DEV Community: Adam Laszlo

Deconstructing a Production-Ready AI Agent: A Beginner's Guide - Part 3

Adam Laszlo — Tue, 25 Nov 2025 14:00:00 +0000

The Secure Factory — Deploying and Operating with Amazon Bedrock AgentCore

3.1 Introduction: The "Prototype vs. Production" Problem

In Part 1, we built a secure frontend (React + Cognito + API Gateway). In Part 2, we designed the agent's logic—its "blueprint"—using the Strands SDK. We now have a "prototype" that likely runs on a developer's laptop with a python main.py command.

Why can't we just put this Python code on a server (like an Amazon EC2 instance), point our API Gateway at it, and call it "done"?

This is the "Prototype vs. Production" gap. As the airline agent example states, "Building a chatbot is easy. Building a production-grade AI agent that can perform real-world tasks securely and at scale is incredibly hard". To go to production, we must first solve a "mountain of engineering" , which includes:

Security: How do we isolate one user's agent session from another? An agent is "stochastic" (unpredictable); what if one user's agent "goes rogue" and tries to access another user's data?
Scalability: What happens when 10,000 users access the agent simultaneously? We would need to manage complex container orchestration, load balancing, and auto-scaling.
State Management: An agent needs "memory" to be useful. A standard serverless function (like AWS Lambda) is stateless and has a 15-minute timeout. What if our "stock analyzer" agent needs 30 minutes to generate a complex report, or needs to wait for user input?
Observability: Because the agent's "model-driven" logic is not deterministic, how do we trace its decisions to debug a problem or audit its "chain of thought"?

3.2 The "Where" vs. the "What": Clarifying the AWS AI Ecosystem

For a beginner, the AWS ecosystem can be confusing. "Amazon Bedrock," "Amazon Bedrock Agents," and "Amazon Bedrock AgentCore" sound similar. The following table clarifies the separation of concerns. These components are not competitors; they are layers of a complete stack.

Component	Role in the System	Analogy (The Robot Factory)
Strands Agents (SDK)	The "Blueprint" (The Logic)	The engineering blueprints and instructions that define how the robot should think, what tools it can use, and what its job is.
Amazon Bedrock (Model Runtime)	The "Brain" (The LLM)	The off-the-shelf, advanced AI brain (e.g., Claude 4, Llama 3) that is leased. It provides the raw intelligence and reasoning power for the agent.
Amazon Bedrock AgentCore	The "Secure Factory" (The Platform)	The fully-managed, secure, and scalable platform that takes the "Blueprint," installs the "Brain," and builds and runs the robot in its own private, secure room.

3.3 Deep Dive: Why AgentCore is the Game-Changer for Production Agents

Bedrock AgentCore provides specific, purpose-built features that solve the production-scaling problems identified in section 3.1.

Key Feature #1: Deterministic Security with microVM Isolation

This is AgentCore's most important security feature. It does not use standard, shared-kernel containers. Instead, AgentCore provides "complete microVM isolation".

This means every single user session gets its own dedicated virtual machine with its own isolated compute, memory, and file system. The "why" for this is critical: an agent's behavior is stochastic. A developer cannot perfectly predict what it will do. By giving each agent session its own dedicated "room" (a microVM), AgentCore ensures that even if an agent "goes wild" or is compromised, it is impossible for it to see or affect another user's data or session. This creates a "deterministic security boundary". When the session terminates (either by choice, after 15 minutes of inactivity, or after a maximum of 8 hours), the entire microVM is terminated and its memory is sanitized. This is non-negotiable for enterprise-grade security.

Key Feature #2: Stateful, Long-Running Workflows

Unlike standard serverless functions (like AWS Lambda) which are stateless and time out after 15 minutes, AgentCore provides "persistent execution environments" that can last for up to 8 hours.

This feature is what enables a new class of complex, stateful agentic workflows. The agent's memory and local file system persist across multiple turns of a conversation. A task like the "airline customer agent" or the "stock analyzer" might need to run for 20 minutes to fetch data and generate a PDF report, or wait 10 minutes for a user to confirm a rebooking. This is impossible in a traditional serverless model but is a native feature of AgentCore.

