ByteByteGo

Popular Backend Tech Stack.

You do not need everything. Choose whatever tech or tools work best for you.

1 - Programming Languages: Some options include Java, Python, C#, Ruby, PHP, JavaScript, Golang, and Rust.
2 - Databases: Multiple options exist, such as PostgreSQL, MySQL, Oracle, MongoDB, AWS DynamoDB, SQLite, and Apache Cassandra.
3 - Frameworks: These include choices such as Spring Boot, NodeJS, Django, Ruby on Rails, FastAPI, and Langchain (for LLM integration).
4 - AI Integration: LLMs such as GPT, Cluade, Gemini, DeepSeek, Mistral, Llama can help augment the backend app with AI capabilities.
5 - Deployment: Options include platforms like AWS, Azure, GCP, Docker, Kubernetes, and Vercel.
6 - CI/CD and Version Control: Tools like Jenkins, GitHub, Gitlab, Bitbucket, and Circle CI help in this area.
7 - Caching: Options include the use of CDNs, Redis, and Memcached.
8 - Architectural Patterns: Some common architectural patterns include microservices, monolithic, serverless, etc.
9 - APIs: APIs include options like REST, JSON, GraphQL, etc.

Over to you: What else will you add to the Backend Tech Stack?

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22 hours ago | [YT] | 1,640

ByteByteGo

Cloudflare is much more than just a CDN and DDoS protection service.

Let’s do a quick comparison of Cloudflare, AWS, and Azure.

Cloudflare has rapidly expanded beyond its traditional CDN roots, launching a suite of modern developer-first services like Workers, R2, D1, and so on. These offerings position it as a serious edge-native alternative to other cloud providers.

Here are the key cloud capabilities that Cloudflare supports:

1 - Edge Compute and Serverless
2 - Object and Blob Storage
3 - Relational Databases
4 - Containers
5 - Sandboxes
6 - Workflows
7 - AI Agents SDK
8 - Vector and AI search
9 - Data Connectivity
10 - AI Infrastructure
11 - Content Delivery Network
12 - DNS
13 - Load Balancing

Over to you: Have you used Cloudflare’s new offerings? What are your thoughts on them?

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1 day ago | [YT] | 2,197

ByteByteGo

Forward Proxy versus Reverse Proxy

A forward proxy sits between clients (users) and the internet. It acts on behalf of users, hiding their identity or filtering traffic before it reaches the external web.

Some applications of a forward proxy are:
1 - Protect users while browsing the internet.
2 - Helps organizations restrict access to certain websites.
3 - Speeds up web browsing by caching frequently accessed content.

A reverse proxy sits between the internet (clients) and backend servers. It acts on behalf of servers, handling incoming traffic.

Some applications of a reverse proxy are:
1 - Distributes traffic across multiple servers to ensure no single server is overwhelmed.
2 - Handles SSL encryption/decryption so backend servers don’t have to.
3 - Helps protect backend servers from DDoS attacks.

Over to you: What else will you add to understand forward proxy and reverse proxy?

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3 days ago | [YT] | 1,911

ByteByteGo

Linux file permission illustrated.

𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩
Every file or directory is assigned 3 types of owner:

🔹Owner: the owner is the user who created the file or directory.
🔹Group: a group can have multiple users. All users in the group have the same permissions to access the file or directory.
🔹Other: other means those users who are not owners or members of the group.

𝐏𝐞𝐫𝐦𝐢𝐬𝐬𝐢𝐨𝐧
There are only three types of permissions for a file or directory.

🔹Read (r): the read permission allows the user to read a file.
🔹Write (w): the write permission allows the user to change the content of the file.
🔹Execute (x): the execute permission allows a file to be executed.

Over to you: chmod 777, good idea?

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4 days ago | [YT] | 2,050

ByteByteGo

Types of Virtualization

Virtualization didn’t just make servers efficient, it changed how we build, scale, and deploy everything. Here’s a quick breakdown of the four major types of virtualization you’ll find in modern systems:

1. Traditional (Bare Metal): Applications run directly on the operating system. No virtualization layer, no isolation between processes. All applications share the same OS kernel, libraries, and resources.

2. Virtualized (VM-based): Each VM runs its own complete operating system. The hypervisor sits on physical hardware and emulates entire machines for each guest OS. Each VM thinks it has dedicated hardware even though it's sharing the same physical server.

3. Containerized: Containers share the host operating system's kernel but get isolated runtime environments. Each container has its own filesystem, but they're all using the same underlying OS. The container engine (Docker, containerd, Podman) manages lifecycle, networking, and isolation without needing separate operating systems for each application.

Lightweight and fast. Containers start in milliseconds because you're not booting an OS. Resource usage is dramatically lower than VMs.

4. Containers on VMs: This is what actually runs in production cloud environments. Containers inside VMs, getting benefits from both. Each VM runs its own guest OS with a container engine inside. The hypervisor provides hardware-level isolation between VMs. The container engine provides lightweight application isolation within VMs.

This is the architecture behind Kubernetes clusters on AWS, Azure, and GCP. Your pods are containers, but they're running inside VMs you never directly see or manage.

Over to you: In your experience, which setup strikes the best balance between performance and flexibility?

