Blockchain

Blockchain is a decentralized, distributed ledger technology that records transactions across multiple computers in a way that ensures security, transparency, and immutability. It operates as a chain of blocks, where each block contains a list of transactions, a timestamp, and a cryptographic hash of the previous block, creating a tamper-resistant record. This technology underpins cryptocurrencies like Bitcoin and enables applications in finance, supply chain, and digital identity.

Also known as: Distributed Ledger Technology, DLT, Block chain, Cryptochain, Chain of blocks

🧊Why learn Blockchain?

Developers should learn blockchain to build decentralized applications (dApps), smart contracts, and secure systems that require trustless transactions and data integrity. It is essential for roles in fintech, Web3, and industries needing transparent audit trails, such as logistics or voting systems. Understanding blockchain also helps in exploring emerging fields like decentralized finance (DeFi) and non-fungible tokens (NFTs).

See how it ranks →

Compare Blockchain

Blockchain vs Centralized Databases→Blockchain vs Traditional Ledgers→Blockchain vs Cloud Storage→

Learning Resources

Blockchain Basics - IBM Developer

Blockchain and Cryptocurrency Explained - Coursera

Introduction to Blockchain Technology - edX

Mastering Blockchain - O'Reilly Book

Blockchain Explained - YouTube Video by Simply Explained

Related Tools

Smart Contracts Cryptography Decentralized Applications Consensus Algorithms Ethereum

Alternatives to Blockchain

Centralized Databases Traditional Ledgers Cloud Storage

Other Blockchain

Access Control is a fundamental security concept that governs how users, systems, or processes are granted or denied access to resources such as data, applications, or networks. It involves defining and enforcing policies to ensure that only authorized entities can perform specific actions, protecting against unauthorized use, modification, or disclosure. This is implemented through mechanisms like authentication, authorization, and auditing to maintain confidentiality, integrity, and availability in computing environments.

Access tokens are short-lived credentials used in authentication and authorization systems to grant secure access to protected resources, such as APIs or user data. They are typically issued by an authorization server after a user or application successfully authenticates, and they contain claims about the requester's identity and permissions. Tokens are commonly implemented using standards like OAuth 2.0 and OpenID Connect to enable stateless, scalable security in modern applications.

Ad-Based Monetization

Ad-based monetization is a revenue model where businesses generate income by displaying advertisements to users, typically in digital products like websites, mobile apps, or games. It involves integrating ad networks or platforms to serve ads, with revenue earned through models such as cost-per-click (CPC), cost-per-mille (CPM), or cost-per-action (CPA). This approach is common in free-to-use services, allowing developers to monetize user traffic without direct payments from end-users.

AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals using artificial intelligence techniques. They combine machine learning, reasoning, and sometimes natural language processing to operate independently or semi-independently in dynamic environments. This concept is foundational in fields like robotics, game AI, and automated systems.

AI Integration refers to the process of incorporating artificial intelligence capabilities, such as machine learning models, natural language processing, or computer vision, into existing software systems, applications, or workflows. It involves connecting AI services, APIs, or custom models to enhance functionality, automate tasks, or provide intelligent insights. This enables systems to perform tasks like predictive analytics, automated decision-making, or personalized user experiences without building AI from scratch.

AI Safety is a multidisciplinary field focused on ensuring that artificial intelligence systems are developed and deployed in ways that are safe, reliable, and aligned with human values and intentions. It addresses risks such as unintended harmful behaviors, security vulnerabilities, and ethical concerns in AI systems, particularly as they become more advanced and autonomous. The goal is to prevent catastrophic outcomes and promote beneficial AI that serves humanity's interests.