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Automated website analysis, custom reports, integrations – you can make it possible implementing APIs into your working processes. Find out how marketers can benefit from API implementation techniques

All of us value time and always try to manage it rationally but I know very few marketers who are happy with how they optimize their working processes. With often insane amounts of data to analyze, we spend hours and hours trying to process and organize the results. Here comes the need to implement automation technique and API is exactly what can help you with this task.

API (application programming interface) is a set of subroutine definitions and functions that lets users access the specific features or components of a tool. API allows you to make requests and get data by using the tool’s interface and integrate the results into your custom programs.

So what’s in it for you? How will API help you deal with your daily tasks? In this post, you’ll find the answers and discover the best API implementation techniques for marketers.

What’s the use of APIs for marketers?

The opportunity to upload a large amount of data.

Automation of routine processes.

Saves time as there’s no need to set tasks for developers or downloading any additional software.

Analytics and business documentation integration.

Creation of the customized reports. Most APIs provide this option.

Best API implementation techniques

APIs may be used for different purposes. I’ll highlight the best techniques that facilitate digital marketing tasks.

Track your performance metrics

Marketing performance evaluation is a crucial step on your way to a successful marketing strategy. There are 27,725,874 live websites using Google Analytics to track their performance metrics, so you are most likely to run one of these websites.

This web service helps users better understand their audiences, evaluate marketing performance and identify tactics that are working well. However, accessing the tool’s interface to get the necessary data is not always convenient.

The Google Analytics Reporting API will provide you with quick access to report data in Google Analytics.

Why you need it

With this API, you can:

Automate сreation of complex reporting tasks

Create custom dashboards for Google Analytics reports

Integrate Google Analytics data with your business applications

An action plan

Google developers regularly update guides to help users integrate Google Analytics data with their own businesses.

Analyze website promotion efficiency analysis by regions

It often happens that website owners don’t use country-specific domains when targeting different regions speaking one common language. Although their geolocation isn’t crucial for website visitors, it’s important for search engines. For instance, even if your page ranks on the first page of Google UK search results, it doesn’t mean it takes similar positions in Google USA.

Why you need it

If you run a blog for several regions, you should track how its pages perform in each region. You can check it with any SEO tool that provides a rank tracking feature. But you’ll have to switch between different Google regions and spend your time and limits. With Serpstat API, you can simplify this process.

An action plan

When it comes to API implementation, most people prefer their old-school methods rather than spending a lot of time trying to figure out how it works. As I’ve already mentioned Serpstat API is created to simplify your SEO analysis. That’s why the Serpstat team and users created lots of scripts or documents so that handling this API doesn’t require deep expertise.

One of the users created an easy-to-use script that lets you see how well your page ranks in different regions.

Let’s look into how it works. The script collects keywords that the page ranks for in two regions and compares positions of the common keywords. For example, page A is ranking for N phrases in the X search engine and for M phrases in the Y one. The script processes a number of N and M keywords and singles out only the common ones.

To analyze your page, enter its URL, select search engines, set a keyword limit and pagination size, and enter your Serpstat API token (generate it in your personal account).

In the result, you’ll see the table with the page’s positions for all the common keywords in the selected regions.

Analyze your competitors’ content and generate ideas

The statistics from the Content Marketing Institute (CMI) show that 91% of B2B marketers use content marketing. At the same time, CMI’s 2023 B2C content marketing stats reveal that 86% of B2C marketers consider content marketing a key marketing strategy.

It’s not a secret that producing content consistently is crucial for your website. Content lets you drive links, build brand awareness, increase conversions, and more.

But how you generate ideas that will help you reach the above-mentioned goals? You should start by gathering as much information as possible and learning what is working well in your industry.

Every content marketer knows that Buzzsumo is a great tool for this purpose. Whether you conduct keyword-based or domain-based search, the tool quickly identifies content that is working best in this space.

But what if you need something more powerful? Analyzing a batch of keywords or domains will take a long time if you’re using the Buzzsumo web interface. Fortunately, there’s Buzzsumo API available for people who want to automate their content analysis.

Why you need it

Buzzsumo API allows users to access BuzzSumo article, sharer, influencer and trending data in a structured format. With it, you’ll get highly specific reports that handle content creation processes for you.

Buzzsumo API is useful for marketers who want to:

Run the tool for lots of keywords and lots of domains at the same time.

Gather keywords from their SEO tool API and quickly pass the output straight into the Buzzsumo API.

Add in additional metrics Buzzsumo doesn’t provide you with (Majestic metrics, Serpstat metrics, etc.).

An action plan

Paste up to five keywords or domains, enter your API key, and enjoy the results.

Optimize your SEO efforts: Comprehensive keyword research

Search engine optimization is an essential part of your marketing strategy and keyword research is the first thing you should consider when developing your website structure.

