AuroraLedger is a solo desktop toolkit that tracks effort on everyday web2 chores while keeping an eye on occasional web3 experiments. It works like a personal journal for side projects, combining payment-ledger style entries with crypto-inspired observability for hobby deployments.
I needed a single place to note what I do after work — sometimes pushing updates to a web2 service, sometimes minting a small NFT idea. AuroraLedger keeps those moments together, so every byte of progress shows up on the same timeline.
- Dual-domain entries: Each log item flags whether it represents a traditional web2 task (deploying a recording server, paying for a plugin) or a web3 experiment (minting a token, inspecting a contract log).
- Daybook feed: A lightweight CLI view that groups entries by day and highlights finishing tasks alongside learning notes.
- Micro-insights: Summary helpers that count the last seven days of work, split by domain, and surface how much bandwidth is spent chasing downloads vs. chasing chains.
auroraledger.corehouses theEntrymodel plus builders for synthetic entries in a dev journal.auroraledger.insightscontains functions that compute daily tallies and simple ratios for the daybook feed.run.pywires everything together; it simulates a quick batch of entries and prints a human-friendly recap.auroraledger.cliis the small argument-based wrapper used to surface domain-limited views when I just need one lane of work.auroraledger.fixtureskeeps the sample payload definitions so the CLI always shares the same diary voice.
- Hook up a file-based store and allow the CLI to append real entries instead of using the built-in sample list.
- Add a thin web frontend that visualizes the ratio of time spent on web2 vs web3 experiments.
- Archive daily snapshots automatically so the log can be reviewed week by week.
- Keep the CLI helpers in sync with the scripts under
scripts/so the local journal can remain cohesive even as more entry sources show up.