Things I've made

Some shipped, some still on the desk, a few I've put down for now. Each one says which.

Soteria

Sisyphus

Identité

Beta

Indian IDs and bank cards as copyable text, on iPhone, iPad and Mac.

FIG. 01 · iPhone, iPad and Mac app · 2026

Soteria

Building

Evidence before confidence

FIG. 02 · AI Reliability / Evaluation Infrastructure · 2026

Sisyphus

Shipped

A ranked-game chronicle for a Discord squad

FIG. 03 · Community Product / Automation / Data · 2026

MASTER_CONTEXT.MDFOR DARREN

Master Context

Sprout is a fictional, local plant-watering reminder service. It reads configuration and plant records from files, calculates which plants need water, and exposes the result over HTTP.

Architecture Overview

Sprout is a small Python package with a file-backed read path.

sprout/ contains the HTTP server, reminder logic, and data migration.
settings.yaml supplies runtime settings. plants.json supplies sample plant records.
tests/ contains reminder and migration tests.

CHECKPOINT-CLEO.MDMON 5 OCT

Commit b26f83cContext

The watering interval setting is being renamed from reminder_days to watering_interval_days to make its purpose explicit.

Changes

settings.json: Replaces reminder_days with watering_interval_days, retaining the value of 7 days and port 8080.

Impact

due_plants now requires watering_interval_days; passing settings that contain only the old key raises a KeyError.

CATCHUP-AVA.MDFRI 9 OCT

While You Were Gone

You need to upgrade plant data, settings, dependencies, and client URLs before bringing an existing installation back up.

Critical Changes (Must-Read)

1. Migrate plant data before starting the service. Stop the service and run python -m sprout.migrate once.

2. Update reminder clients to GET /api/reminders.

3. Move settings to YAML and install PyYAML.

4. Rename the watering interval key.

5. Expect reminders on the scheduled day.

Checkpoint

Shipped

Project memory that stays with the code

FIG. 04 · Developer Tool / AI / Automation · 2026

No.ed

Experiment

From scattered interests to a visible learning path

FIG. 05 · Product / AI / Experiment · 2025