DEV Community: ANIRUDDHA ADAK

Cyber Law and Ethics :: MAKAUT B.Tech CSE( OEC-CS801B)

ANIRUDDHA ADAK — Mon, 22 Jun 2026 09:54:28 +0000

Unit	Topic Title	Detailed Topics	Hrs/Unit
1	Introduction & Categories of Cybercrime	• Introduction of Cybercrime: What is cybercrime? • Forgery • Hacking • Software Piracy • Computer Network intrusion [4L] • Category of Cybercrime: How criminals plan attacks • Passive attacks • Active attacks • Cyberstalking [4L]	8
2	Cybercrime Mobile & Wireless Devices	• Security challenges posted by mobile devices • Cryptographic security for mobile devices • Attacks on mobile/cellphones: Theft, Virus, Hacking • Bluetooth • Different viruses on laptop [8L]	8
3	Tools and Methods Used in Cybercrime	• Proxy servers • Panword checking (Password checking) • Random checking • Trojan Horses and Backdoors • DOS & DDOS attacks • SQL injection • Buffer overflow [8L]	8
4	Phishing, Identity Theft & Legal Aspects	• Phishing & Identity Theft: Phishing methods, ID Theft • Online identity method [4L] • Cybercrime & Cybersecurity: Legal aspects • Indian laws • IT act • Public key certificate [4L]	8

Cryptography and Network Security (MAKAUT B.Tech CSE:: PEC-CS801B )

ANIRUDDHA ADAK — Mon, 22 Jun 2026 09:28:47 +0000

Unit	Topic Title	Detailed Topics	Hrs/Unit
1	Attacks on Computers & Computer Security	• Introduction • Need for Security • Security approaches • Principles of Security • Types of attack	5
2	Cryptography: Concepts & Techniques	• Introduction • Plaintext & Cipher text • Substitution Techniques • Transposition Techniques • Encryption & Decryption • Symmetric & Asymmetric key Cryptography • Key Range & Key Size	7
3	Symmetric Key Algorithm	• Introduction • Algorithm types & Modes • Overview of Symmetric Key Cryptography • DES (Data Encryption Standard) algorithm • IDEA (International Data Encryption Algorithm) algorithm • RC5 (Rivest Cipher 5) algorithm	8
4	Asymmetric Key Algorithm, Digital Signature and RSA	• Introduction • Overview of Asymmetric key Cryptography • RSA algorithm • Symmetric & Asymmetric key Cryptography together • Digital Signature • Basic concepts of Message Digest and Hash Function (Algorithms on Message Digest and Hash function not required)	5
5	Internet Security Protocols, User Authentication	• Basic Concepts • SSL protocol • Authentication Basics • Password • Authentication Token • Certificate based Authentication • Biometric Authentication	6
6	Electronic Mail Security	• Basics of mail security • Pretty Good Privacy • S/MIME	4
7	Firewall	• Introduction • Types of firewall • Firewall Configurations • DMZ Network	3

E-Commerce & ERP (OEC-CS802A) :: MAKAUT B.Tech (CSE) Syllabus

ANIRUDDHA ADAK — Mon, 22 Jun 2026 04:05:23 +0000

Module	Topic Title	Detailed Topics	Contact Hours
1	Overview & Fundamentals	• Overview • Definitions • Advantages & Disadvantages of E-Commerce • Threats of E-Commerce • Managerial Prospective • Rules & Regulations For Controlling E-Commerce • Cyber Laws	3L
2	Technologies	• Relationship Between E-Commerce & Networking • Different Types of Networking Commerce • Internet, Intranet & Extranet • EDI Systems • Wireless Application Protocol: Definition, Hand Held Devices, Mobility & Commerce, Mobile Computing, Wireless Web • Web Security • Infrastructure Requirement For E-Commerce	5L
3	Business Models of E-Commerce	• Model Based On Transaction Type • Model Based On Transaction Party - B2B, B2C, C2B, C2C • E-Governance	2L
4	E-Strategy	• Overview • Strategic Methods for developing E-commerce	2L
5	Four C’s	• Convergence: Technological Advances in Convergence – Types, Convergence and its implications, Convergence & Electronic Commerce • Collaborative Computing: Collaborative product development, contract as per CAD, Simultaneous Collaboration, Security • Content Management: Definition of content, Authoring Tools & Content Management, Content – partnership, repositories, convergence, providers, Web Traffic & Traffic Management, Content Marketing • Call Center: Definition, Need, Tasks Handled, Mode of Operation, Equipment, Strength & Weaknesses of Call Center, Customer Premises Equipment (CPE)	6L
6	Supply Chain Management	• E-logistics • Supply Chain Portal • Supply Chain Planning Tools (SCP Tools) • Supply Chain Execution (SCE) • SCE - Framework • Internet’s effect on Supply Chain Power	3L
7	E-Payment Mechanism	• Payment through card system • E-Cheque • E-Cash • E-Payment Threats & Protections	1L
8	E-Marketing	• Home-shopping • E-Marketing • Tele-marketing	1L
9	Electronic Data Interchange (EDI)	• Meaning, Benefits, Concepts, Application • EDI Model • Protocols (UN EDI FACT / GTDI, ANSI X-12) • Data Encryption (DES / RSA)	2L
11	Risk of E-Commerce	• Overview • Security for E-Commerce • Security Standards • Firewall • Cryptography • Key Management • Password Systems • Digital certificates • Digital signatures	4L
12	Enterprise Resource Planning (ERP)	• Features, capabilities and Overview of Commercial Software • Re-engineering work processes for IT applications • Business Process Redesign • Knowledge engineering and data warehouse • Business Modules: Finance, Manufacturing (Production), Human Resources, Plant Maintenance, Materials Management, Quality Management, Sales & Distribution ERP Package • ERP Market: ERP Market Place, SAP AG, PeopleSoft, BAAN, JD Edwards, Oracle Corporation • ERP-Present and Future: Enterprise Application Integration (EAI), ERP and E-Commerce, ERP and Internet, Future Directions in ERP	10L

