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CircuitPython Turbo can make the sushi conveyor belt animation run faster by compiling selected, math-heavy Python functions into native machine code. It is not a general speed switch: the project uses MicroPython’s Viper decorators for graphics work, and those functions come with strict typing and memory-safety limitations.

What the sushi conveyor belt project does

Adafruit Learning System’s CircuitPython Turbo Sushi Conveyor Belt, by Liz Clark, is a sushi conveyor-belt animation designed for a long bar display. Clark writes that an earlier CircuitPython attempt was “way too slow,” so she built the earlier version with Arduino and PlatformIO. The Turbo approach revisits the project by accelerating performance-sensitive graphics code while keeping the project in CircuitPython.

How CircuitPython Turbo speeds up graphics

Turbo lets a developer write Python helper modules that use Viper decorators from MicroPython. The helper modules are compiled with mpy-cross for the architecture of the chip that will run them. The resulting .MPY module is placed in the device’s /lib directory on the CIRCUITPY drive. At compilation, Viper-decorated functions become native machine code.

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This can help selected work that performs substantial calculations, such as graphics. It does not mean every part of an application—or every Python workload—will run faster. The guide describes a qualitative benefit for heavy-math graphics, but reports no benchmark figure or testing method, so it does not establish a speedup percentage or multiplier.

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What a Viper function can and cannot do

Viper functions are written in Python syntax but follow a restricted set of rules. The guide quotes Tim C. describing them as “their own weird flavor of Python with an extra set of rules that must be followed.” In the guide’s explanation, local variables and arguments must use supported types: int, uint (32-bit only), bool, ptr, ptr8, ptr16, ptr32, or object.

  • No floats: floating-point values are unavailable in the described Viper functions.
  • No overflow protection: integer values silently wrap, so arithmetic can produce an incorrect result without the usual kind of warning.
  • Indexes need careful bounds checks: an out-of-range index can corrupt memory or crash instead of producing a clean IndexError.
  • Types cannot change: a variable must stay within its declared type rather than being reassigned to a different kind of value.
  • Mixing typed and object values can cost speed: operations involving object values may fall back to slower runtime calls.

These constraints make Viper a targeted optimization, not a place to move arbitrary application code. The useful candidate is a performance-sensitive function whose inputs, numeric ranges, and memory access can be kept within those rules. The guide does not provide a firmware compatibility matrix or a comparison across chip families.

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Which display and controller does the project use?

The guide names a Qualia ESP32-S3 for TTL RGB-666 displays as the controller, alongside two RGB TTL TFT bar-display choices. The displays differ in resolution, physical size, touch support, and the guide page’s displayed price and stock status observed on October 7, 2026:

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Display option Resolution Touch Price and availability shown
Rectangle Bar RGB TTL TFT Display, 3.2-inch 320×820 Capacitive touch $29.95; in stock
Rectangle Bar RGB TTL TFT Display, 4.58-inch 320×960 No touchscreen $19.95; in stock

The 3.2-inch option is the one to consider if touch capability matters; the 4.58-inch option has the higher listed resolution and no touchscreen. The controller is a separate part of the compatibility decision: confirm that the exact display uses the supported TTL RGB-666 interface and pairs with the Qualia ESP32-S3 before ordering. The guide page showed the controller out of stock when accessed on October 7, 2026. Prices and inventory are snapshots, not lasting guarantees.

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What to know before choosing Turbo

  • Use Turbo when a specific computation-heavy section, such as graphics processing, is the bottleneck—not as an assumption that all CircuitPython code will improve.
  • Keep Viper’s supported types and integer ranges in view when writing or adapting a function.
  • Check array and pointer bounds explicitly; an invalid access can damage memory or stop the program.
  • Choose the display based on dimensions and touch needs, then verify controller and interface compatibility.

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