Introduction: The Rise of AI in Business

Artificial Intelligence (AI) has been revolutionizing industries, and one of the most transformative AI technologies is the Large Language Model (LLM). From customer service automation to business insights, LLMs are becoming a critical tool for companies looking to stay competitive in today’s rapidly evolving digital landscape. But what exactly are LLMs, and how can businesses benefit from them?

This article will break down LLMs in a way that business professionals can easily grasp — explaining what they are, how they work, their use cases, and the potential benefits they offer to various industries.

What Are Large Language Models (LLMs)?

Large Language Models (LLMs) are a type of artificial intelligence system designed to understand, generate, and manipulate human language at a high level of proficiency. They are built on neural networks that are trained on vast datasets comprising texts from books, websites, research papers, and other written materials. The more data the model is exposed to, the better it becomes at predicting and generating text based on the context.

The most prominent LLMs include GPT-4 by OpenAI, BERT by Google, and T5 (Text-To-Text Transfer Transformer). These models can be used for a range of tasks like:

- Text Generation: Writing articles, reports, or even marketing copy.
 — Translation: Converting text from one language to another with high accuracy.
 — Sentiment Analysis: Determining the emotional tone behind a piece of text.
 — Summarization: Reducing large volumes of text into concise summaries.

What sets LLMs apart is their ability to mimic human-like understanding and generate meaningful, coherent responses in conversations or written text. This capability makes them indispensable for businesses that handle large amounts of textual data.

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How LLMs Work: A Simplified Breakdown

LLMs operate using deep learning, a subset of machine learning where algorithms are structured to mimic the human brain. The model is exposed to large datasets and “learns” patterns, grammar rules, and even subtle meanings in language.

At the heart of an LLM is the Transformer architecture, which allows the model to handle long-term dependencies in text, such as understanding context from earlier sentences to inform responses later in the text.

Here’s a simplified step-by-step process of how an LLM works:

1. Pre-Training: The model is trained on a large corpus of text. This allows the model to learn basic language structure, grammar, and general world knowledge.

2. Fine-Tuning: For specific business applications, LLMs can be fine-tuned on smaller datasets that are industry-specific or task-specific, allowing businesses to get more relevant and accurate outputs.

3. Inference: Once the model is trained and fine-tuned, it can generate responses, translate text, or perform other tasks based on the inputs it receives from users.

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Use Cases of LLMs Across Business Sectors

Businesses across industries are already harnessing the power of LLMs for a range of tasks. Let’s look at some specific examples:

1. Customer Support and Service:
 — LLMs power chatbots and virtual assistants that provide real-time responses to customer inquiries. For instance, companies like Zendesk and Salesforce integrate LLMs to automate customer support, reducing the need for human intervention in common queries.
 — These bots can handle everything from FAQs to more complex troubleshooting, significantly reducing operational costs and improving customer satisfaction.

2. Marketing and Content Generation:
 — LLMs can generate marketing copy, social media posts, or even entire blog articles with minimal human oversight. Jasper AI and Copy.ai are examples of tools that use LLMs to help marketers create content more efficiently.
 — Businesses can also use LLMs to create personalized marketing messages by analyzing customer preferences and behaviors, leading to more effective outreach.

3. Data Analysis and Business Insights:
 — LLMs are now used to process large datasets and generate meaningful business insights from unstructured data, such as customer reviews, social media posts, or internal communications.
 — For example, sentiment analysis can help brands understand public perception and adjust their strategies accordingly.

4. Legal and Compliance:
 — In industries like finance and healthcare, where regulatory compliance is critical, LLMs can be used to review documents, contracts, and policies.
 — They can identify clauses, flag potential compliance risks, and even help draft legal documents, speeding up what would otherwise be time-consuming processes.

5. Human Resources:
 — LLMs are increasingly being used in resume screening, helping HR departments quickly identify qualified candidates by automatically filtering applications based on job requirements.
 — They can also be used for employee engagement surveys, summarizing feedback and providing actionable insights to HR teams.

6. Healthcare:
 — In healthcare, LLMs assist in processing patient data and medical literature to support diagnostic decision-making. Tools like IBM Watson Health use LLMs to extract useful information from vast amounts of clinical data.