Key Feature #3: The Fully-Managed "Extras"

AgentCore is a complete platform, not just a runtime. It provides the "entire mountain, pre-built" by integrating other managed services:

AgentCore Memory: Managed, persistent short-term and long-term memory for agents.
AgentCore Gateway: A service to securely turn existing internal company APIs into "tools" for the agent.
Observability: Comprehensive dashboards, logs, and tracing (via Amazon CloudWatch and OpenTelemetry) to trace, debug, and audit the agent's decisions.
Identity: Native integration with identity providers to handle the complex "delegated access" and M2M authentication discussed in Part 1.

3.4 Putting It All Together: The szakdolgozat Final Architecture (End-to-End)

We can now trace a single user request from start to finish, connecting all three parts of this series.

[User] opens the **** (Part 1).
The user logs in. **** authenticates them and provides a JWT (Part 1 / ).
he user sends a prompt: "Analyze SIM_STOCK." The React app POSTs this prompt to [API Gateway], attaching the JWT in the Authorization header (Part 1 / ).
[API Gateway] uses its [Cognito Authorizer] to validate the JWT. The user is now authenticated and authorized (Part 1 / ).
API Gateway forwards the allowed request to the **** endpoint.
[AgentCore] receives the request and, in a critical step, spins up a new, dedicated microVM for this specific user's session (Part 3 / ).
AgentCore loads the **** "blueprint" (the szakdolgozat Python code) into this microVM (Part 2 / ).
The AgentCore platform also injects a unique **** into the microVM, granting the agent itself M2M permissions (e.g., permission to call Bedrock models) (Part 3 / ).
The **** event loop (Part 2 / ) begins. It uses the **** (the "brain") to reason. The LLM plans: "I need to call the gather_stock_data tool."
Strands executes the @tool function, which might call an external financial API.
This "Reason-Act" loop continues until a final report is generated.
The final response is streamed back through API Gateway to the React app. The microVM remains active (for up to 8 hours), "remembering" this entire interaction, so the user's follow-up prompt ("Now compare it to AAPL") will have full context (Part 3 / ).

This end-to-end flow reveals the architecture's true sophistication: a Dual-Layer Security Model.

Layer 1: User-to-Agent Security: Handled by Cognito and API Gateway. This layer answers the question: "Is this human allowed to talk to my agent?".
Layer 2: Agent-to-Service Security: Handled by AgentCore's microVM isolation and the agent's unique IAM Role. This layer answers the questions: "Is this agent allowed to access this S3 bucket?" and "Can I guarantee it can't access another user's S3 bucket?".

This dual-layer, defense-in-depth model provides the "enterprise-grade security" that separates a mere prototype from a true production-ready application.

3.5 Series Conclusion: From GitHub Repo to Scalable, Secure AI Application

This three-part journey has deconstructed the ladam2000/szakdolgozat repository, revealing it to be a complete, end-to-end blueprint for the next generation of AI-native applications.

In Part 1, we built the "face" (React) and the secure "front door" (Cognito + API Gateway), creating a secure, authenticated client.
In Part 2, we defined the "brain" (Strands), using a model-driven framework to create a powerful, flexible "blueprint" for our agent's logic.
In Part 3, we deployed this "blueprint" into the "secure factory" (AgentCore), giving it a stateful, isolated, and scalable "body" to live in.

The final architecture is more than just a collection of services; it is a thoughtful, secure, and robust system designed to solve the real-world challenges of running autonomous AI agents at scale.

Deconstructing a Production-Ready AI Agent: A Beginner's Guide - Part 2

Adam Laszlo — Tue, 18 Nov 2025 14:00:00 +0000

The Agent's Brain — Defining Logic and Reasoning with Strands Agents

2.1 Introduction: The "Blueprint" vs. the "Factory"

In Part 1, we established a secure "front door." A user can now authenticate and send a prompt to a secure API endpoint. The next question is: what handles that prompt?