Anthropic: Building AI Safety and Innovation

ANIRUDDHA ADAK — Wed, 10 Jun 2026 17:49:49 +0000

Anthropic: Pioneering AI Safety and Innovation

In the rapidly evolving landscape of artificial intelligence, Anthropic stands out as a company committed to not only advancing AI capabilities but also ensuring its responsible and ethical development. Founded by the visionary siblings Dario Amodei, CEO, and Daniela Amodei, President, Anthropic is at the forefront of building AI systems that are powerful, interpretable, and steerable.

The Foundation of Scaling Laws

A cornerstone of Anthropic's success is the concept of "scaling laws," a theoretical framework developed by Dario Amodei. This principle posits that large language models (LLMs) exhibit significant performance improvements simply by increasing the volume of training data and computational resources. This insight has been instrumental in guiding Anthropic's research and development, leading to breakthroughs in AI performance without necessarily requiring fundamental algorithmic changes.

Claude: A Suite of Advanced AI Models

Anthropic's flagship AI model, Claude, represents a significant leap in AI capabilities. The company has released several iterations, including the high-performance Opus 4.6 and 4.7. Beyond its general intelligence, Claude is specialized through various tools:

Claude Code: This specialized tool automates substantial portions of software engineering. According to Boris Cherny, Head of Claude Code, it has reached a level of sophistication where it can generate 100% of the code for some engineers, fundamentally transforming the software development workflow.
Claude Cowork: Extending automation beyond coding, Claude Cowork assists with a broad spectrum of enterprise tasks. This includes data analysis, email management, and presentation creation, empowering human professionals to focus on higher-level strategic thinking and interpersonal collaboration.

Addressing Critical Challenges: Productivity and Safety

Anthropic's mission is twofold: to enhance human productivity and to champion AI safety and responsibility. The company aims to:

Alleviate Productivity Bottlenecks: By automating complex and time-consuming tasks, Anthropic's tools enable individuals and organizations to streamline operations, allowing human talent to be reallocated to more creative and impactful endeavors.
Ensure AI Safety and Responsibility: A core tenet of Anthropic's philosophy is the belief that AI development must be accompanied by robust safeguards. They actively research and implement mechanisms to prevent AI from exhibiting undesirable behaviors such as hallucination, deception, or misuse for harmful purposes like mass surveillance or autonomous weaponry.

Constitutional AI: Ethical Guardrails for Advanced Systems

A key innovation in Anthropic's approach to AI safety is Constitutional AI. Claude is trained to adhere to a set of guiding principles, or a "constitution," derived from foundational documents such as the UN Declaration of Human Rights. This constitutional framework ensures that Claude's behavior remains consistently helpful, honest, and harmless, even as its capabilities grow.

Professional Warmth: A Distinct AI Persona

Unlike some AI systems that might be perceived as overly friendly or robotic, Claude is designed with a unique persona characterized by "professional warmth." This approachability, combined with a clear and concise communication style, makes interactions with Claude intuitive and effective, fostering a productive human-AI collaboration.

Navigating Ethical Dilemmas and Commercial Pressures

Anthropic has demonstrated a steadfast commitment to its ethical principles, even when faced with significant commercial pressures. A notable instance involved the decision not to release Mythos, a powerful model capable of identifying thousands of cybersecurity vulnerabilities. The company prioritized the potential risks of such a "superweapon" falling into the wrong hands over immediate commercial gains.

Furthermore, Anthropic famously engaged in a principled stand-off with the Pentagon, refusing to permit Claude's deployment for mass domestic surveillance or fully autonomous weapons systems. This unwavering commitment to ethical "red lines" led to a temporary blacklisting by the Department of Defense, underscoring Anthropic's dedication to its core values.

Addressing Existential Risks and Advocating for Responsible Governance

Dario Amodei openly acknowledges the profound risks associated with advanced AI, citing a "10-25% chance of civilization collapse" due to its uncontrolled development. In response, Anthropic advocates for proactive measures, including mandatory pre-release testing, independent auditing, and a balanced regulatory framework. This approach seeks to avoid both unchecked AI proliferation and excessive governmental nationalization, aiming instead for a collaborative global effort to mitigate existential risks.

