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Pankaj Gupta
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Pankaj GuptaScholar
Asked: 9 months agoIn:
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Is blockchain still relevant for startups in 2025, or has it been overshadowed by AI?

Is blockchain still relevant for startups in 2025, or has it been overshadowed by AI?

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aiblockchain
  1. Pankaj Gupta
    Pankaj Gupta Scholar

    Yes, blockchain is still very relevant, but its role has evolved, and its visibility has been overshadowed by the AI boom. --- The Current Landscape (2025) 1. AI is Dominating Headlines Artificial Intelligence — especially Generative AI — has taken center stage. Most funding, media attention, and taRead more

    Yes, blockchain is still very relevant, but its role has evolved, and its visibility has been overshadowed by the AI boom.

    —

    The Current Landscape (2025)

    1. AI is Dominating Headlines

    Artificial Intelligence — especially Generative AI — has taken center stage. Most funding, media attention, and talent are being funneled toward AI startups. This doesn’t mean blockchain is dead — it’s just less hyped right now.

    2. Blockchain’s Shift from Hype to Utility

    The 2017–2021 era was heavy on speculation (think ICOs, NFTs, and meme coins). But now, in 2025, the blockchain space has matured:

    Enterprise adoption is rising (e.g., supply chain, data integrity, tokenization).

    Layer 2 solutions are making transactions faster and cheaper.

    Decentralized identity and Zero-Knowledge Proofs are gaining real traction in privacy-focused applications.

    —

    Where Blockchain Still Shines for Startups

    1. Decentralized Finance (DeFi)
    Startups are building real banking alternatives, especially in developing nations.

    2. Supply Chain Transparency
    Blockchain ensures authenticity and traceability — critical in food, pharma, and luxury goods.

    3. Decentralized Storage and Web3
    Projects like IPFS and Filecoin power a new internet architecture that startups can build on.

    4. Creator Economy & Ownership
    Startups are using NFTs (not as art, but as tools) to manage rights, royalties, and digital identity.

    5. Interoperability and Identity
    Self-sovereign identity systems built on blockchain are becoming foundational for trust in digital ecosystems.

    —

    Investor Sentiment (2025)

    AI is the big fish. Startups with AI + X (e.g., AI + Healthcare, AI + Education) are securing massive rounds.

    Blockchain funding has become more focused. VCs are backing infrastructure projects or use cases with provable real-world impact.

    “AI x Blockchain” startups are emerging, combining the strengths of both (e.g., using blockchain for verifiable AI model outputs or protecting data provenance).

    —

    Strategic Takeaway for Startups

    If your idea is AI-first, go all in — it’s a gold rush.

    If your problem demands decentralization, transparency, or trust without intermediaries — blockchain is still your best bet.

    If you can mix AI and blockchain meaningfully — you’re in an emerging sweet spot.

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Aditya Gupta
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Aditya GuptaScholar
Asked: 9 months agoIn:
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“Which skills should I start learning now to stay ahead as a cloud developer in the future?”    “भविष्य में एक क्लाउड डेवेलपर के रूप में आगे रहने के लिए मुझे अभी कौन-कौन सी स्किल्स सीखनी चाहिए?”

“Which skills should I start learning now to stay ahead as a cloud developer in the future?”

   “भविष्य में एक क्लाउड डेवेलपर के रूप में आगे रहने के लिए मुझे अभी कौन-कौन सी स्किल्स सीखनी चाहिए?”

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  1. Pankaj Gupta
    Pankaj Gupta Scholar

    Skills to Learn Now to Stay Ahead as a Future-Ready Cloud Developer 1. Deep Cloud Platform Expertise Master at least one major cloud provider:Focus on platforms like AWS, Microsoft Azure, or Google Cloud Platform (GCP). Gain proficiency in core services: Compute (EC2, Azure VMs, Compute Engine) StorRead more

    Skills to Learn Now to Stay Ahead as a Future-Ready Cloud Developer

    1. Deep Cloud Platform Expertise

    • Master at least one major cloud provider:
      Focus on platforms like AWS, Microsoft Azure, or Google Cloud Platform (GCP). Gain proficiency in core services:

      • Compute (EC2, Azure VMs, Compute Engine)

      • Storage (S3, Azure Blob, Cloud Storage)

      • Networking (VPC, Load Balancers)

      • Security (IAM, KMS)

      • Serverless (AWS Lambda, Azure Functions)

    • Learn multi-cloud strategies:
      Understand how to architect and manage applications across multiple cloud platforms to avoid vendor lock-in and improve resilience.

    2. Infrastructure as Code (IaC)

    • Learn tools like Terraform, AWS CloudFormation, Azure ARM Templates, or Pulumi to automate cloud infrastructure deployment.