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Implementing LLMs in Business: Challenges and Considerations

While LLMs offer a wide range of applications, implementing them requires careful planning and understanding of certain challenges:

1. Data Privacy and Security:
 — When deploying LLMs, especially in industries like finance or healthcare, data privacy concerns need to be addressed. Businesses need to ensure that sensitive information is protected and that AI models comply with privacy regulations such as GDPR or HIPAA.

2. Infrastructure Needs:
 — LLMs require significant computing power, particularly during training. Cloud platforms like Amazon Web Services (AWS) or Microsoft Azure offer scalable solutions, but businesses need to factor in the costs associated with cloud infrastructure.

3. Bias and Fairness:
 — Because LLMs are trained on data sourced from the internet, they can sometimes reflect societal biases. Businesses need to regularly audit their AI systems to ensure fairness and mitigate any potential biases that could harm their reputation or result in regulatory issues.

4. Cost of Fine-Tuning and Maintenance:
 — While pre-trained LLMs can be useful, fine-tuning them to suit specific business needs may require expertise and incur additional costs. Businesses must evaluate the trade-off between adopting a generalized LLM versus investing in a specialized one.

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Benefits of LLMs for Businesses

Now that we’ve explored what LLMs are and how they can be applied, let’s look at the tangible benefits they bring to businesses:

1. Cost Savings:
 — Automating customer service, marketing, and HR processes with LLMs reduces the need for manual labor, leading to significant operational cost savings.

2. Scalability:
 — LLMs can handle large volumes of data and interactions simultaneously, making them ideal for businesses looking to scale without hiring a proportional number of employees.

3. Improved Customer Experience:
 — With LLM-powered chatbots and virtual assistants, businesses can provide real-time support and personalized recommendations, improving overall customer satisfaction and retention.

4. Faster Decision-Making:
 — LLMs can analyze unstructured data quickly, providing insights and recommendations that help business leaders make informed decisions faster.

5. Innovation and Agility:
 — By adopting LLMs, companies can stay ahead of the curve in terms of innovation. AI-driven tools can help businesses adapt to market changes faster and remain competitive.

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Conclusion: Riding the AI Wave with LLMs

Large Language Models are not just another tech buzzword; they represent a fundamental shift in how businesses can leverage AI to streamline operations, enhance customer experience, and generate insights. For business leaders, understanding and implementing LLMs can provide a significant competitive advantage, driving innovation and operational efficiency.

By adopting LLMs, businesses stand to not only optimize their current processes but also unlock new avenues for growth and customer engagement. The potential is vast, and those who tap into it early will undoubtedly shape the future of their industries.

Understanding Large Language Models (LLMs): A Business Perspective was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI agents are autonomous software entities designed to perform tasks and make decisions on behalf of users or systems. They leverage machine learning, LLMs, and other AI techniques to interact with their environment, process data, and achieve specific objectives. These agents often operate in dynamic environments, using contextual understanding and decision-making capabilities to adapt and optimize their actions.

Key Characteristics of AI Agents

1. Autonomy: Operate without continuous human intervention.
2. Adaptability: Learn and evolve based on new data and scenarios.
3. Proactivity: Anticipate user needs or future states and act accordingly.
4. Interactivity: Communicate effectively with users, other systems, or agents.
5. Goal-Oriented: Focus on achieving predefined objectives or solving specific problems.

Applications of AI Agents

AI agents are versatile and can be applied across numerous domains. Here are some key areas where they provide value, particularly in scenarios involving LLM APIs:

1. Customer Support and Virtual Assistants

• Use Case: Automate responses to customer queries, provide step-by-step guidance, or manage customer service tickets.
• LLM Integration: Fine-tuned models like OpenAI’s GPT can generate contextually relevant responses, enhancing customer satisfaction.
• Example: Chatbots for e-commerce platforms, technical support desks, or healthcare FAQs.

2. Content Creation and Moderation

• Use Case: Generate blogs, marketing copy, or product descriptions. Additionally, filter inappropriate content or spam.
• LLM Integration: Use generative models to produce engaging and grammatically correct content, while leveraging classifiers to identify harmful content.
• Example: Social media tools that create posts based on trending topics or applications that moderate online forums.

3. Personalized Learning

• Use Case: Provide tailored educational experiences for students, adapting content based on their progress.
• LLM Integration: Design quizzes, explain complex topics, or provide personalized study plans.
• Example: AI tutors for STEM subjects, language learning applications, or corporate training programs.