This brings us to the most confusing—and most important—distinction in this modern AWS stack: the difference between Strands Agents and Amazon Bedrock AgentCore.

To clarify this, one can use an analogy of building a sophisticated robot:

Strands Agents (SDK): This is the "Blueprint." It is an open-source Python framework that a developer uses to define the robot's logic. This is the code for its "brain" (how it reasons), its "memory" (how it remembers), and the "tools" it can use (its "hands"). Strands is the what.
Amazon Bedrock AgentCore: This is the "Secure Factory." It is the fully-managed AWS platform where the "Blueprint" is deployed. AgentCore is the "where." It takes the Strands code and handles what one article calls the "mountain of engineering" required to run it securely and at scale: the servers, the security, the isolation, and the monitoring.

Amazon Bedrock AgentCore is framework-agnostic. This means the "Secure Factory" doesn't care which blueprint is used. The szakdolgozat repository chooses to deploy a Strands agent, but AgentCore could just as easily run an agent built with LangGraph or CrewAI.

This part of the series focuses only on the "Blueprint": the Strands Agents SDK.

2.2 What is Strands Agents? The Open-Source "Blueprint"

Strands Agents is an open-source framework, developed by AWS, for building production-ready, multi-agent AI systems. Its core philosophy is "Model-Driven Orchestration".

This philosophy marks a significant departure from traditional programming:

Traditional Code (Imperative): A developer writes explicit if/then logic. "If the user says 'stock report', then call function_A, then function_B, then function_C." This is rigid and breaks easily.

Strands (Declarative): A developer does not write this explicit if/then logic. Instead, they simply:

Define a Prompt: (e.g., "You are an expert financial assistant.")
Define a List of Tools: (e.g., "Here are the tools you can use: gather_stock_data, analyze_stock_performance.")

Strands then leverages the Large Language Model (LLM) itself as the orchestrator. It uses the model's advanced reasoning capabilities to plan the necessary steps, chain its thoughts, call the tools it needs, and reflect on the results to achieve the user's goal. This is the essence of "agentic" behavior.

2.3 Inside the szakdolgozat Repo: How Strands Builds the "Brain"

When the user's prompt (e.g., "Analyze SIM_STOCK") arrives from API Gateway, the Strands agent logic kicks in via its "Event Loop". This is a "Reason-Act" (ReAct) cycle:

The user's prompt enters the Strands event loop.
Strands passes the prompt, the conversation history (memory), and the list of available tools to the LLM (e.g., Claude 4, as in ).
**** The LLM "thinks out loud" (a process of reasoning) and determines its plan: "To fulfill this complex request, I must first call the gather_stock_data tool."
[Act] Strands detects this "tool use" request from the LLM, validates it, and executes the corresponding Python function.
The tool returns its data (e.g., a JSON blob of stock information). Strands adds this result to the ongoing conversation history.
Strands "loops," sending the new context (including the stock data) back to the LLM.
**** The LLM re-evaluates: "Great. I have the data. Now I must call the analyze_stock_performance tool to make sense of it."
[Act] Strands executes the analyze_stock_performance tool.
This "Reason-Act" loop continues, with the LLM planning and Strands executing, until the LLM decides the task is fully complete and generates a final, comprehensive answer for the user.

Giving Your Agent "Hands" with the @tool Decorator

The practical magic of Strands lies in its simplicity. To give the agent "hands" (tools), a developer doesn't need to write complex API handlers. notes: "Any Python function can be easily used as a tool with the Strands @tool decorator."

A hypothetical example from the szakdolgozat repository's logic might look this simple:

from strands.tools import tool
import financial_api

@tool
def gather_stock_data(ticker_symbol: str) -> dict:
    """
    Fetches comprehensive stock data for a given ticker symbol, 
    including price, volume, and market cap.
    """
    #... code to call a financial API...
    data = financial_api.get_data(ticker_symbol)
    return data

By simply adding the @tool decorator, this Python function and its docstring (which is critical for the LLM to understand what the tool does) are now part of the "tool-belt" that Strands will show to the LLM during its reasoning step.