Why you need it

Have you ever researched keywords? You begin with brainstorming all the seed keywords and then analyze them one by one entering the keywords into your SEO tool’s search box. Each time you start from scratch, filter the results and export the right keywords. This is a pretty time-consuming process when you have an online shop to collect keywords for.

That’s where SEO tool’s APIs come in handy. I’ll illustrate it with the Serpstat API. Serpstat’s team created an easy-to-use document that lets you not only collect keywords but also analyze domains or URLs.

An action plan

The main benefit of using API that works on the basis of Google Sheets is its ease of use. Here are six simple steps to getting a comprehensive keyword report:

Make a copy of the document.

Generate your API token in your account.

Enter it into the relevant cell of the document.

Brainstorm your seed keywords and enter the list into the Keywords column.

Give your permission to run a script and wait for the results.

Save your time automating the routine processes

You might have seen API is not a terrific word only programmers are able to understand. Fortunately, there are lots of ready-to-use scripts and documents that let everyone benefit from using APIs. Don’t miss your chance to save your time and become more productive automating the routine processes.

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Mediapipe Tasks Api And Its Implementation In Projects


Deep Learning has revolutionized the field of AI by enabling machines to learn and improve from large amounts of data. Mediapipe, a cross-platform and open-source framework for building multimodal ML pipelines, has introduced a new Tasks API that makes it easier than ever to incorporate Deep Learning models into your projects.

This article will explore three exciting projects using the Mediapipe Tasks API focused on a separate domain: Audio, Image, and Text. With these examples, you will learn how to apply Deep Learning to solve real-world problems and build cutting-edge applications.

Before we jump into building end-to-end projects, let’s first look at Mediapipe.

Learning Objectives:

In this article, we will:

We will understand the basics of Mediapipe tasks API.

How to install it in the local system?

How to build our own object detection?

How to implement object detection using OpenCV and Mediapipe?

How to implement audio classification using Mediapipe?

Understanding text sentiment analysis and implementing it using Mediapipe

This article was published as a part of the Data Science Blogathon.

Table of Contents What is Mediapipe Tasks API?

Mediapipe is an open-source and flexible framework for building multimodal ML pipelines that allow developers to create complex processing graphs for Audio, Image, and other sensor data. It provides a set of pre-built components called “Graphs” that are easily combined to create end-to-end ML pipelines.

With the recent release of the Mediapipe Tasks API, developers can now access pre-trained Deep Learning models for various tasks, including Audio, Image, and Text processing. These pre-trained models are trained on large datasets using state-of-the-art techniques. They are made available in the “tflite” format, optimized for deployment on a broad range of edge devices such as IoT and Android/IOS. The Tasks API provides a simple and compatible interface for using these models, making it easy for developers to integrate Deep Learning into their projects without needing a deep understanding of the underlying models.

Installation of Mediapipe

To install mediapipe in your local system, you can use pip install and a specific version of mediapipe.

pip install mediapipe==0.9.1

One can also use Google Colab to run the following projects. Run the following commands in Google Commands:

!pip install -q flatbuffers==2.0.0

!pip install -q mediapipe==0.9.1

Let’s go ahead and build our first project.

Project 1: Build your own Object Detection

Object detection is a Computer Vision technique that involves identifying and locating objects within an image or video. It is a critical task in various applications such as Surveillance, Autonomous Vehicles, and Robotics. In simpler terms, object detection is like finding hidden treasures in a picture or video. Imagine playing a game to locate all the objects hidden in an image. Object detection is like a computer playing a game, but instead of finding the objects for fun, it does it to help us solve real-world problems.

Now in this project, you will:

Understand how Mediapipe Tasks API can simplify the process of object detection by providing pre-trained models and machine learning algorithms.

Understand the significance of the tflite format and how it can help developers deploy c on mobile devices.

Implement Object Detection using OpenCV and Mediapipe

First, we need to import the required libraries.

import cv2 import matplotlib.pyplot as plt import mediapipe as mp from mediapipe.tasks import python from mediapipe.tasks.python import vision

Download the Pre-trained Model: Efficientdet Lite Model. And you can use any input image that you need to detect objects.


#use Mediapipe Tasks API base_options = python.BaseOptions(model_asset_path=model) options = vision.ObjectDetectorOptions(base_options=base_options,score_threshold=0.5) detector = vision.ObjectDetector.create_from_options(options) #using Mediapipe Image Attribute initialize the input image path. image = mp.Image.create_from_file(input_image) detect_image = detector.detect(image) image = image.numpy_view()

Mediapipe Tasks API works based on three endpoints:

BaseOptions: This line initializes the BaseOptions class with the path to the object detection model in TFLite format, which is ‘efficientdet_lite2.tflite’ in this case.