4. Recommendation Systems

• Use Case: Suggest products, services, or content based on user preferences and behavior.
• LLM Integration: Enhance recommendation logic by analyzing unstructured data, such as reviews or user feedback.
• Example: E-commerce platforms recommending products or streaming services suggesting movies.

5. Healthcare Assistance

• Use Case: Support medical professionals by analyzing symptoms, suggesting diagnoses, or providing medication information.
• LLM Integration: Interpret patient data, summarize medical research, or generate prescription explanations.
• Example: Virtual health assistants for chronic disease management or post-appointment follow-ups.

6. Business Process Automation

• Use Case: Streamline repetitive tasks like document processing, report generation, or data extraction.
• LLM Integration: Automate drafting and editing reports, summarizing lengthy documents, or extracting insights from data.
• Example: AI agents that manage employee onboarding or process financial audits.

7. Coding and Development Assistance

• Use Case: Help developers write, debug, and optimize code.
• LLM Integration: Generate code snippets, explain algorithms, or provide suggestions for optimizing existing code.
• Example: Integrated development tools like GitHub Copilot or ChatGPT-based code assistants.

8. Dynamic Workflows and Task Management

• Use Case: Assign tasks, prioritize actions, or manage workflows dynamically.
• LLM Integration: Understand user inputs, suggest task priorities, and provide actionable summaries.
• Example: Project management tools with built-in AI agents.

9. Market Research and Data Analysis

• Use Case: Analyze market trends, customer reviews, or competitor strategies.
• LLM Integration: Summarize datasets, generate insights, or perform sentiment analysis.
• Example: Applications for competitive intelligence or consumer behavior analysis.

Technical Considerations for Using AI Agents with LLM APIs

When building applications involving AI agents and LLM APIs, consider the following:

1. API Integration

• Choose an API that aligns with your use case, such as OpenAI, Anthropic, or Google’s PaLM.
• Use APIs for specific tasks, such as text completion, summarization, or translation.

2. Fine-Tuning and Customization

• Fine-tune pre-trained LLMs on domain-specific datasets to improve accuracy.
• Leverage embeddings for semantic search and contextual understanding.

3. Context Management

• Implement strategies to handle conversational history effectively.
• Use techniques like token window optimization to manage long inputs and outputs.

4. Performance Optimization

• Monitor latency and response times of API calls.
• Use batching or caching mechanisms for high-demand applications.

5. Ethical and Responsible AI

• Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA).
• Implement measures to avoid generating harmful or biased content.

6. Scalability

• Plan for scalability to accommodate growing user bases.
• Use cloud-based solutions like AWS or Azure to manage infrastructure demands.

Challenges and Solutions

1. High Costs

• Challenge: LLM API usage can be expensive for large-scale applications.
• Solution: Optimize prompts, limit token usage, and use local models where feasible.

2. Contextual Errors

• Challenge: LLMs may generate incorrect or irrelevant outputs.
• Solution: Implement feedback loops and validation layers.

3. Ethical Concerns

• Challenge: Risk of misuse or biased outputs.
• Solution: Use safety mechanisms like content filtering and human oversight.

Conclusion

AI agents, powered by LLM APIs, offer transformative capabilities across industries. By understanding their potential applications and addressing technical considerations, businesses can build innovative, user-centric solutions. As these technologies evolve, the key to success lies in ethical deployment, robust design, and continuous improvement.

Please reach out to our email digitalneeds.tech@gmail.com

Understanding AI Agents and Their Applications with LLM APIs was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

1. Foundation of ChatGPT: The Transformer Architecture

At the heart of ChatGPT lies the Transformer model, introduced by Google in the 2017 paper “Attention Is All You Need.” Transformers have two key components:

• Encoder: Processes the input text.
• Decoder: Generates output text.

However, ChatGPT uses only the Decoder part, optimized for language generation.

Key Concepts of the Transformer Decoder:

• Tokenization: The input text is broken into smaller units called tokens (words, subwords, or characters).
• Embedding: Each token is converted into a numerical representation (vector) using embedding layers.
• Attention Mechanism: The model uses self-attention to weigh the importance of each token in relation to others.
• Feed-Forward Neural Network: Processes attention outputs and generates predictions.

Number of Layers and Parameters in ChatGPT

Modern versions of ChatGPT, such as GPT-4, consist of up to 96 decoder layers. Each layer contains the following key sub-layers:

1. Multi-Head Self-Attention Mechanism
2. Feed-Forward Neural Network
3. Layer Normalization

Each layer refines the representations of tokens, and the output from one layer serves as input to the next.