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6 days ago | [YT] | 1,843

ByteByteGo

The AI Agent Tech Stack

1. Foundation Models: Large-scale pre-trained language models that serve as the “brains” of AI agents, enabling capabilities like reasoning, text generation, coding, and question answering.
2. Data Storage: This layer handles vector databases and memory storage systems used by AI agents to store and retrieve context, embeddings, or documents.
3. Agent Development Frameworks: These frameworks help developers build, orchestrate, and manage multi-step AI agents and their workflows.
4. Observability: This category enables monitoring, debugging, and logging of AI agent behavior and performance in real-time.
5. Tool Execution: These platforms allow AI agents to interface with real-world tools (for example, APIs, browsers, external systems) to complete complex tasks.
6. Memory Management: These systems manage long-term and short-term memory for agents, helping them retain useful context and learn from past interactions.

Over to you: What else will you add to the list?

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1 week ago | [YT] | 1,883

ByteByteGo

How to Build a Basic RAG Application on AWS?

RAG is an AI pattern that combines a search step with text generation. It retrieves relevant information from a knowledge source (like a vector database) and then uses an LLM to generate accurate, context-aware responses.

Ingestion Stage

1. All raw documents (PDFs, text, etc) are first stored in Amazon S3.
2. When a file is added, AWS Lambda runs an ingestion function. This function cleans and splits the document into smaller chunks.
3. Each chunk is sent to Amazon Bedrock’s Titan embeddings model, which converts it into vector representations
4. These embeddings, along with metadata, are stored in a vector database such as OpenSearch serverless, DynamoDB

Querying Stage:

1. A user sends a question through the app frontend, which goes to API Gateway and then a Lambda query function.
2. The question is converted to an embedding using Amazon Bedrock Titan Embeddings.
3. This embedding is compared against the stored document embeddings in the vector database to find the most relevant chunks.
4. The relevant chunks and the user’s questions are sent to an LLM (like Claude or OpenAI on Bedrock) to generate an answer.
5. The generated response is sent back to the user through the same API.

Over to you: Which other AWS service will you use to build an RAG app on AWS?

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1 week ago | [YT] | 1,429

ByteByteGo

How to Learn AWS?

AWS is one of the most popular cloud platforms. When AWS goes down, a large part of the Internet goes down.

Here’s a learning map that can help you master AWS:

1 - AWS Fundamentals
This includes topics like “What is AWS?”, Global Infrastructure, AWS Billing, Management, and IAM basics.
2 - Core Compute, Storage & Networking
This includes compute services like EC2, Lambda, ECS, EKS, Storage Services (such as S3, EBS, EFS, Glacier), and Networking Services (such as VPC, ELB, Route 53).
3 - Databases and Data Services
This includes topics like Relational Databases (RDS MySQL and PostgreSQL), NoSQL, and In-Memory Databases like ElastiCache (Redis and Memcached).
4 - Security, Identity & Compliance
Consists of topics like IAM Deep Dive, Encryption (KMS, S3 SSE), Security Tools, VPC Security Groups, and Compliance-related tools for HIPAA, SOC, and GDPR.
5 - DevOps, Monitoring & Automation
This includes topics like DevOps Tools (CodeCommit, CodeBuild, CodePipeline), Infrastructure as Code, CI/CD Pipelines, Monitoring Tools (CloudWatch, CloudTrail), and Cost Management and Billing Dashboard
6 - Learning Paths and Certifications
Consists of topics like AWS Learning Resources, such as Skill Builder and documentation, and certification paths such as Cloud Practitioner, Solutions Architect Associate, Developer Associate, SysOps, and DevOps Engineer.

Over to you: What else will you add to the list for learning AWS?

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1 week ago | [YT] | 2,543

ByteByteGo

How to Design Good APIs

A well-designed API feels invisible, it just works. Behind that simplicity lies a set of consistent design principles that make APIs predictable, secure, and scalable.

Here's what separates good APIs from terrible ones:

- Idempotency: GET, HEAD, PUT, and DELETE should be idempotent. Send the same request twice, get the same result. No unintended side effects. POST and PATCH are not idempotent. Each call creates a new resource or modifies the state differently.

Use idempotency keys stored in Redis or your database. Client sends the same key with retries, server recognizes it and returns the original response instead of processing again.

- Versioning

- Noun-based resource names: Resources should be nouns, not verbs. “/api/products”, not “/api/getProducts”.

- Security: Secure every endpoint with proper authentication. Bearer tokens (like JWTs) include a header, payload, and signature to validate requests. Always use HTTPS and verify tokens on every call.

- Pagination: When returning large datasets, use pagination parameters like “?limit=10&offset=20” to keep responses efficient and consistent.

Over to you: What’s the most common API design mistake you’ve seen, and how would you fix it?

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1 week ago | [YT] | 2,339

ByteByteGo

Big Data Pipeline Cheatsheet for AWS, Azure, and Google Cloud

Each platform offers a comprehensive suite of services that cover the entire lifecycle:

1 - Ingestion: Collecting data from various sources
2 - Data Lake: Storing raw data
3 - Computation: Processing and analyzing data
4 - Data Warehouse: Storing structured data
5 - Presentation: Visualizing and reporting insights

AWS uses services like Kinesis for data streaming, S3 for storage, EMR for processing, RedShift for warehousing, and QuickSight for visualization.

Azure’s pipeline includes Event Hubs for ingestion, Data Lake Store for storage, Databricks for processing, Cosmos DB for warehousing, and Power BI for presentation.

GCP offers PubSub for data streaming, Cloud Storage for data lakes, DataProc and DataFlow for processing, BigQuery for warehousing, and Data Studio for visualization.

Over to you: What else would you add to the pipeline?

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1 week ago | [YT] | 2,560