ObjectDetectorOptions: This line initializes the ObjectDetectorOptions class with the BaseOptions object as a parameter. Additionally, it sets the minimum score threshold for object detection to 0.5, which denotes that the bounding box will be marked only when the probability score is more than 0.5.

ObjectDetector: This line creates an instance of the ObjectDetector class using the ObjectDetectorOptions object as a parameter. The create_from_options method initializes the ObjectDetector with the specified options.

Since we used Mediapipe to read the input image path, it needs to be converted into numpy to add the bounding box, label, and mAP score on the detected object.

for detection in detect_image.detections: # Insert bounding_box bbox = detection.bounding_box # the bounding box contains four parameters: #x, y, width and height start_point = bbox.origin_x, bbox.origin_y end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height cv2.rectangle(image, start_point, end_point, (0,255,0), 25) # mAP score and the Detected image label target = detection.categories[0] category_name = target.category_name score = round(target.score, 2) label = f"{category_name}:{score}" loc = (bbox.origin_x+15,bbox.origin_y+25) cv2.putText(image, label, loc, cv2.FONT_HERSHEY_DUPLEX,14,(255,0,0),20) plt.imshow(image)

The provided code detects objects in an image by drawing a bounding box around each detected object, displaying the object label and its mAP score on the image. To accomplish this, the OpenCV library helps to draw the bounding boxes and text on the image.

Putting All Together: Object Detection using Mediapipe Tasks API import cv2 import matplotlib.pyplot as plt import mediapipe as mp from mediapipe.tasks import python from mediapipe.tasks.python import vision input_image = "surface-81OnSSXJo-I-unsplash.jpg" model = "efficientdet_lite2_uint8.tflite" #use Mediapipe Tasks API base_options = python.BaseOptions(model_asset_path=model) options = vision.ObjectDetectorOptions(base_options=base_options,score_threshold=0.5) detector = vision.ObjectDetector.create_from_options(options) #using Mediapipe Image Attribute initialize the input image path. image = mp.Image.create_from_file(input_image) detect_image = detector.detect(image) image = image.numpy_view() for detection in detect_image.detections: # Insert bounding_box bbox = detection.bounding_box # the bounding box contains four parameters: #x, y, width and height start_point = bbox.origin_x, bbox.origin_y end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height cv2.rectangle(image, start_point, end_point, (0,255,0), 25) # mAP score and the Detected image label target = detection.categories[0] category_name = target.category_name score = round(target.score, 2) label = f"{category_name}:{score}" loc = (bbox.origin_x+15,bbox.origin_y+25) cv2.putText(image, label, loc, cv2.FONT_HERSHEY_DUPLEX,14,(255,0,0),20) plt.imshow(image) plt.axis("off")

Time for the second project

Project 2: Audio Classification to detect Speech or Silent

Audio classification involves categorizing audio signals into predefined classes based on their content. It is an important task, as it has numerous applications in Music, speech recognition, and sound monitoring.

Mediapipe tasks API provides a range of ML algorithms for audio classification applications. These algorithms are optimized for processing sequential data and are capable of learning complex patterns in audio signals. Popular algorithms include RNN and CNN, which are capable of processing spectrograms and other time-frequency representations of audio signals.

Implement Audio Classification using Mediapipe

First, import the required libraries. In our example, we will use .wav audio files, thus import wavfile from chúng tôi to process the input audio file.

from mediapipe.tasks import python from mediapipe.tasks.python import audio from chúng tôi import wavfile import urllib import numpy as np

Yamnet is the Transfer learning technique to classify an audio signal using Mediapipe. We don’t have to worry about converting the time domain signal to the frequency domain. Yamnet’s pre-trained model will take care of that. Download Model: Yamnet Tflite model.

model = "yamnet_audio_classifier_with_metadata.tflite" #download sample audio file audio_file_name = 'speech_16000_hz_mono.wav' urllib.request.urlretrieve(url, audio_file_name)

The sample audio can be displayed using the following code:

from IPython.display import Audio, display file_name = 'speech_16000_hz_mono.wav' display(Audio(file_name, autoplay=False))

The audio is of just 4 seconds which is both speech and silent background sound. Here the target in Audio Classification is to predict whether the audio contains speech or silence. The process is similar to the previous code example.

For AudioClassifierOptions, we pass max_results instead of threshold score. max_results=4 specify the timestamps to record in the audio file. The max_results specifies the maximum number of classification results that the classifier should return. In this case, the value is set to 4, which means that the classifier will return up to 4 different predictions, ranked in order of their confidence level.