Strands is also designed for more complex systems, offering simple primitives for :

Handoffs: Passing a task from one specialized agent to another (e.g., from a "Data Gathering Agent" to a "Report Writing Agent").
Swarms: A group of agents working in concert.
Agent-as-Tool (A2A): One agent can be used as a "tool" by a parent agent.

2.4 Strands vs. LangGraph: A Quick Aside

The research material also provides examples of agents built with LangGraph. Both Strands and LangGraph are powerful frameworks for building agents, and both can be deployed on Bedrock AgentCore. The key difference lies in their philosophy:

LangGraph: Requires the developer to explicitly define a graph (a flowchart). The developer defines the nodes (agents or tools) and the edges (the path the logic must follow). This is more structured and deterministic.
Strands: (In its simplest form) is more dynamic. It is "model-driven". The developer does not define the explicit path; they trust the LLM's reasoning to create the path on the

The szakdolgozat repository's choice of Strands is a specific design decision. It represents a bet on the increasing power of LLM reasoning, trading the rigid control of a pre-defined graph for the dynamic flexibility of a model-driven orchestrator.

We now have a complete "blueprint" for our agent. We have used the Strands SDK to define what our agent is, how it thinks, and what tools it can use. This gives us a set of Python files—a "prototype." But this code cannot run itself, and it is not yet secure or scalable. In Part 3, we will take this "blueprint" and move it into the "secure factory" (AgentCore) to bring it to life.

Deconstructing a Production-Ready AI Agent: A Beginner's Guide - Part 1

Adam Laszlo — Tue, 11 Nov 2025 17:25:08 +0000

The Secure Frontend — Building the "Face" and "Front Door" of Your AI Agent

1.1 Introduction: The Anatomy of an AI-Native Application

The term "AI agent" has rapidly entered the developer lexicon, often used interchangeably with "chatbot." However, this comparison is insufficient. A chatbot typically follows a predefined script, answering questions or performing simple tasks. An AI agent, in contrast, is a more sophisticated system. It is designed to perceive its environment, plan a multi-step course of action, reason about its choices, and act autonomously by using a set of available tools. The ultimate goal of an agent is not just to provide information, but to automate complex, end-to-end tasks with minimal human intervention.

This series will deconstruct a complete, end-to-end example of such a system: the ladam2000/szakdolgozat repository. We will analyze this project not as a single, monolithic "AI app," but as a modern, distributed system composed of three distinct, interacting layers:

The Experience Layer: The frontend where the user interacts. (Part 1)
The Logic Layer: The agent's "brain," which defines its capabilities and reasoning. (Part 2)
The Runtime Layer: The secure, scalable platform where the agent "lives" and executes. (Part 3)

This architecture represents a fundamental paradigm shift toward "Agent-Native Applications." In this model, the AI agent is not a feature bolted onto an existing application; the agent is the application's core value. The "business logic" is no longer defined by rigid if/then statements, but by a set of goals and tools provided to an autonomous reasoning engine. The frontend, as we will see, becomes a "cockpit" for directing this new, intelligent backend.

1.2 Deconstructing the Frontend: The User's Cockpit

The frontend's primary role is to provide this "experience layer." In a modern stack like that seen in the szakdolgozat repository, this is almost certainly a React application.

The user interface (UI) itself often resembles a familiar chat window. The core logic for this involves two key React elements:

State Management: Using React hooks like useState to manage the user's current query and the agent's answer or streaming response.
Asynchronous Function: An async function, such as askAgent or handleSend, that is triggered when the user clicks a "Send" button.

The most critical function of the frontend, however, is securely connecting to the agent's "brain." This must be done in a specific, secure manner. A common mistake for beginners is attempting to call AI services (like OpenAI or Amazon Bedrock) directly from the React code. This approach is critically flawed as it exposes secret API keys in the client-side code, visible to anyone.

The correct and secure architecture, as employed in this type of application, is as follows:

The React app makes a standard fetch or axios POST request to a single, secure backend API endpoint (e.g., /api/agent).
The body of this request is a simple JSON object containing the user's message, for example: {"message": "query"}.