Additionally, GPT-4 has an estimated 1.7 trillion parameters, making it one of the largest language models ever built.

2. Tokenization and Embedding: The First Step

When you provide a prompt (e.g., “Write a blog about AI”), the text is broken down into tokens:

['Write', 'a', 'blog', 'about', 'AI']

Each token is then mapped to a high-dimensional space via embedding.

Token Embedding Vector (Simplified Example) Write [0.21, 0.45, 0.78, …] a [0.11, 0.35, 0.22, …] blog [0.56, 0.89, 0.31, …]

These embeddings are the starting points for prediction.

3. Self-Attention Mechanism: Understanding Context

The self-attention mechanism allows the model to focus on relevant tokens while predicting the next word.

For example, if the input is:

"Write a blog about AI and machine learning."

While predicting the next token after ‘AI’, the model will assign higher weights to ‘blog’ and ‘AI’ because they are more contextually relevant.

Multi-Head Attention

Instead of using a single attention mechanism, Transformers use multi-head attention, where multiple attention heads work in parallel to capture different aspects of token relationships.

Attention Head Focus Token Head 1 ‘blog’ Head 2 ‘AI’ Head 3 ‘learning’

The results from each head are combined and passed forward.

4. Feed-Forward Network: Processing Attention Outputs

The outputs from the self-attention layer are passed through a position-wise feed-forward neural network (FFN). Each token embedding is transformed independently through this network, which typically has two linear layers with a ReLU activation function in between.

5. Next-Word Prediction: Real-Life Example

Imagine you’re writing a blog titled “The Future of AI.” You start with the prompt:

"Artificial Intelligence is changing the way..."

Step-by-Step Prediction:

1. The input text is tokenized and embedded.
2. Multi-head self-attention identifies that ‘changing’ and ‘way’ are important tokens.
3. The feed-forward network processes these embeddings.
4. The final layer generates probabilities for the next word.

Example of token probabilities:

Token Probability we 0.35 humans 0.25 organizations 0.15 understand 0.10

The token with the highest probability (‘we’) is selected, and the sentence continues:

"Artificial Intelligence is changing the way we..."

This process repeats until the model generates a complete output.

6. Temperature and Top-k Sampling: Adding Diversity

• Temperature: Controls randomness in predictions (low values = deterministic, high values = creative).
• Top-k Sampling: Limits predictions to the top k probable words.

7. Conclusion

At its core, ChatGPT generates text token by token, using embeddings, multi-head self-attention, and neural networks to predict the most probable next word based on context. With dozens of layers and trillions of parameters, it can generate highly coherent and contextually appropriate text.

Understanding these building blocks not only demystifies ChatGPT but also provides a strong foundation for exploring advanced topics in NLP and AI.

With this knowledge, you’re now better equipped to dive deeper into language models or build projects leveraging ChatGPT’s capabilities!

How ChatGPT Works Internally: A Deep Dive into Next-Word Prediction was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the rapidly evolving world of artificial intelligence (AI), understanding the different types of AI agents can provide valuable insights for businesses and technical professionals. AI agents, including those powered by large language models (LLMs), are entities capable of perceiving their environment and taking actions to achieve specific goals. These agents are classified based on their capabilities and functionalities. This article explores the various types of AI agents, their unique characteristics, and real-world applications in business and technology.

1. Reactive Agents

Reactive agents operate based on the current state of their environment. They do not have memory or the ability to learn from past experiences. Their decision-making is based on predefined rules or heuristics.

Characteristics:

• No memory or internal state.
• Immediate response to stimuli.
• Simplistic but fast decision-making process.

Use Cases:

• Spam Filters: Identify spam emails based on preset rules.
• Autonomous Robots: Basic robots that follow fixed paths or avoid obstacles.
• Real-Time Systems: Temperature control systems that react to immediate changes.

2. Model-Based Agents

Model-based agents build an internal representation of their environment to make decisions. They consider both the current state and predictions of future states to act effectively.

Characteristics:

• Maintains an internal model of the environment.
• Can predict the consequences of actions.
• More complex than reactive agents.

Use Cases:

• Self-Driving Cars: Navigate traffic by predicting the behavior of other vehicles.
• Smart Home Systems: Adjust heating, lighting, or security settings based on patterns.
• Inventory Management: Optimize stock levels by forecasting demand, aiding business efficiency.