#mediapipe tasks API endpoints base_options = python.BaseOptions(model_asset_path=model) options = audio.AudioClassifierOptions(base_options=base_options, max_results=4) classifier = audio.AudioClassifier.create_from_options(options)

To read the input audio file and process the AudioData we use scipy and Mediapipe container components.

sample_rate, wav_data = audio_clip = containers.AudioData.create_from_array(wav_data.astype(float) / np.iinfo(np.int16).max, sample_rate) result = classifier.classify(audio_clip)

Now we shall loop through a result list of timestamps and corresponding classification results, and printing out the top classification label and score for each timestamp.

for idx, timestamp in enumerate([0,750,1500,3000,4500]): target = result[idx] label = target.classifications[0].categories[0] print(f'Timestamp {timestamp}: {label.category_name} ({label.score})') Putting All Together: Audio Classification Using Mediapipe Tasks API from mediapipe.tasks import python from mediapipe.tasks.python import audio from chúng tôi import wavfile import urllib import numpy as np model = "yamnet_audio_classifier_with_metadata.tflite" #download sample audio file from Mediapipe Assets storageapis audio_file_name = 'speech_16000_hz_mono.wav' urllib.request.urlretrieve(url, audio_file_name) base_options = python.BaseOptions(model_asset_path=model) options = audio.AudioClassifierOptions(base_options=base_options, max_results=4) classifier = audio.AudioClassifier.create_from_options(options) sample_rate, wav_data = audio_clip = containers.AudioData.create_from_array(wav_data.astype(float) / np.iinfo(np.int16).max, sample_rate) result = classifier.classify(audio_clip) for idx, timestamp in enumerate([0,750,1500,3000,4500]): target = result[idx] label = target.classifications[0].categories[0] print(f'Timestamp {timestamp}: {label.category_name} ({label.score})')

Lastly, let’s complete our final project

Project 3: Text Sentiment Analysis

Sentiment analysis is a subfield of Natural Language Processing (NLP) that aims to extract information from text, such as opinions, emotions, and attitudes expressed by individuals. The goal of sentiment analysis is to automatically classify the polarity of a piece of text, whether it is positive, negative, or neutral.

In comics, sentiment analysis extracts the emotional tone of a character’s dialogue or a particular scene. With the help of sentiment analysis, we can automatically classify the sentiment of the dialogue as negative, which can help us understand how the character is feeling and how the emotions may impact the storyline. In this example, we shall take two dialogues from DC/Marvel comics and apply Sentiment Analysis using Mediapipe pre-trained BERT model.

Implement Sentiment Analysis using Mediapipe

This program is pretty straightforward. Since this is the third project, you now have an idea about how to use API endpoints in a Python program. It’s an NLP project, so our input will now be a text.

from mediapipe.tasks import python from mediapipe.tasks.python import text sample_text1 = "We’ll do our part, dear sister, and let our maker do his!…It’ll work out" sample_text2 = "Some people are in such utter darkness that they will burn you just to see a light"

Let us define the three API endpoints of Tasks API. Download the pre-trained transformer model: Bert Text Classifier

model = "bert_text_classifier.tflite" base_options = python.BaseOptions(model_asset_path=model) options = text.TextClassifierOptions(base_options=base_options) classifier = text.TextClassifier.create_from_options(options)

I hope at this point, the above-mentioned code is self-explanatory. We are using the same 3 API endpoints to classify the label or sentiment of the text.

for input_text in [sample_text1,sample_text2]: sentiment = classifier.classify(input_text) label = sentiment.classifications[0].categories[0] print(f"{input_text} is:n {label.category_name}. Score:{label.score}n") Putting All Together: Text Sentiment Analysis Using Mediapipe Tasks API from mediapipe.tasks import python from mediapipe.tasks.python import text #example text to classify sample_text1 = "We’ll do our part, dear sister, and let our maker do his!…It’ll work out" sample_text2 = "Some people are in such utter darkness that they will burn you just to see a light" #define mediapipe API endpoints model = "bert_text_classifier.tflite" base_options = python.BaseOptions(model_asset_path=model) options = text.TextClassifierOptions(base_options=base_options) classifier = text.TextClassifier.create_from_options(options) #get the sentiment for input_text in [sample_text1,sample_text2]: sentiment = classifier.classify(input_text) label = sentiment.classifications[0].categories[0] print(f"{input_text} is:n {label.category_name}. Score:{label.score}n")

Yes, we did it👍


In conclusion, the Mediapipe Tasks API has proven to be a powerful tool for implementing Deep Learning models in real-world projects. The key takeaways are:

The Mediapipe Tasks API is a versatile and easy-to-use tool for implementing Deep Learning models in real-world projects. Its pre-trained models are accurate and robust, and it provides a wide range of APIs and tools for data processing and model evaluation.

Through building three projects using the Tasks API, we have demonstrated the applicability of Deep Learning in solving problems across various domains. From object detection in images to audio classification and sentiment analysis, Deep Learning has been used for the automation of a wide range of tasks.