This frontend architecture may appear overly simple, but this simplicity is a deliberate and powerful design choice. The frontend acts as a "dumb terminal," a concept that mirrors the "Backend for Frontend" (BFF) pattern, but "supercharged" for AI. The frontend is intentionally decoupled from the agent's operational complexity. It does not know—and does not need to know—if the backend is a simple Python script, a complex multi-agent "swarm" , or a stateful, long-running microVM. This abstraction is the key to a scalable and maintainable system, as it allows the backend agent logic to be re-architected, scaled, or even completely swapped (e.g., from LangGraph to Strands) with zero changes to the frontend client.

1.3 The "Auth" in Authentication: Why Your Agent Needs an Identity

Before a user can interact with the agent, the system must establish trust. This involves two distinct concepts:

Authentication (AuthN): Verifying "Who are you?" This is the process of proving an identity.
Authorization (AuthZ): Determining "What are you allowed to do?" This is the process of verifying permissions.

In a traditional web application, the system typically only needs to authenticate the user. Agentic systems, however, introduce a more complex security challenge: the management of two distinct identities and the secure link between them.

The User Identity: This is the human logging into the React application. The system must verify who they are (AuthN) and what they are allowed to ask the agent to do (AuthZ).
The Agent Identity: The agent itself is a non-human, machine entity. When it needs to perform a real-world action (e.g., "generate a stock report and save it to S3," as seen in a similar stock-analyzer agent ), it must authenticate itself to the S3 service (AuthN) and prove it has permission to write to that specific S3 bucket (AuthZ). This is a "machine-to-machine" (M2M) flow.

The application's primary security challenge, therefore, is managing "Delegated Access". The user (a human) is delegating their authority to the agent (a machine). A compromised or misbehaving agent can "actively harm your systems". The entire architecture of the szakdolgozat project is built around securely binding the User Identity to the Agent Identity for a limited, auditable, and secure session.

1.4 Practical Implementation: Securing the Application with AWS Cognito

To manage these identities, the szakdolgozat architecture leverages dedicated AWS services.

The "Front Door" (User Identity)
The User Identity is managed by Amazon Cognito. This service provides a complete solution for user sign-up, sign-in, and access control. The flow works as follows:

The ****, likely using the AWS Amplify library , displays a sign-up or sign-in form.
The user enters their credentials, which are sent directly to [Amazon Cognito].
Cognito authenticates the user and returns a set of JSON Web Tokens (JWTs)—specifically, an ID Token (proving identity) and an Access Token (proving permissions).
The React app securely stores these tokens to manage the user's session.

The "Bouncer" (User Authorization)
Now, this user identity must be linked to the API request. This is where Amazon API Gateway comes in, acting as the secure "bouncer" for the agent backend.

When the user sends a prompt, the React app retrieves the JWT Access Token and attaches it to the API request in the Authorization header (e.g., headers: { 'Authorization': \Bearer ${accessToken}}).
This POST request is sent not to the agent's server directly, but to the [Amazon API Gateway] endpoint.
This API Gateway is configured with a "Cognito User Pool Authorizer".
This authorizer automatically intercepts the incoming request. Before the agent's code is ever executed, API Gateway inspects the Authorization header, validates the JWT with Cognito, and confirms that the user is valid and has the correct permissions (scopes) for this action.
If the token is invalid, expired, or missing, API Gateway blocks the request with a 401 Unauthorized error, and the agent logic never even runs.

This architecture masterfully decouples user authentication from agent logic. A junior developer might be tempted to validate the JWT inside the agent's Python code. This is an insecure and inefficient practice. By using an API Gateway Authorizer, the szakdolgozat architecture makes the agent logic simpler and far more secure. The agent code, when it finally runs, operates with a guarantee that the request has already been authenticated and authorized. It can focus on its core task—agentic reasoning—instead of boilerplate security checks.

