-only interface optimized for offline-first functionality.

Key Features

Data Structure - 12-month dataset rendered as a grid-based layout with pre-populated date objects

State Management - Completely stateless; no persistent storage or sync capabilities

User Interaction - Supports reads and writes; compatible with any pen/pencil (similar to a POST request, but non-reversible)

Rendering - Raster-based output; responsive design (fits on my wall)

Accessibility - Physical accessibility for all members of my cohort

Portability - Take a picture once a month, refer to that on your phone

Performance - O(1) lookup time for any date; zero latency, zero network overhead

Technical Advantages

Zero dependencies, requires only wall space and a pen.

No API to support / learn, no loading times / lag, no version conflicts.

Guaranteed uptime. It’s also compliant with most privacy standards, though it does require physical access.

Limitations

The calendar has no real-time sync, no cross-device functionality, and no undo. Annotations are permanent with an ink pen. It cannot adjust for time zones or integrate with other calendar systems. No ICS compatible export of appointment data. And unfortunately, the application will need to be replaced after just one year.

If you’re interested in implementing a similar app, I can point you in the right direction…

  • Fmstrat@lemmy.world
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    1 day ago

    no persistent storage

    Doesn’t it have one-time copy-on-write persistent storage?

    Performance - O(1) lookup time for any date

    Going to need to see the math here. It’s March, explain the O(1) transition to view October 12th.

    • Ephera@lemmy.ml
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      1 day ago

      Yeah, this should be equivalent to interpolation search, which has an average performance of O(log(log(n))).

      It helps that the months are separately indexed, so instead of a search on 365 input elements, you can do two searches with much lower input size, i.e. 12 and 31.

      But yeah, you’re still in the larger O(log(log(n))) category with that.