    • IaC enables repeatability, consistency, and scalability — a must-have for efficient cloud operations.

    3. Containerization and Orchestration

    • Master Docker for containerizing applications.

    • Dive deep into Kubernetes — the de facto standard for container orchestration.

    • Learn Helm Charts for Kubernetes application packaging.

    • Understand service meshes (e.g., Istio) and cloud-native patterns.

    4. DevOps and CI/CD Pipelines

    • Learn to build automated CI/CD pipelines with tools like Jenkins, GitLab CI, GitHub Actions, Azure DevOps.

    • Understand continuous integration, continuous delivery, and continuous deployment principles.

    • Familiarize yourself with monitoring tools like Prometheus, Grafana, and logging with ELK Stack.

    5. Serverless Architectures

    • Explore serverless computing to build highly scalable, cost-effective applications.

    • Get hands-on with AWS Lambda, Azure Functions, and Google Cloud Functions.

    • Understand event-driven architectures and microservices patterns.

    6. Cloud Security Best Practices

    • Study cloud security fundamentals — identity and access management (IAM), encryption, secure networking, compliance (e.g., GDPR, HIPAA).

    • Learn about Zero Trust Architecture and how to implement it in cloud environments.

    • Practice using security tools and services like AWS GuardDuty, Azure Security Center.

    7. Cloud-Native Application Development

    • Understand how to design cloud-native apps optimized for scalability, availability, and resilience.

    • Learn about 12-factor app methodology, microservices, API gateways, and service discovery.

    8. Data Engineering and AI/ML Integration

    • Cloud developers increasingly need skills in data pipelines, ETL, and analytics.

    • Learn cloud data services: AWS Glue, BigQuery, Azure Data Factory.

    • Explore how to integrate AI/ML services like AWS SageMaker, Azure ML, or Google AI Platform into your applications.

    9. Programming and Scripting

    • Be proficient in languages commonly used in cloud environments such as Python, Go, JavaScript/TypeScript, or Java.

    • Automate workflows using shell scripting or Python scripts.

    10. Soft Skills and Business Acumen

    • Develop strong problem-solving and communication skills.

    • Understand business needs and translate them into technical solutions.

    • Stay adaptable, curious, and ready to learn new technologies quickly.

    Bonus: Emerging Trends to Watch

    • Edge Computing: Learn how cloud extends to the edge, including IoT integration.

    • GitOps: Automating infrastructure and deployments via Git repositories.

    • Cloud Cost Management: Optimizing cloud spend with tools and strategies.

    • Quantum Computing in Cloud: Early but growing area in providers like AWS Braket.

    Summary Table of Key Skills

    Skill AreaWhy It MattersRecommended Tools/Technologies
    Cloud Platform ExpertiseFoundation for all cloud developmentAWS, Azure, GCP
    Infrastructure as CodeAutomation & scalabilityTerraform, CloudFormation
    Containers & OrchestrationEfficient app deployment & managementDocker, Kubernetes, Helm
    DevOps & CI/CDSpeed & reliability in software deliveryJenkins, GitHub Actions, GitLab CI
    Serverless ArchitecturesCost-effective & scalable appsAWS Lambda, Azure Functions
    Cloud SecurityProtect data & appsIAM, KMS, GuardDuty, Zero Trust
    Cloud-Native DevelopmentBuild resilient & scalable appsMicroservices, API Gateways
    Data & AI/ML IntegrationEnhance apps with data insights & AIAWS SageMaker, BigQuery, Azure ML
    Programming & ScriptingCore development & automationPython, Go, JavaScript
    Soft Skills & Business AcumenAlign tech solutions with business needsCommunication, problem-solving

    The cloud landscape evolves rapidly, so continuous learning is vital. Building a strong foundation now with these skills will position you as a versatile, future-proof cloud developer ready to innovate and lead.

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Pankaj Gupta
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Pankaj GuptaScholar
Asked: 9 months agoIn:
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Could You Explain Meta’s Open-Source Strategy in AI System Development?

Could You Explain Meta’s Open-Source Strategy in AI System Development?

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metameta's open-source strategy in aiopen source
  1. Pankaj Gupta
    Pankaj Gupta Scholar

    Meta's open-source strategy in AI system development is centered around transparency, collaboration, and accelerating innovation. The company has consistently released its AI models, frameworks, and tools to the public, allowing researchers, developers, and businesses to contribute, adapt, and improRead more

    Meta’s open-source strategy in AI system development is centered around transparency, collaboration, and accelerating innovation. The company has consistently released its AI models, frameworks, and tools to the public, allowing researchers, developers, and businesses to contribute, adapt, and improve upon them. Here’s a breakdown of Meta’s approach:

    1. Open-Sourcing Large Language Models (LLMs)

    Meta has released multiple versions of Llama (Large Language Model Meta AI) as open-source alternatives to proprietary models from OpenAI and Google.

