What We Do

Our Services

At {F}Consulting.tech, we have been transforming businesses since 2019 with our cutting-edge IT software and consulting services.
Our goal is simple: to empower your business with technology-driven solutions that drive growth and innovation. Whether you\'re an IT company looking for specialized support or a business owner seeking to harness the power of technology, you\'ve come to the right place.

Custom Software Development

From idea to implementation, we craft tailored software solutions that align with your unique business needs. Our expert developers use the latest technologies to create intuitive and efficient software that boosts your operational excellence.

IT Consulting

Unlock the true potential of your IT infrastructure with our strategic consulting services. We analyze, plan, and execute strategies that optimize your technology stack, enhance security, and streamline processes

Digital Transformation

Stay ahead in the digital age. We facilitate your digital transformation journey by integrating advanced technologies like AI, IoT, and Big Data, driving innovation and delivering exceptional customer experiences

ESP32 and ESP8266 with Micropython

Flash and install firmware


esptool.py --chip esp32 --port /dev/tty.usbserial-1410 --baud 460800 write_flash -z 0x1000 ./ESP32_GENERIC-20231005-v1.21.0.bin


Preliminary actions

Install esptool

download the firmware


flash ESP8266

esptool.py --port /dev/tty.usbserial-0001 erase_flash

Install firmware

esptool.py --port /dev/tty.usbserial-0001 --baud 460800 write_flash --flash_size=detect 0 ./ESP8266_GENERIC-20231005-v1.21.0.bin

Unlocking the Power of Neural Networks: A Deeper Look

Today, we’re diving deeper into the fascinating world of neural networks, shedding light on some critical concepts.

Label and Features:

  • Label (output): Labels are the ultimate goals for our neural networks. They’re like the answers to a challenging question. When you show your AI a picture of a cat, the label would be “cat.” It’s what the network aims to predict.
  • Features (input): Think of features as the building blocks of your data. They’re like the clues that help the network understand and make predictions. In an image, features could be the whiskers, fur, and pointy ears that scream “cat.”

Example with Label (Used for Training):

When we train a neural network, we provide real data with both features (like the cat’s fur and whiskers) and labels (the “cat” tag). The network uses these paired examples to learn how to make predictions accurately. It’s like studying with answer keys to get better at a quiz.

Example without Label (Used for Testing):

For testing, we give our network real data (with features) but keep the labels hidden. The network makes predictions, and we see how well it does. This helps us evaluate its ability to work with new, unlabeled data.


The model is the brain behind the operation. It’s a mathematical representation of how the network should process data to produce the correct output (label). The model learns by tweaking its internal parameters, often referred to as “weights and biases.”

Regression vs. Classification:

In the world of machine learning, we have two main types of tasks: regression and classification.

  • Regression: This is like predicting a number, such as the price of a house given its size. The model finds a function to map inputs (features) to a continuous range of values.
  • Classification: Here, we’re assigning labels to data. It’s like deciding whether a given image is of a cat or a dog. The model sorts data into predefined categories.

Training and Loss:

Training is the phase where our neural network learns from examples. It fine-tunes its model to make better predictions. Loss measures how far off the network’s predictions are from the real labels. It’s like a report card telling the network what it needs to improve.

Linear Regression:

Linear regression is a fundamental technique in machine learning. It’s like drawing a straight line through data points to find the best fit. The goal is to minimize the squared loss, which measures how far each prediction is from the actual label.

The famous formula is

y = mx + b

where y is the label (output), m (or sometimes w) is the wheight, x is the feature (input) and b is the bias

The linear regression model uses a loss function called squared loss (RSS), which quantifies the error between observations and predictions. This loss guides the model in adjusting its parameters (m and b) to get closer to the real labels. In essence, it helps the model become a pro at drawing those best-fit lines. Formula for squared loss is

sum of all (label - prediction(x))2 ==> sum(y - (mx+b))2

The “adjusting” is an iterative phase. At each loop the model tries to reduce the loss applying a new value for m. The new weight value is of the weight plus (or minus, it depends if the loss is negative or positive) a value that will allow us to reach the 0 loss in an efficient way, that is in less iteration than possible. This iteration represents basically the learning rate.

Understanding these concepts is like unlocking the secret language of neural networks. They’re the tools and principles that underpin AI’s incredible capabilities. Stay curious and keep exploring this exciting world!

Popular Python Libraries for Machine Learning

In the exciting world of Artificial Intelligence, having the right tools is crucial. In this lesson, we’ll explore three of the most popular Python libraries for machine learning: TensorFlow, PyTorch, and Keras.


  • What is TensorFlow? TensorFlow is an open-source machine learning library developed by Google Brain. It’s used for a wide range of machine learning and deep learning tasks, including neural networks, natural language processing, and image recognition.
    • Tensorflow architecture is basically a multilayer APIs, where each top layer relies on the direct bottom layer
  • Why is it Popular? TensorFlow is renowned for its flexibility and scalability. It’s highly preferred for building and training deep learning models.
  • Who Uses It? Google, Uber, and Airbnb are just a few of the big names using TensorFlow for various AI applications. Learn more about TensorFlow.


  • What is PyTorch? PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab (FAIR). It’s known for its dynamic computation graph, which is great for research and prototyping.
  • Why is it Popular? Researchers and academics favor PyTorch for its user-friendly interface and dynamic computation capabilities.
  • Who Uses It? Facebook, Twitter, and NVIDIA are among the organizations harnessing PyTorch’s power. Learn more about PyTorch.


  • What is Keras? Keras is an open-source neural networks API written in Python. It serves as an interface for TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). Keras makes it simpler to build and experiment with deep learning models.
  • Why is it Popular? Keras is renowned for its ease of use, making it an excellent choice for beginners. It also allows for seamless integration with TensorFlow.
  • Who Uses It? Keras is widely used across various industries, thanks to its user-friendly approach. It’s a favorite for many startups and researchers. Learn more about Keras.

Choosing the Right Library

Selecting the best library depends on your specific project and goals. TensorFlow is great for large-scale applications, while PyTorch is fantastic for research and experimentation. Keras, with its simplicity and integration options, is often the choice for quick prototyping.

In summary, these libraries are the driving force behind many AI applications. Whether you’re diving into deep learning, conducting research, or building AI-powered products, having these tools in your arsenal will be a game-changer.

Quiz Time:

  1. What is TensorFlow primarily developed by?
    • A) Facebook
    • B) Google
    • C) Microsoft
    • D) Twitter
  2. Which library is renowned for its dynamic computation graph?
    • A) TensorFlow
    • B) Keras
    • C) PyTorch
    • D) Theano
  3. Which library serves as an interface for TensorFlow, Theano, and CNTK?
    • A) TensorFlow
    • B) Keras
    • C) PyTorch
    • D) Scikit-learn
  4. What makes Keras popular among beginners and for quick prototyping?
    • A) Dynamic computation graph
    • B) Ease of use
    • C) Integration with PyTorch
    • D) Complex syntax

Correct Answers:

  1. B) Google
  2. C) PyTorch
  3. B) Keras
  4. B) Ease of use