Tasty Planet cover art

Tasty Planet

A katamari-style growth loop testing light steering and eat-order routing.

Here's the argument for where this game sits - disagree freely.
Match your experience?
EXECUTION4 /10READS0 /10VARIANCE1 /10OPTIMIZATION3 /10

the cheat-test profile

The four axes - the argument

Each axis is rated on its own - how far one cheat would trivialize the game here. They don't trade off; low on all four means the game is an experience, not a contest.

  1. Execution

    cheat: Aimbot - perfect inputs

    4/10

    Steering the goo to eat efficiently under timers

  2. Reads

    cheat: Out-predict an adaptive opponent

    0/10

    Prey and hazards move on simple loops

  3. Variance

    cheat: See what's hidden / map the RNG

    1/10

    Level layouts are fixed once seen

  4. Optimization

    cheat: A perfect solver / coach

    3/10

    Eating order routes optimise growth speed

How to read this

Each axis is named by the cheat that would break the game along it. Execution is the hands. Reads is out-thinking an adaptive opponent. Variance is hidden information & RNG. Optimization is the solvable systems layer. A game's region is the top one or two axes.

These four aren't one rating split four ways - they're independent 0-10 questions. A 9 on one axis takes nothing from the others, and a game low on all four isn't missing data: it's telling you there's no skill here to cheat.

The basis is how the majority actually plays this game - not a niche mode or a personal run. For a game with very different modes (a story campaign versus ranked multiplayer), that's the version most people mean.

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Ranked by shared genre first, then by how closely the cheat-test profile matches - a cross-genre tag means a pick matched on skill profile alone.

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Game details

Released
2006
Developer
Dingo Games
Platforms
browser, Android, PC, iOS, Mac
Modes
Single player
IGDB users
67/100
SportAdventureIndieAction