3. Goal-Based Agents

Goal-based agents take actions to achieve specific goals. They evaluate various possibilities and choose actions that best achieve their objectives.

Characteristics:

• Goal-oriented decision-making.
• Requires planning and reasoning.
• Flexible in dynamic environments.

Use Cases:

• Route Optimization: Navigation systems that find the shortest path to a destination.
• AI Assistants: Provide tailored recommendations to meet user goals, often leveraging LLMs.
• Logistics Management: Optimize delivery schedules and routes, benefiting businesses.

4. Utility-Based Agents

Utility-based agents go beyond achieving goals; they aim to maximize utility, which represents a measure of success or satisfaction.

Characteristics:

• Considers trade-offs between different actions.
• Balances multiple goals for optimal outcomes.
• Evaluates the desirability of outcomes.

Use Cases:

• Financial Trading Systems: Maximize profits while minimizing risks.
• E-commerce Personalization: Suggest products based on user preferences and satisfaction metrics.
• Energy Management: Optimize energy usage for cost savings and environmental impact in businesses.

5. Learning Agents

Learning agents have the ability to improve their performance over time by learning from experiences. They consist of four main components: performance element, learning element, critic, and problem generator.

Characteristics:

• Can adapt to changing environments.
• Uses data to enhance decision-making.
• Incorporates feedback mechanisms.

Use Cases:

• Chatbots: Improve conversational accuracy over time, often powered by LLMs.
• Predictive Analytics: Forecast trends using historical data for business growth.
• Fraud Detection: Learn new fraudulent patterns to enhance security measures.

6. Multi-Agent Systems (MAS)

Multi-agent systems involve multiple AI agents interacting and collaborating to achieve common or individual goals. These agents can either work cooperatively or competitively.

Characteristics:

• Composed of multiple agents with distinct roles.
• Can communicate and share information.
• Capable of handling complex tasks.

Use Cases:

• Supply Chain Management: Coordinate activities among manufacturers, suppliers, and distributors.
• Traffic Control Systems: Manage urban traffic flow using multiple agents for different intersections.
• Gaming: Simulate realistic scenarios with multiple entities.

Conclusion

Understanding the types of AI agents helps businesses and technical teams make informed decisions about integrating AI into their operations. From reactive agents to advanced learning systems powered by LLMs, each type offers unique strengths suited for specific tasks. Leveraging the right AI agent can drive innovation, improve efficiency, and unlock new opportunities in the business landscape as AI technology continues to evolve.

Understanding Different Types of AI Agents and Their Real-Life Applications was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

Table of contents

1. Prerequisites
2. Setting up SAM and project
3. Creating AWS Codecommit repo & S3 Bucket for storing Artifacts
4. Push the first commit in the master branch
5. Setting up SAM pipeline configuration — dev, prod
6. Push initial commit to verify the deployment

Prerequisites

Before diving deep into the article, I will request everyone to please go through a basic understanding of the following key points.

1. IAM Role, Policy
2. What is serverless framework and SAM
3. Setup AWS SAM CLI

Setting up SAM and project

1. First, we need to download & install SAM CLI via link — https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html
2. After installing SAM CLI from step1, start setting up projects by following the commands mentioned in the screenshot

In the above screenshot, you can see the following commands that will help you in setting up sam based nodejs project in your local system.

1. sam init — This command will initialize your project
2. cd sam-app-test && sam pipeline init — bootstrap-This command will setup the required cloud formation stack for your code pipeline like user roles and make it more automotive to update the deployment pipeline

After setting up the project as mentioned in previous screenshots, this is how your folder structure will look like. There is a file in the above screenshot which is buildspec.yaml. Which is nothing but the one shown below.

Key Points on build phases from the above file

1. install — This command will set up a basic framework or runtime tool like java, nodejs, or python. For more clarity, please refer to the link — https://docs.aws.amazon.com/codebuild/latest/userguide/runtime-versions.html
2. pre_build- This command will execute your basic unit tests
3. build- This command will build your artifacts based on the language you are using
4. post_build- This command will basically zip your entire artifact as a zip or an image based on your requirement and push those on the S3 bucket which will be provided as a runtime variable.

CCreating AWS Codecommit repo & S3 Bucket for storing Artifacts

After this, navigate to the AWS console and create one code repository in AWS Codecommit (Only if you are using AWS Code commit as your VCS)

After this create an S3 bucket which will be used to store artifacts created as part of sam package command.