    By open-sourcing Llama 2, Meta aimed to make powerful AI models accessible to a broader audience while fostering community-driven advancements.

    The upcoming Llama 3, expected in 2024, is likely to follow this trend with further improvements.

    2. AI Frameworks and Developer Tools

    PyTorch: Initially developed by Meta, PyTorch is one of the most widely used deep learning frameworks. It was later transitioned to the Linux Foundation to ensure it remains a neutral and community-driven project.

    FAIR (Facebook AI Research): Meta actively publishes research papers and makes its AI models available, promoting open science.

    3. AI-Powered Infrastructure and Hardware Contributions

    Open Compute Project (OCP): Meta collaborates with industry leaders to develop and share designs for AI data centers and hardware, improving scalability.

    AI Accelerators: Meta has been working on custom AI chips (like the MTIA – Meta Training and Inference Accelerator) and is likely to open-source parts of its hardware designs.

    4. AI Ethics and Responsible AI Development

    Meta promotes transparency in AI by releasing details on model training processes, datasets, and biases.

    It has developed tools like Fairness Flow to detect and mitigate biases in AI models.

    5. Challenges and Criticism

    Open-sourcing powerful AI models has sparked debates on misuse risks, including misinformation and deepfake generation.

    Some argue that while Meta’s AI is “open,” it still retains significant commercial advantages by integrating AI into its platforms like Facebook, Instagram, and WhatsApp.

    6. Future Outlook

    Meta is likely to continue balancing open-source AI with commercial interests, ensuring its AI models benefit both the developer community and its own ecosystem.

    With upcoming innovations in multi-modal AI, generative AI, and metaverse applications, Meta’s open-source strategy will play a key role in shaping the future of AI.

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Aryan Shukla
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Aryan ShuklaBeginner
Asked: 9 months agoIn:
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How is Nested Class different from Inheritance?

How is Nested Class different from Inheritance?

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inheritancejavanested classnested class vs inheritance
  1. Pankaj Gupta
    Pankaj Gupta Scholar

    A nested class is not the same as an inherited class. Let’s see why with simple examples. Nested Classes A nested class is just a class declared inside another class. (a) Static Nested Class class School { static class Student { void showDetails() { System.out.println("I am a student of the school."Read more

    A nested class is not the same as an inherited class. Let’s see why with simple examples.

    1. Nested Classes

    A nested class is just a class declared inside another class.

    (a) Static Nested Class

    class School {

    static class Student {

    void showDetails() {

    System.out.println(“I am a student of the school.”);

    }

    }

    }

     

    public class Demo {

    public static void main(String[] args) {

    School.Student s = new School.Student();

    s.showDetails();

    }

    }

     

    • Student is a nested class inside School.
    • It is not automatically inherited, it’s just contained inside.

    (b) Inner Class (Non-static)

    class School {

    class Teacher {

    void display() {

    System.out.println(“I am a teacher of the school.”);

    }

    }

    }

     

    public class Demo {

    public static void main(String[] args) {

    School school = new School();

    School.Teacher t = school.new Teacher();

    t.display();

    }

    }

    • Teacher is an inner class inside School.
    • It can access School’s instance variables and methods.

     

    1. Inherited Classes

    Inheritance happens when one class extends another.

    class Person {

    void displayInfo() {

    System.out.println(“I am a person.”);

    }

    }

     

    class Student extends Person {

    void showDetails() {

    System.out.println(“I am a student.”);

    }

    }

     

    public class Demo {

    public static void main(String[] args) {

    Student s = new Student();

    s.displayInfo();  // inherited from Person

    s.showDetails();  // defined in Student

    }

    }

    • Student inherits from Person.
    • That means Student automatically gets displayInfo().
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Aryan Shukla
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Aryan ShuklaBeginner
Asked: 9 months agoIn:
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What is Nested Class in Java?

What is Nested Class in Java?

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classjavanested class
  1. Sujeet Singh
    Sujeet Singh Beginner

    A nested class is a member of its enclosing class. It establishes a structural relationship where one class is entirely contained within the declaration of another. This allows the nested class to be closely associated with the functionality of the outer class and can even grant it special access prRead more

    A nested class is a member of its enclosing class. It establishes a structural relationship where one class is entirely contained within the declaration of another. This allows the nested class to be closely associated with the functionality of the outer class and can even grant it special access privileges (especially inner classes) to the outer class’s private members.

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