Pre-trained models are an essential component of Deep Learning projects, as they provide a starting point for training and can save time and resources compared to training a model from scratch.

Integration of Mediapipe to other tools and APIs is straightforward.

I hope these examples have inspired you to explore the potential of the Mediapipe Tasks API for your projects.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


Top 10 Important Business Analysis Techniques

Business analysis is a process to analyze an organization’s business needs and identify opportunities to improve or exploit. Business analysis is the

Business Analysis Disciplines

Business analysis is a broad term that includes a number of different disciplines. There are three main types of business analysis: functional, process and organizational. Functional business analysis looks at the current system to see how it works and what the customer needs. Process business analysis looks at how the process is executed by examining its steps and workflow. Organizational business analysis examines the corporate culture and how it performs in relation to customer needs, market conditions, competition, etc. A great way to increase your chances of success in any type of business analysis is by bringing together people with

Below we will list down the important business analysis techniques: SWOT Analysis

A SWOT analysis is a quick and simple way to identify the strengths, weaknesses, opportunities and threats of a business. A SWOT analysis is an instrument that is used to compile information about the company, its strengths, weaknesses, opportunities and threats. It is a very practical organizational tool that helps in analyzing performance and potential of the business. This technique identifies significant aspects of a business or organization so it can take steps in the right direction with clear strategies for success. SWOT analysis is commonly used in smaller businesses and startups.  

MOST Analysis

MOST analysis is a common form of qualitative research that helps to determine which purchasing motivations are most important for individual consumers. MOST analysis is a process where the researchers ask the consumers what they think motivates them to purchase a certain product and how much they value each motivation. The survey consists of five motivations – money, other people, status, image and fear of missing out. The survey asks respondents which two they consider most important or how happy they are with each aspect among participants.  

Business Process modelling

Business process modelling is the process of analyzing your business processes and then providing a diagram that identifies where efficiencies can be made. Business Process Modelling is important for any company looking to improve its operational efficiency. It can help you identify what processes are most time consuming, which ones are redundant and what could be done differently to make your business more productive. Business Process Modelling also provides a blueprint for future growth opportunities, by measuring the potential impact of new technologies on company operations.  

Use Case Modeling

The use case model is a representation of the system being developed. The process involves identifying stakeholders, actors, and use cases. The use case model is a representation of the system being developed. The process involves identifying stakeholders, actors, and use cases. This method can be used by business analysts to determine the requirements of a system from an end user’s perspective. It will also help them identify gaps that need to be filled in by software development teams. Use Case modelling is an integral part of agile software development because it helps engineers understand how the product will be used and what it must accomplish during each stage of its lifecycle.  


Brainstorming in business analysis is a way of generating new ideas and solutions for problems. It’s a collaborative process that involves many people. Brainstorming is important to businesses because it helps increase productivity, creativity, and problem solving skills. This process also gives workers a chance to think about their own ideas without the pressure of having to come up with an answer immediately. It can be challenging to get people from all levels in an organization involved in brainstorming sessions. But it’s worth the effort because the more diverse viewpoints that are included, the better solutions can be found.  

Non-functional Requirement Analysis

Non-functional requirements are often overlooked, but they are the most important part of a software. These requirements include security, reliability, scalability, usability and accessibility among others. They are more difficult to test and assess than functional requirements because they are not code-based and their effects are not immediately visible.  

PESTLE Analysis

There are several factors that need to be taken into consideration when performing a PESTLE analysis. These include: – Political landscape – Economic stability – Social conditions – Technological environment – Legal and regulatory framework. PESTLE analysis is a tool that can be used to assess the external and internal environment in which a business operates. It provides a snapshot of the political, economic, social, technological, legal, environmental and competitive factors that shape an organization’s operating environment. PESTLE analysis is useful because it helps business people to see both the opportunities and challenges present in their sectors.  

Requirement Analysis

Requirement analysis is a critical stage of a project because it is the stage where we know what are the requirements that need to be fulfilled. A project can be failed if its requirements are not met. Requirement analysis is a systematic and research-oriented process to identify, analyze, and document the needs or requirements of stakeholders in all aspects of a proposed product or service. It involves identifying stakeholder needs, understanding stakeholder priorities, and synthesizing this information into detailed requirements for how to satisfy these needs.  

User Stories

User stories are a great format for documenting the requirements of a new system. They are also often used by teams to coordinate their work. User stories help us to understand the motivations and priorities of the users in different ways. The user stories represent an atomic unit of system functionality. The team then needs to break these user stories into tasks and estimate how long they will take.  


CATWOE stands for context, audience, task, work environment, organization, and equipment. It is a mnemonic device to help analysts to remember the essential aspects of the context in which they are performing analysis.