Push the first commit in the master branch

After creating the repo in the previous step and execute the following commands on your project created in step 2 above.

1. git init
2. git remote add origin https://your-repo-url.com
3. git add .
4. git commit -m” Add Initial commit”
5. git push -u origin master

Setting up SAM pipeline configuration — dev, prod

This step is one of the most important steps to configure. Please refer to the steps below very carefully. There are two pipelines can be created for one or two stages based on your configuration in step 3 above.

1. Navigate to the AWS Code pipeline service
2. Give pipeline a name
3. Create code build project with configuration mentioned in next steps.
4. Choose the image as ubuntu or Amazon Linux 2 based on the runtime you want to choose- Please refer to the link- https://docs.aws.amazon.com/codebuild/latest/userguide/runtime-versions.html
5. Add an environment variable in the building project as S3_BUCKET, which is nothing but the name of the S3 bucket created in one of the steps above.
6. Add the Deployment step by configuring the provider as AWS Cloudformation as in the below screenshot.

Push initial commit to verify the deployment

After that, One can push the code to the code commit repo and once you push the code in this repo, you may see this entire pipeline running from the build to the deployment stage.

One may see that pipeline getting succeeded and you may see the required lambda and AWS SQS getting created. you can verify the same and test SQS and lambda connectivity as well.

Conclusion

This article will help all the architects/developers who may be having challenge in setting up SAM based pipelines where you can deploy AWS lambda or any other resources.

Deploy Node-based SAM App on AWS was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

BAAS

FAAS

In the rest of the article, Let's see what are these two terms and how we can relate them to Serverless platforms.

1. Serverless Definition
2. BAAS
3. FAAS
4. Conclusion

Serverless

Serverless, in layman’s terms, is without a server. That means anything which is not on the server. But that's not true if you ask a cloud or software engineer. Anything, where an application runs that are used by people globally, needs to be managed via a server. The server can be any machine, either it can be on any cloud platform or on any private machine-based server.

When we talk of serverless, we internally mean that the Server is there too but the application development team may not be responsible to manage the infrastructure components as mentioned below.

1. Auto Scaling
2. Load balancing
3. Network security (Shared Model)
4. Backups

All the points listed above are certain configurations, which should be managed by the infra team if we are working on product development. And if we get all these out of the box from a cloud provider without any additional manpower and time, we may opt for it. That's where Serverless comes forward. And cloud provider gives you their platform and you deploy your application and manage the business logic.

In nutshell, This is what Serverless is

BAAS

Backend as a service.

When we are working on a distributed development. We have our component of application working on one server and another one working on some other server and they both interact via different API connectivity methods.

Now, there are some general functionalities that every product needs.

1. Authentication
2. Analytics
3. Notification

These are some of the common functionalities that most products need. So what cloud providers are doing is, there are exposing these functionalities ready-made via some additional configuration so that the application team can follow the real-world development and can have a quick release to market.

Examples- Google Firebase, AWS Amplify, Parse, Sentry

Benefits

1. Reduces time to market
2. Reduces development effort
3. Can be integrated into a serverless or non-serverless module.

FAAS

Function As A Service

We normally relate Serverless to cloud functions that are normally cloud native. There are different types of services being provided by different cloud providers to deploy normal functions on cloud service. I have listed a few of the popular cloud providers for Function

Examples — AWS Lambda, Azure Functions, Google cloud functions

Benefits

1. Quick to release as an MVP
2. Reduces App size
3. Less overhead in managing the server

Cons

1. Have some execution limit of 10–15 minutes depending on cloud providers.
2. Have certain challenges on cold start
3. Vendor lock in- Depends if you have a written function in such language/framework that's not supported by any other cloud provider.
4. Serverless functions are better until a certain limit because there is a concurrency challenge also that doesn't permit one to run a lot of lambda functions in parallel.
5. Increased latency because of cold start and increasing running time and cost of lambda as well.

Conclusion

So, to sum up, Both terms i.e BAAS and FAAS can be marked under the Serverless umbrella as more or less they relate to common benefits and use cases.

Serverless -BAAS or FAAS or Both was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

1. Creating Dart as a backend reduced my time to market as there was no need to learn extra technology to create different stuff for my app.
2. And its community is growing and it offers a wide range of features and performance is also enhanced.

