README.md – MicroBit Tutor AI (MicroPython + micro:bit V2)

Microbit para Taller de Cultura Digital usando Vibe Conding

This gallery example presents a Markdown-based teaching guide for a digital culture workshop, using a vibe coding methodology with the Micro:bit platform. It shows a structured table of 40 mini-projects organized into administrative categories such as time management, inventory control, personnel, finance, and customer service. Each row details a project name, its objective, estimated duration of one or two sessions, and a difficulty rating. The document is purely textual, outlining a pedagogical framework for rapid prototyping rather than visualizing data, with its layout structured through standard Markdown table syntax.

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Gist 3db23ae2b492f204b7dbde1886f8224c

This visualization presents a collection of 45 practical exercises for the micro:bit V2, a microcontroller, designed for educational and prototyping purposes. The dataset is a markdown list of activities, each detailing a project that uses sensors for data acquisition and radio communication for transmission. The projects span categories like sports, classroom, environmental science, and STEM, showing how the physical device can be used to graph data. The text shows the projects are intended for graphing events and for creating a radio transmitter between micro:bits to acquire or transmit data, with no code or libraries used in the rendering.

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Gist 2c891272ae6fcbec4f6d2e7de448177a

This example demonstrates a catalog of 45 MicroPython lab exercises for the Raspberry Pi Pico W, covering Bluetooth (BLE and classic HC-05) communication and OLED display control. Each exercise pairs a specific practice—such as BLE scanning, sensor data transmission, or relay control—with recommended Android/iOS apps and the primary lab function. The code references the `bluetooth`, `machine`, and `ssd1306` libraries to manage wireless links and render output on the SSD1306 or SH1106 display. The list is structured as a Markdown table, detailing the Bluetooth type, app, and purpose for each numbered activity.

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40 Misiones con IA (AI Create) para micro:bit – Actividades Educativas

This visualization presents a table of 40 educational AI activities for the micro:bit, pairing each mission with its objective, sensors, and labels. It shows a structured curriculum, from basic motion detection to cooperative multi-device tasks, while the static screenshot highlights the markdown source file that organizes the content. The rendering relies on the GitHub gist interface to display the raw markdown table, with no interactive code or data processing libraries involved. The visible data is the author’s curated list of sensor-based exercises, emphasizing how the built-in accelerometer, microphone, and light sensor can be used with AI Create to classify gestures, sounds, and environments.

Listado de practica microbit ISC Interfaz (descanzo en alto nivel)

This example presents a curated list of 50 intermediate and advanced practice exercises for the Micro:bit v2, organized into two clear levels. It shows a structured table of contents, detailing each project's number, name, and a brief description of its functionality. The visual output is a straightforward rendering of the provided Markdown file, likely using a Markdown parsing library to display the data in a clean, readable HTML table format. The source is formatted as a Gist on GitHub.

Gist 626d64298c79f4cc42c6382ca71d1680

This example presents a structured guide for using NotebookLM, branded as "NotebookML," in a first-semester high school setting. It outlines pedagogical strategies, recommended resource types, and seven categories of classroom activities, including automatic quizzes and step-by-step tutoring. The visualization includes specific prompt templates for students, such as "explain this as if I were 12," and proposes three complete practical exercises integrating mathematics, cultural digital topics, and Micro:bit programming. The content is organized with emojis and a clear hierarchical structure, relying on the Markdown format for its presentation.

Gist 82d472ceee4909cc7880ce2b2e0885dc

This example shows a MicroPython learning activity where an ESP32 or Raspberry Pi Pico W sends prompts to the OpenAI ChatGPT API and displays the text response on an SSD1306 OLED display via I²C. The code uses `network.WLAN` for Wi-Fi connectivity, `urequests.post` to call the chat completions endpoint, and `SSD1306_I2C` from the `ssd1306` library to render the reply. The demonstration includes connection setup, JSON response parsing, and a simple text-wrapping routine for the OLED’s 16-character line width, with optional scroll or truncation for longer answers.

Gist d68534c6cd5f4fe95e426fb37c694be4

This example presents a Markdown-rendered Python cheatsheet originally authored by Mosh Hamedani, converted and annotated by IoTeacher. It displays code snippets covering variables, input handling, string operations, arithmetic, control flow, loops, lists, tuples, and dictionaries. The visualization is plain text with syntax highlighting applied through Markdown formatting. It does not use JavaScript libraries, datasets, or interactive rendering; instead, it relies on static code blocks and headings to organize the tutorial content.