Janitor Ai Chatbot: Revolutionizing Automated Assistance

See More : Is Janitor Down Right Now? The Status of Janitor AI

Janitor AI harnesses the power of natural language processing (NLP) to intelligently understand and respond to user queries. NLP allows the chatbot to interpret and comprehend human language, enabling it to provide accurate and relevant responses. With NLP capabilities, Janitor AI can engage in natural and meaningful conversations, making interactions more intuitive and user-friendly.

One of the standout features of Janitor AI is its emphasis on personalization. Users have the freedom to choose from a diverse range of chatbots that cater to their specific needs. Whether it’s a chatbot specialized in customer support or one focused on content creation, Janitor AI offers a wide array of options. Additionally, users can even design their own personalized chatbot, tailoring it to their unique requirements. This level of customization ensures that Janitor AI adapts to individual preferences, providing a truly personalized experience.

Janitor AI offers flexibility when it comes to integration. It can be utilized as a standalone online chatbot, allowing users to access its features directly through the website. This standalone functionality ensures easy accessibility for individuals who prefer a web-based experience. Moreover, Janitor AI can also be integrated into projects as an Application Programming Interface (API). This integration capability enables developers to leverage Janitor AI’s features and incorporate them seamlessly into their own applications or platforms, enhancing the overall functionality and user experience.

Also Read : Janitor AI Not Loading: Tips and Solutions

Janitor AI has gained popularity for its NSFW (Not Safe For Work) filters, which can be disabled. While these filters are essential for maintaining appropriate and safe communication, Janitor AI recognizes the importance of user control. By allowing users to disable the NSFW filters, Janitor AI empowers individuals to customize their chatbot experience according to their own preferences and requirements.

Using Janitor AI as a chatbot is a simple and straightforward process. Users can visit the official website and register for an account. The registration process is user-friendly, requiring basic information to set up an account. Once registered, users gain access to the wide range of features and functionalities offered by Janitor AI. Whether it’s scheduling appointments, writing emails, or seeking assistance in various tasks, Janitor AI is ready to lend a helping hand.

Q1: What tasks can Janitor AI assist with?

Janitor AI is designed to assist with various tasks, including appointment scheduling and email writing. It offers a versatile set of features that cater to different user needs.

Q2: How does Janitor AI understand user queries?

Janitor AI leverages natural language processing (NLP) algorithms to understand and interpret user queries accurately. This enables it to provide relevant and meaningful responses.

Q3: Can I personalize Janitor AI according to my requirements?

Absolutely! Janitor AI offers a range of chatbot options to choose from. You can select a chatbot that aligns with your needs or even design your own personalized chatbot.

Q4: Can I integrate Janitor AI into my own projects?

Yes, you can! Janitor AI can be integrated into projects as an API, allowing developers to incorporate its features seamlessly into their applications or platforms.

Q5: What are NSFW filters, and can they be disabled?

NSFW filters, which stand for Not Safe For Work filters, are designed to prevent inappropriate content. Janitor AI recognizes user preferences and allows the disabling of NSFW filters, giving individuals more control over their chatbot experience.

Q6: Is there an alternative to Janitor AI?

Janitor AI has revolutionized the way we interact with automated assistance. With its AI-powered capabilities, it offers seamless communication and personalized experiences. Whether you’re scheduling appointments or seeking help with email writing, Janitor AI is a reliable companion. Moreover, the ability to disable NSFW filters and the option to integrate it into projects as an API further enhance its versatility. However, if you’re looking to explore alternatives, ChatGPT is a highly recommended option. Embrace the power of AI and enjoy the benefits of automated assistance with Janitor AI and ChatGPT.

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Hextech Repair Tool • Lol Automated Troubleshooting

Our Review Pros Easy to use Easy to install Troubleshoots issues effectively Cons None How to install Hextech Repair Tool

You can deploy Hextech Repair Tool on your Windows PC without significant efforts. All you have to do is download the installer executable (obviously) and launch it. The installer will probably ask for your permission a couple of times.

After you grant it access to perform modifications on your system (required), the installation will carry on automatically. No additional assistance will be requested from you, as the installer will take care of everything. You won’t be even able to choose the destination path.

However, it’s worth mentioning that Hextech Repair Tool will head wherever the game is installed. It will identify the drive you’ve installed League of Legends on, and create a “Riot GamesHextech Repair Tool” folder combination in the root directory.

User-friendly interface

If you’re not exactly tech-savvy, don’t fret. This is exactly the reason why this tool was developed in the first place. You can use it to troubleshoot various issues with League of Legends without being a PC expert. You can stick to your gaming and Hextech Repair Tool will do the rest for you.