Please refer to my latest article on how to create a Dart-based backend server at the below link.

https://digitalneeds.medium.com/create-a-backend-server-with-apis-in-dart-1fc6e897ba33

Creating Backend Servers In Dart was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

Grpc vs Rest is a kind of choice we have to make whenever we are exposing two distributed web components in such a way that they both can talk to each other to fetch data.

Hi Readers, REST has been around for more than a while now or I should say for about more than 2 decades which says that REST is now a kind of the most trusted architecture style with which two web components can expose their data to each other via application layer i.e. HTTP layer. With REST, one of our services can call other services via an HTTP client and get data from other clients. The following are some of the salient features which are the most favorable and deciding factors for REST to implement.

1. Rest is an architectural style where there are specific methods defined for fetching, editing, and creating or updating a resource. This resource can be an entity so REST mostly deals with updating data for a certain entity in our application.
2. REST works on different methods like GET for fetching an entity, POST for creating a new entity, and PUT for updating entity and PATCH for updating certain attributes for a resource and DELETE for deleting a resource.
3. REST mostly deals with a noun or an entity so whenever we are sending a request over REST-style we tend to deal with a specific resource.

Now I will talk about what is GRPC and then I will compare them with a use case which might help you understand in a better way.

1. GRPC works by calling a specific method on server side from client layer with a schema layer defined by protobuf file( which is a model layer for request and response and create stubs for sending and receiving service)
2. In GRPC, client calls GRPC client which internally calls service method via a stub which is created over a channel.
3. In GRPC, data is exposed over HTTP protocol only but the only difference being it uses HTTP2 protocol which communication between client and service not only synchronous but gives additional options like Client or server streaming and bi-directional streaming along with request cancellation and request timeout handling.
4. In GRPC, we basically deal with a verb for a specific entity. For example If one has to add a new order, we will send a request as /api/order?orderId=1 along with POST object but in case of GRPC, we just need to call method createOrder along with POST request.
5. In GRPC, One does not want to expose the functionality to any other service outside our cluster or don't want to invoke the functionality by any web client.

If we talk about how the data send and receive formats for two protocols so there is not much of a difference. The main difference lies in the architectural style along with additional capabilities that one provides over the other so that basically tells you which one should be used.

Below are a few scenarios where you will use REST.

1. If you want to expose a service data over the network data for multiple services or clients then we should definitely use REST as it exposes a specific URL with a domain model layer.
2. If we are more concerned about fetching, editing, creating, delete a specific resource then we should use REST.
3. If we want our caching strategy to be in place along with the architectural method then we should use REST as it has caching mechanism inbuilt in a few methods according to the need.

Below are a few scenarios where one should use GRPC over REST.

1. If we want to expose a document service internal to our cluster services like microservices then we should definitely prefer GRPC.
2. If we want our response to be fast, streaming, and client and server streaming then we should prefer GRPC.
3. If we are not much concerned about entity retrieval but only concerned about functionality under the hood.

So these are the few basic differences between REST and GRPC. GRPC’s are preferred in microservices-based architecture in today’s world.

I hope the article was clear and might help you in future system design processes when you need to decide between the two.

Thanks for reading the article. Please reach out to me in case of any help required in designing or developing any kind of digital product in web or mobile apps by dropping an email at digitalneeds.tech@gmail.com

Grpc vs Rest Protocol was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

I encountered an issue for which I thought of writing an article and sharing it so that if anyone encounters a problem as I had so they might not waste their time. So this article is created with an intention to reduce the developer time when using firebase storage for the web app with flutter.

When we are using firebase storage to store different files on firebase cloud it works pretty well with firebase_storage plugin but somehow it's not fully supported in the flutter web edition. So in the web, we need to use a different plugin along with an option to pick an image from mobile phone or desktop.

So I will list down first which all plugins we should use to upload a file on to firebase storage which does have complete support for flutter web.

1. image_picker_web
2. firebase

So these are two plugins that are required to pick and upload a file on firebase storage in the flutter web.

Image_picker_web:- Image picker web is an alternative to image_picker plugin for picking up the files from your local machine whether its mobile phone or desktop on the web.

firebase:- firebase is a plugin that has almost all capabilities of firebase services in web-like firebase storage, authentication, firestore along other prime services offered by a firebase cloud solution.

So I will add two files one for picking up the file by using image_picker_web plugin and the other one for uploading the file on firebase storage as below.

