Ship
Changes requested
Windows resize from all edges and snap to half or full screen when dragged to a screen edge. A boot splash, a first-run hint toast and the SPICE indicator
Nacho Terminal and TORTILLA PAINT
Start menu, desktop icons and Queso Calc 🌮
I added my first app window: The nacho recipe
The striped desktop background, and a top panel with a live clock.
Ship
The terminal can search with s, switch between Google, DuckDuckGo and
Yahoo, and jump straight to Google, YouTube or GitHub with commands: g, yt, gh. I also added the readme
I added an info command that provides information about planets and an ls command that compares them.
I’ve added the other planets too (I can’t upload all 7 planets).
Added an ASCII planet
Static terminal layout and input loop
history on the arrow keys, and Tab completion. Commands register into a
table so later work can add them without touching the dispatcher.
Ship
Pending review
Moodify is a Spotify bot that rebuilds one playlist for me every two days based on what I actually listened to. It runs entirely on GitHub Actions, so there is no server and it costs nothing. The hard part was not picking songs, it was getting honest feedback: if you ask a model to rate its own playlist it gives itself an 8 out of 10 forever, so instead the bot counts real plays and tunes itself from that. That got complicated fast. Spotify only hands you your last 50 plays, which badly undercounts over a two day cycle, so a second workflow runs every hour just to keep a counter. And my first tuning rule could only ever lower the number of familiar tracks it kept, which sent it into a spiral where the playlist became all strangers and I stopped listening to it. Fixing that, by comparing the carried over tracks against the fresh ones in the same playlist instead of looking at the overall average, is the part I am most proud of. There is a live demo you can try, and the README walks through pointing it at your own Spotify account.
The complete dashboard: a request box that sends free text to the curator, a GitHub token flow that triggers the workflow directly from the panel, and live run tracking.
I added two new panels—Mood History and Bot Learnings (showing extracted listener patterns and confidence levels)—along with a Score Trend chart comparing AI self-assessed scores to real engagement scores from play counts.
I addressed the omissions in the README, translated the entire codebase into English (from Turkish), and added my first release.
I Added the Playlist History panel, where each cycle is listed with the AI’s self-assessed score as a bar and its written analysis.
Added a Manual Update section and Open Playlist link to trigger cycles on demand, alongside a Bot Strategy panel that displays the discovery quota, cumulative play counts from the hourly poll job, carry-over limit, and the bot’s rationale for its last adjustment.
Expanded the stats grid to six cards: current mood, AI score, real score and discovery count are now read from the same state file.
The panel now pulls the bot state from a secret Gist and renders it. The status badge turns green and the cycle counter shows real data for the first time. I also Added the Playlist Archive table, which lists every past cycle with its track count and average plays, plus a link to the Excel history in the repo.
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