We have now successfully built the "face" (React) and the secure "front door" (Cognito + API Gateway). A validated user can now send a secure prompt. In Part 2, we will explore what lies behind that door: the agent's "brain."

How to Choose a Compute Option on AWS

Adam Laszlo — Mon, 29 May 2023 22:00:00 +0000

One of the most important decisions that you have to make when building your applications on AWS is how to run your code. AWS offers a variety of compute options that cater to different needs and preferences. But how do you choose the right one for your use case?

In this blog post, we will explore the three main types of compute options on AWS: virtual machines, container services, and serverless functions. We will compare their features, benefits, drawbacks, and scenarios where they are most suitable.

Virtual Machines

A virtual machine (VM) is a software emulation of a physical computer that runs an operating system and applications. On AWS, you can use Amazon Elastic Compute Cloud (EC2) to launch VMs with different configurations of CPU, memory, storage, network, and security.

Some of the advantages of using VMs are:

You have full control over your infrastructure and can customize it according to your needs
You can leverage existing skills and tools that work with VMs
You can run any type of application or workload that requires specific operating systems or software packages

Some of the drawbacks of using VMs are:

You have to manage the underlying infrastructure such as patching, scaling, backup, etc.
You have to pay for the resources that you provision even if they are not fully utilized
You have to deal with potential performance issues such as boot time or latency

Some scenarios where VMs are ideal are:

You need to run legacy applications that cannot be easily migrated or refactored
You need to run complex or specialized workloads that require fine-grained control over your environment
You need to comply with certain regulations or standards that require specific configurations or settings

Container Services

A container is a lightweight package of code and dependencies that can run anywhere without requiring a separate operating system. On AWS,
you can use Amazon Elastic Container Service (ECS)^[2]^ or Amazon Elastic Kubernetes Service (EKS)
to run your containers on clusters of EC2 instances or on AWS Fargate^[3]^ , which is a serverless platform for containers.

Some of the advantages of using container services are:

You can improve portability and consistency across different environments by packaging your code and dependencies together
You can increase efficiency and utilization by running multiple containers on the same host without wasting resources
You can simplify deployment and scaling by using orchestration tools such as ECS or EKS

Some of the drawbacks of using container services are:

You still have to manage some aspects of your infrastructure such as networking, security groups, load balancing, etc.
You still have to pay for the resources that you provision even if they are not fully utilized unless you use Fargate
You still have to deal with potential performance issues such as cold starts or latency

Some scenarios where container services are ideal are:

You want to adopt a microservices architecture where each service runs in its own container
You want to leverage existing skills and tools that work with containers such as Docker or Kubernetes
You want to migrate existing applications from other platforms without changing much code

Serverless Functions

A serverless function is a piece of code that runs in response to an event such as an HTTP request, a message from a queue, a change in a database.

On AWS, you can use AWS Lambda to run your serverless functions without provisioning any servers.

Some of the advantages of using serverless functions are:

You only pay for the resources that you consume when your code runs
You don’t have to worry about managing any infrastructure such as scaling, patching, backup, etc.
You can easily integrate with other AWS services and third-party APIs

Some of the drawbacks of using serverless functions are:

You have to adhere to certain limitations such as memory size, execution time, concurrency.
You have to deal with potential performance issues such as cold starts or latency
You have to refactor your code to fit into small and stateless functions

Some scenarios where serverless functions are ideal are:

You want to build event-driven applications that react to changes in data or user behavior
You want to create simple and scalable APIs or microservices that don’t require complex logic or state
You want to perform short-lived and asynchronous tasks such as image processing or data transformation

Conclusion

As you can see, there is no one-size-fits-all solution when it comes to choosing a compute option on AWS. Each option has its own pros and cons, and you have to consider factors such as cost, performance, scalability, flexibility, and complexity.
The best way to decide is to understand your requirements and use cases, and then experiment with different options until you find the one that works best for you. If you need help with choosing a compute option on AWS, you can use AWS Compute Optimizer, which is a service that recommends optimal AWS compute resources for your workloads based on performance and cost data.
Please let us know if you have any feedback or suggestions.
Thank you for reading. 😊

Why I Chose A Cloud Guru for AWS Training

Adam Laszlo — Tue, 09 May 2023 22:00:00 +0000

As a cloud enthusiast, I wanted to learn more about AWS and get certified as an AWS professional. There are many online platforms that offer AWS training and certification courses, but I decided to go with A Cloud Guru for several reasons.

