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Codecraft’s TinyML course teaches embedded machine learning with a graphical, Scratch 3.0-based programming environment and the Wio Terminal. Its lessons guide learners through creating a model, collecting data, training and deploying the model, then programming a device to use its predictions. This legacy block-based Codecraft is distinct from Seeed’s current conversational CodeCraft assistant, so choose instructions and hardware for the specific course or workflow you intend to use.

What Codecraft means in the TinyML course

The name can refer to two different Seeed-related tools. In the TinyML course, Codecraft is the legacy graphical environment: Seeed Studio’s CodeCraft repository describes it as “a graphical programming software which is based on Scratch 3.0.” Learners use visual blocks rather than writing every instruction as text. The course pairs that environment with the Wio Terminal to introduce embedded machine learning.

Seeed’s current CodeCraft website describes a different, browser-based workflow in which a user selects hardware, describes a project in natural language, reviews generated code, flashes it to a board, and debugs it. Seeed describes cloud compilation and one-click upload as capabilities of that service. Those features do not make the current assistant the same product as the older graphical block editor, nor do they establish that current CodeCraft hardware works with the older TinyML lessons. Seeed’s current CodeCraft site and CodeCraft wiki document that newer workflow.

How the TinyML learning process works

The course organizes embedded ML into four stages. The goal is to make a trained model part of a working microcontroller project, rather than stopping at model training on a computer.

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  1. Model creation: Define the problem the device should solve, such as recognizing motion or a gesture.
  2. Data acquisition: Collect examples with the sensors used by the project. The examples need to represent the inputs the model is expected to recognize.
  3. Training and deployment: Train a neural network, then deploy it for inference on a Cortex-M microcontroller.
  4. Programming: Use the device’s inference results in a program—for example, to show information or control other hardware.

The TinkerGen / Seeed Education course repository describes seven step-by-step projects and was introduced in June 2021. Its examples include motion recognition using the Wio Terminal’s built-in accelerometer, gestures using a light sensor, and recognition using a thermal camera. The course presents itself as suitable for learners without prior programming or electronics knowledge; that is its stated design premise, not a measured learning outcome.

What hardware you need

The Wio Terminal is the anchor device for this TinyML course. The course’s stated hardware requirements include one Wio Terminal, four Grove cables, a Grove Multichannel Gas Sensor v2, and a Grove Thermal Imaging Camera. That list covers course materials, not necessarily the parts required for every individual project. Check the bill of materials for the lesson you plan to follow before buying anything; a project may use only some of the listed parts.

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The legacy Codecraft repository lists these supported devices:

  • Wio Terminal
  • Grove Zero
  • Arduino Mega and Arduino Uno
  • Grove Beginner Kit
  • micro:bit
  • M.A.R.K (CyberEye)
  • GLINT
  • Bittle

This is a general device-support list, not confirmation that each device can run every TinyML course project. Match the board and sensor requirements to the specific lesson. The current CodeCraft site and wiki feature Wio Terminal, XIAO ESP32S3 Sense, and Grove Beginner Kit in the context of the newer assistant; do not treat that list as a compatibility guide for the legacy block-based lessons.

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Choosing the right instructions and board

Before starting, identify whether a tutorial targets the legacy graphical Codecraft or Seeed’s current conversational CodeCraft. Then check these details in the lesson’s documentation:

  • Lesson compatibility: Confirm the exact board and sensors named in that project, rather than relying on a broad repository support list.
  • Inputs: Check which sensors the activity uses, such as an accelerometer, light sensor, or thermal camera.
  • Project needs: Look for requirements involving connectivity, a display, or additional hardware controlled by the board.
  • Parts and kits: Determine whether the lesson expects a board alone, separate Grove modules and cables, or a complete kit.
  • Software generation: Follow instructions written for the same Codecraft workflow you are using; the current assistant’s natural-language code-generation steps are not interchangeable with legacy block-editor directions.

Hardware availability, kit contents, and software access can change. The course repository is the source for the course’s parts list, but it does not establish current retail stock or what a seller’s bundle contains. Verify those details with the lesson and the seller before ordering.

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What the course does—and does not—establish

The course materials explain a beginner-oriented workflow and provide projects to follow. The consulted official descriptions do not provide independent measurements of model accuracy, inference latency, memory use, or learning outcomes. They also do not establish that every board on the legacy support list is suitable for every TinyML example. Treat claims about ease, speed, model quality, or cross-version compatibility as unverified unless a specific lesson or independent test supports them.

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