After installation, Hextech Repair Tool will be launched automatically. Therefore, if you’ve already installed it, you should now be facing the marvelous main screen of this program. Here you can perform a bunch of configurations before you launch the troubleshooter. Let’s see what those are.

How to use Hextech Repair Tool

In the main screen, you’ll be able to see a bunch of checkboxes, a region selection dropdown menu, and a couple of buttons used to toggle between Options and Task Log sections. Note that the Task Log will initially be empty if you didn’t run any assessments before (i.e. pressing “Start”).

In the region selection section you’ll also be able to see a bunch of status buttons. These provide you with a quick overview of the current situation regarding the game itself, the store, the website, and the client.

Hextech Repair Tool can be used to run ping tests, synchronize your system clock, gather system and LoL logs, force the repatching of the game, reinstalling the game altogether, create a firewall exception (which subsequently enables it), disable the firewall, and choosing between auto and public DNS servers.

Collects system logs for future investigation

If Hextech Repair Tool doesn’t cut it for you, then you might need to address your issue to a higher entity: the tech support crew. However, you can greatly simplify the support team’s tasks by providing them with useful insight, which this tool can help collect.

All you have to do is make sure that the “Gather System & LoL Logs” checkbox is selected before pressing start. The app will collect various helpful information bits from your system and LoL logs and will conveniently place them within a ZIP file.

When the time comes, you can attach the ZIP folder to the ticket/email you’ve prepared for League of Legends’ support team.

Note: if you’re not comfortable with Hextech Repair Tool harvesting info from your PC, you can turn this feature off. You can still enjoy every other of its features, minus the log collection part.

Handy automated troubleshooting tool for League of Legends

To wrap it up, if you’re experiencing technical issues with League of Legends and don’t know what to do, Hextech Repair Tool can help you perform automated troubleshooting. It can also collect a bunch of system and LoL logs in case you’ll need to hand them over to the real experts.

You can download and install the Hextech Repair Tool for free, as there’s no cost associated with it. It comes with a user-friendly interface and can be used even by novices, regardless of PC operating skills and/or previous experience with similar software solutions.

FAQ: learn more about Hextech Repair Tool

How to use Hextech Repair Tool?

After installing it on your PC, you just run it, configure it to your liking and hit the Start button. It will automatically attempt to fix whatever’s wrong with League of Legends on your computer.

How long does Hextech Repair Tool take?

Depending on the issue(s) you’re facing, Hextech Repair Tool might take longer or patch things up in a jiffy. For instance, if it’s a DNS-related issue, it gets solved on the spot, but a complete reinstallation of the game can take several minutes.

How to run Hextech Repair Tool as admin?

Best Free Nutrition Analysis Software For Windows 11/10

Here is a list of the best free nutrition analysis software for Windows 11/10 PC. These free software help you find out and analyze the nutrition values that a particular food item contains. These show you nutrient values like proteins, minerals, fats, carbs, amino acids, fatty acids, vitamins, etc. You can plan your meals according to your target nutrition values. Additionally, these can also be used as a diet planner as these software contain a database of foods and recipes. So, you can plan, manage, and monitor your diet with the help of these free nutrition analysis software.

So, if you are looking for free software that helps you determine nutrition values in your food servings, this article is your stop. You can check out these software and their features, and then get the one you like the most.

What is Nutrition Analysis software?

A nutrient analysis software is the one that enables you to view, track, and analyze the nutrient values of like proteins, minerals, fats, carbs, vitamins, sugar, amino acids, fatty acids, etc. It allows you to monitor the nutrient composition of your meals and recipes and analyze the nutrition reports of your dietary habits. If you have a target value for nutrition intake, nutrient analysis software like CRON-o-Meter helps you. You can find some nice ones below in this post.

Best free Nutrition Analysis software for Windows 11/10

Here is a list of the best free Nutrition Analysis software for Windows 11/10:


eFit Calorie Counter

EasyMenu Balanced Meal Planner


Let us discuss the above software in detail now!

1] CRON-o-Meter

CRON-o-Meter is a free and open source nutrition analysis software for Windows 11. This software is primarily used to monitor and track your daily calorie intakes. And also, check out your progress made by following a specific diet plan. It can be used by different users. All users can enter their specific body profiles to track and analyze their respective nutrition values. They can have their personal diet plan and food intake plans that they can monitor.

To start with, you need to enter your body details including birthdate, gender, height, weight, and activity. You then are required to set your nutritional targets i.e., the number and quantity of nutrients you need in your meals such as protein, carbs, water, caffeine, fats, energy, alcohol, sugars, starch, vitamins, mineral, amino acids, and more. You can enable these nutrients and then enter the respective minimum and maximum values and number of units as per your requirements. It also provides a Set to Dietary Reference Intakes feature. This feature basically set all the target nutrients’ values configured particularly for your body profile.