First of all, A Cloud Guru has a comprehensive course library that covers all the topics and skills needed for AWS certifications. Whether you are a beginner or an expert, you can find courses that suit your level and goals. You can also access hands-on labs that let you practice your skills in real AWS environments.

Secondly, A Cloud Guru has a flexible and affordable pricing plan that allows you to access unlimited courses and labs for a monthly or annual fee. You can also get discounts if you buy multiple licenses for your team or organization. Compared to other platforms that charge per course or per exam, A Cloud Guru offers more value for money.

Thirdly, A Cloud Guru has a supportive and engaging community of learners and instructors. You can join forums, webinars, podcasts, events, and challenges to interact with other cloud enthusiasts and experts. You can also get feedback, tips, and guidance from experienced instructors who are passionate about teaching.

Of course, A Cloud Guru is not the only option for AWS training. There are other alternatives such as Traineaze, Cloud Academy and TalentCards. These platforms have their own strengths and weaknesses, such as different course offerings, pricing models, learning formats, etc.

However, after comparing these alternatives with A Cloud Guru, I found that A Cloud Guru best suited my needs and preferences. That's why I chose A Cloud Guru for AWS training and certification.

If you are interested in learning more about A Cloud Guru or signing up for their courses, you can visit their website.

Please let me know if you have any feedback or suggestions.

How to Start Learning AWS: The First Step

Adam Laszlo — Wed, 12 Apr 2023 22:00:00 +0000

If you are interested in cloud computing and want to learn AWS, you might be wondering where to start. AWS is a huge platform that offers hundreds of services and features for various use cases and scenarios. It can be overwhelming to navigate through all the options and decide what to learn first.

The good news is that there is no one right answer. Depending on your background, goals, and interests, you can choose different paths and resources to start learning AWS. However, there is one common step that everyone should take before diving into any specific service or topic: setting up your AWS account and environment.

Why is this important? Because having your own AWS account and environment will allow you to:

Access all the services and features that AWS offers
Experiment with different configurations and settings
Practice your skills with hands-on exercises and projects
Monitor your usage and costs
Learn best practices for security and governance

How do you set up your AWS account and environment? Here are some basic steps:

Create an AWS account. You will need an email address, a phone number, a credit card, and an identity verification process. You can use the free tier for 12 months to access some services without charge.
Create an IAM user. IAM stands for Identity and Access Management. It allows you to create users, groups, roles, policies, etc., that control who can access what resources in your account. You should create an IAM user for yourself with administrator permissions instead of using the root user, which has full access to everything.
Set up a billing alarm. This will notify you if your monthly charges exceed a certain amount that you specify. This way, you can avoid unexpected bills or fees if you use more resources than expected.
Choose a region. A region is a geographical area where AWS has data centers that host its services. You can choose any region that suits your needs based on factors such as latency, availability, cost, etc.
Install the AWS CLI. The CLI stands for Command Line Interface. It allows you to interact with AWS services using commands in your terminal or shell instead of using the web console, which is the graphical user interface (GUI) for AWS.
Install an IDE. An IDE stands for Integrated Development Environment. It is a software application that helps you write code faster and easier by providing features such as syntax highlighting, code completion, debugging tools.

Congratulations! You have completed the first step to learn AWS: setting up your account and environment.

What's next? Now that you have your own sandbox to play with AWS services and features,
you can start exploring different topics based on your interests.

Some popular topics include:

Compute: Learn how to run applications on servers or serverless platforms using services such as EC2, Lambda, ECS
Storage: Learn how to store data on different types of storage systems such as S3⁴, EBS⁴, EFS, Glacier
Database: Learn how to work with relational or non-relational databases using services such as RDS, DynamoDB, Aurora
Networking: Learn how to connect different resources within or across regions using services such as VPC, ELB, Route 53
Security: Learn how to protect your data and resources from unauthorized access or attacks using services such as KMS, WAF, Shield

Of course, these are just some examples of topics that you can learn about AWS.