Explanation:- As you can see in the above code that I have written a function where you are using image picker plugin and getting imageInfo Object which contains image object details and those are stored in a variable and we are setting the state once we have the imageInfo object.

Below is the code to upload the file to firebase storage.

As you can see in the above code this function is used to upload the image on firebase storage with the same object that we got in the previous file and we are using fb.app().storage() function to refer to firebase storage for your particular project.

So this is a very simple way of uploading the files on the web and getting the URL of the uploaded file.

Please let me know if you have any doubt in understanding the concept or you need any help in designing your web or app product, please consult by dropping in your comment or reach out to me at digitalneeds.tech@gmail.com or tech-support@digitalneeds.co.in

Firebase storage in flutter web was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.

So first of all I will start with what is flutter and why its quite useful for developing native mobile web or mobile apps. Flutter is based on dart language which is newly designed and developed and its used in angular as well earlier. With flutter, you can reduce your lot of boiler plate code which is used to be done in any other android or any other mobile platform app development. With flutter you just need to plugin the different widgets or components(in terms of angular) and combine them under one view and render the view page. There is no need to define anything specifically for using different widgets and all.

There are different kinds of widgets being already developed in flutter framework which basically makes work quite easier for a developer by reducing the go to deployment time for a developer which is quite important and that time can be invested in new innovation thinking points which will enable them better native apps.

Coming to what are widgets and will explain you some of the important widgets.

Material

Material is quite basic widget which is used in flutter framework being used for developing apps based on material design developed under google. For this you need not to add any extra css file because every layout management part is handled in dart file itself which makes it super simple for a guy who doesnot know much about css and designing. So inside Material widget you can use various different widgets which will enable to make UI look very simple and very stylish.

Scaffold

Then next we have a scaffold widget which is basically a wrapper of you entire page on app where you can add different basic parts on the page with very much ease. Like if you want to add AppBar content or footer context menu.Then its quite easier by using this widget. It can be used under any of the parent designing widget which can be material as well.

Container

Container is basically as the name suggests is a widget which is like a box in which you can add different kinds of widgets and content like text, column, row or card or any other widget you intend to.

Row & Column

Next are row and column widget. These are two different widgets basically used for purpose of aligning the content in a row or column format respectively. Its almost similar feature like in css flexbox serves which aligns the divs with column or row as flex direction so here these two widgets serve the similar purpose. They have different properties listed for them which basically aligns the content in center, sideways or in any of the responsive formats.

Stack

Stack is also one of the majorly used widget in apps development. As the name is suggesting stack widget allows multiple widgets to be stacked upon one another and aligning them by using different properties related to the widget. Stack is a kind of layered architecture where you can put different widgets in layer manner on top of one another.

Padding

Padding is another quite simple widget which is basically used to put some content inside by adding padding or spacing between borders to align the content perfectly. Its almost similar to container with one of the differences being that padding widget automatically can get padding content by using this particular widget.

Text

Text is a pretty simple widget which is used to add text in specific place and you can add particular styling to the text inside this specific widget only and can be plugged in any of the widgets.

Center

Center is used to align the content or widgets present inside it in center of the page or get aligned to central axis.

InkWell

Inkwell is a click handler widget which basically handles the click event on any of the widget which basically navigates to different page.So inkwell gives a special kind of ripple effect on click event of a specific widget. And this behaves similar as GestureDetector which is also mostly used for detcting any kind of gesture on screen including click event.

ClipRreact

This widget is used to show kind of cutting or specific curl behaviour in the design. Like in some apps you need to define the background color in such a way that you need to cover 20% or sometimes specific areas of screen. So Cliprreact is quite useful for those tasks.

Expanded

Expanded is a very simple widget which basically aligns the content inside it properly and gives the child widgets the proper spacing it needs and can expand the specific widget if more space available by using flex property inside it which is similar to flexbox property.

So This was the list of some basic widgets. There are different properties provided inside these widgets to align and show the content in proper manner on the screen.

I would list down some more as well on this page in coming days and would write new articles on explaining properties related to different widgets and problems being encountered while using different widgets and would be taking up a specific design and breaking down in different widgets and explaining that with which all widgets we can design the screen.

For any clarifications or guidance around this topic. Please drop me an email at digitalneeds.tech@gmail.com or comment below.

Flutter Basic Widgets guide was originally published in digital tech articles on Medium, where people are continuing the conversation by highlighting and responding to this story.