Once done entering the nutritional targets, you can perform the nutritional analysis to get to your target. You can select the current date, and from the Diet section and tap on the Add serving button. You can then add the food items you intake and also specify the amount of food in gram/oz you intake. It contains a database of popular food items with their respective nutrition values. You can search for a specific food item and then add it to your meal plan.

After you are done adding your food servings, you can have a look at the bottom part of the Diet section where there is a nutrition analysis report. It contains the report in form of both a chart and statistics. The nutrition report shows you the nutrition values of proteins, minerals, amino acids, lipids, and vitamins present in your food servings. On the basis of the report, you can easily analyze various nutrients present in your food items and then compare them with your target nutrients values.

You can save the nutrition analysis report in HTML or text format. To do that, go to the Reports menu and tap on the Nutrition Report option. It lets you select the time period to save the report. It lets you print different reports on paper or save them as a PDF file.

All in all, it is one of the best nutrition analysis software that you get for free.

Read: How to use Food Data Type in Microsoft Excel?

2] eFit Calorie Counter

eFit Calorie Counter is another free nutrition analysis software for Windows 11. It can be used for analyzing nutrition values in your food as well as it can be used as a Meal planner, Calorie Counter, Shopping Manager, and Diet Manager. You can start by adding your body details like height, weight, waist, and more, and then start performing a nutrition analysis.

Using its calorie tracking feature, you can get the nutritional information of different foods and recipes. This feature can be accessed in its Planner section. In the Planner section, you get a database of over ten thousand food items and recipes. From here, you can add your food items and the number of servings based on what it shows you nutritional details. It shows values of protein, fat, minerals, iron, magnesium, etc.

It also lets you create and add new food items and food recipes by manually entering all the nutritional values. You can also plan meals for different times and then track the nutrition values.

This software can be utilized by multiple users. They just have to create their specific profile by entering personal details and body measurements (height, weight, etc.). You can then set your goals like losing weight, gaining weight, and more, and monitor your progress.

Besides planning meals and tracking nutrition values, it also lets you generate your shopping list. A feature to generate a shopping list from websites like Amazon Grocery and more is also present in it.

If you liked it, you can download it from

See: Best free Fitness apps for Windows.

3] EasyMenu Balanced Meal Planner

Another free nutrition analysis app that you can use on Windows 11/10 is EasyMenu Balanced Meal Planner. It is a simple app that shows you nutritional information on food items and recipes based on the number of servings.

It lets you plan three-time meals a week and then track the intake nutrition values. It contains a database of multiple recipes that you can add to your meal plan. About 100 recipes are there in its database. All the recipes show you their respective nutritional information like protein, fat, cholesterol, sodium, vitamins, etc., are available. Apart from nutritional details, it also shows you information like cooking time, cooking methods, and ingredients.

You can add foods to breakfast, lunch, and dinner. And then, go to the Summary section to analyze the nutrition report for a whole week. It shows graphs and statistics of nutrients you intake that include Average daily nutrition, Daily calories, and Average daily FAT intake. Apart from that, you can also create and manage your shopping lists through this app.

Related: 10 Best Foods to Stay Active and Energetic in Office.

4] NutritionInfo

NutritionInfo is a free nutrition analysis app for Windows 11/10. As its name suggests, this simple app gives you information on the nutritional values of various fruits, vegetables, etc. There are mainly four sections called vegetables, leafy vegetables, fruits, and flesh foods. You can explore different items in these categories and get information on the nutrition they contain.

For each food item, it shows values of nutrients like protein, water, ash, starch, sugar, fat, and more. You can easily analyze the nutrition values and plan your meal accordingly. It also shows the information on the background of fruits, vegetables, etc. Besides that, it shares the fun facts and dimensions of a food item. So, basically, you can know about a particular food or vegetable and its nutrition values using this app.

It is a pretty basic nutrition analysis app. You can get it here.

Read: Best Food, Nutrition and Diet apps for Windows PC

What is a Diet analysis?

Dietary analysis (or diet analysis) is a method to analyze the personal diet of an individual based on the food and drink consumed over a specified time. The method helps to measure nutrient intake, energy consumption, and nutrient diversity. By analyzing the routine diet, you can find if you are having a healthy diet in terms of receiving vital nutrients (like Vitamin D, Iron, Protein, etc.).

How to do Nutrient analysis?

To conduct a nutrient analysis, laboratory analysis Is a certified method. In this method, the food sample is sent by companies to the laboratory for physical analysis. There, scientific equipment and methods are used to analyze the food sample for different components. On the other hand, if you want to perform nutrient analysis (or nutritional content of food) on your computer, then nutrient analysis software can be used.

Now read: How to create a Restaurant Menu in Word?

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