There are many more topics that cover various aspects of cloud computing such as analytics,
machine learning, devops, etc.

You can find many online courses, tutorials, books, podcasts, blogs, that offer quality content on these topics.

Some recommended resources include:

Udemy: A popular online learning platform that offers hundreds of courses on various topics related to AWS certifications and skills at affordable prices and flexible schedules
A Cloud Guru: A leading cloud learning platform that offers comprehensive courses, labs, exams, and community events on all things cloud including AWS certifications and skills at a monthly or annual fee.
AWS Training and Certification: The official source of AWS learning content that offers free digital training, paid classroom training, and certification exams on various AWS topics and roles
FreeCodeCamp: A nonprofit organization that offers free online courses, articles, podcasts, and projects on various web development and cloud computing topics including AWS

These are just some of the many resources that you can use to start learning AWS.

The important thing is to find what works best for you and stick with it.

Remember, learning AWS is a journey, not a destination. You will always find new things to learn and improve as you go along. So, don't be afraid to take the first step and set up your account and environment.

Please let me know if you have any feedback or suggestions.

Why I Chose to Focus on AWS While I Am Studying for My Bachelor Degree in Software Engineering

Adam Laszlo — Tue, 28 Feb 2023 11:12:21 +0000

Why I Chose to Focus on AWS and Cloud Technologies While I Am Studying for My Bachelor Degree in Software Engineering at Obuda University?

Hello everyone, welcome to my blog. Today I want to share with you my story of how and why I decided to focus on AWS and cloud technologies while I am studying for my bachelor degree in software engineering at Obuda University.

First of all, let me introduce myself. My name is Adam Laszlo and I am a third-year student at Obuda University, which is one of the oldest and largest universities of applied sciences in Hungary. I am enrolled in the Computer Science Engineering (BSc) program, which covers topics such as programming, data structures, algorithms, software engineering, operating systems, databases and artificial intelligence.

I chose software engineering because I was fascinated by how software can solve complex problems and create value for people and businesses. I wanted to learn how to design, develop and maintain high-quality software systems that are reliable, efficient and secure. I also enjoyed working with computers and learning new technologies.

However, after studying for a few semesters, I realized that software engineering is not only about writing code or building applications. It is also about understanding the needs and expectations of customers and users, managing projects and resources, collaborating with other developers or stakeholders and adapting to changing requirements or environments.

That's when I decided to focus on AWS and cloud technologies. AWS stands for Amazon Web Services, which is the world's most comprehensive and broadly adopted cloud platform. Cloud computing is the delivery of IT resources over the internet with pay-as-you-go pricing.

I chose AWS and cloud technologies because they offer many benefits for software engineers such as:

Scalability and flexibility: AWS allows you to easily adjust your IT resources according to your needs without worrying about capacity or performance issues .
Cost savings: AWS helps you reduce your IT costs by only paying for what you use without any upfront or long-term commitments.
Security: AWS provides high levels of security for your data and applications by using encryption, firewalls and access control mechanisms.
Innovation and speed: AWS enables you to launch new products and services faster by using ready-made tools and services that simplify development and deployment processes.
Global reach: AWS allows you to access customers and markets around the world by using its global network of data centers or regions.

By focusing on AWS and cloud technologies, I hope to improve my skills in IT or apply them to solve real-world problems in various domains such as healthcare, education or agriculture.

I also hope to enhance my career prospects as a software engineer by gaining valuable experience or certifications in AWS or cloud computing.
I believe that focusing on AWS and cloud technologies can help me become a better software engineer who can deliver value-added solutions for customers or users.

That's why I chose to focus on AWS and cloud technologies while I am studying for my bachelor degree in software engineering at Obuda University.

Thank you for reading my first blog post. If you have any questions or comments, please feel free to comment below.