In conclusion, Anthropic is not merely developing cutting-edge AI; it is actively shaping the future of AI with a profound sense of responsibility. By integrating advanced technical capabilities with a robust ethical framework, the company strives to ensure that artificial intelligence serves humanity's best interests, fostering innovation while safeguarding against potential perils.

Anthropic: Pioneering AI Safety and Innovation

ANIRUDDHA ADAK — Wed, 10 Jun 2026 17:49:03 +0000

Anthropic: Pioneering AI Safety and Innovation

The Foundation of Scaling Laws

Claude: A Suite of Advanced AI Models

Claude Code: This specialized tool automates substantial portions of software engineering. According to Boris Cherny, Head of Claude Code, it has reached a level of sophistication where it can generate 100% of the code for some engineers, fundamentally transforming the software development workflow.
Claude Cowork: Extending automation beyond coding, Claude Cowork assists with a broad spectrum of enterprise tasks. This includes data analysis, email management, and presentation creation, empowering human professionals to focus on higher-level strategic thinking and interpersonal collaboration.

Addressing Critical Challenges: Productivity and Safety

Anthropic's mission is twofold: to enhance human productivity and to champion AI safety and responsibility. The company aims to:

Alleviate Productivity Bottlenecks: By automating complex and time-consuming tasks, Anthropic's tools enable individuals and organizations to streamline operations, allowing human talent to be reallocated to more creative and impactful endeavors.
Ensure AI Safety and Responsibility: A core tenet of Anthropic's philosophy is the belief that AI development must be accompanied by robust safeguards. They actively research and implement mechanisms to prevent AI from exhibiting undesirable behaviors such as hallucination, deception, or misuse for harmful purposes like mass surveillance or autonomous weaponry.

Constitutional AI: Ethical Guardrails for Advanced Systems

Professional Warmth: A Distinct AI Persona

Navigating Ethical Dilemmas and Commercial Pressures

Addressing Existential Risks and Advocating for Responsible Governance

Herclew: The Autonomous AI Game Master in Your DMs 🦞☤

ANIRUDDHA ADAK — Wed, 10 Jun 2026 12:45:52 +0000

Herclew: The Autonomous AI Game Master Living in Your DMs 🦞☤

What if your next favorite role-playing game didn't live on Steam or in a browser, but directly inside your chat apps—managed by an autonomous, self-improving AI agent that could compile your code in a secure sandbox?

Introducing Herclew (the hybrid convergence of Nous Research's Hermes Agent and OpenClaw's universal gateway). For the June Solstice Game Jam, I built Solstice Cage, an interactive, cryptographic text-adventure game hosted entirely on WhatsApp, Telegram, and Discord, where the Game Master is an autonomous AI agent running inside a containerized kernel-level sandbox.

🎮 What I Built: "Solstice Cage"

Solstice Cage is a terminal-based and chat-based cryptographic adventure. You play as a field agent in June 1941, working to decrypt intercepted transmissions.

The Interface: You play by simply DMing the Herclew agent on WhatsApp, Telegram, or Discord.
The Gameplay: The agent acts as the Game Master (GM), describing your environment, generating cryptographic ciphers, and spawning a secure, interactive bash shell where you can write and execute decryption scripts.
The Goal: Break the codes and escape the facility before the solstice sun sets.

☀️ Solstice Theme Relevance

The game is built around the passage of time and the duality of light and shadow:

Daylight Cycle: The game’s difficulty and environmental descriptions sync with the real-world daylight hours of the June Solstice. During the longest day of the year, players have more time, but the heat depletes their resources.
Visual Contrast: If played via the Web Canvas UI (A2UI), the interface transitions dynamically from deep shadow during night hours to bright, high-contrast layouts representing intense solstice daylight.

🏛️ How I Built It (The Herclew Architecture)

Herclew is structured as a decoupled monorepo:

TypeScript Gateway (gateway-node): Handles connection interfaces for WhatsApp, Telegram, and Discord, processes incoming DMs, and manages active session states.
Python Reasoning Core (core-agent): Manages the AI agent planning loop, memory recall, and tool execution.
The Secure Sandbox: Running untrusted code is dangerous. Herclew cages the agent and the player's scripts using Linux kernel-level enforcement:
- Landlock LSM: Strictly limits file system access.
- Seccomp BPF: Filters system calls (blocking dangerous actions like ptrace or mount).
- Open Policy Agent (OPA): Restricts outbound network calls.

👑 Best Ode to Alan Turing

As an entry in the "Best Ode to Alan Turing" category, the game directly incorporates his legacy:

The Imitation Game: The core puzzle mechanic is an "inverted Turing Test" where the player must convince the AI gatekeeper that they are human by solving algorithmic code-breaking challenges.
Bletchley Park Ciphers: Puzzles are modeled after the Enigma and Caesar ciphers. Players must write code in JavaScript or Python to decrypt complex patterns, mirroring the cryptanalysis work done during WWII.

🧠 Best Google AI Usage (Gemini 3.5)

Herclew uses the Google Gemini API (via Gemini 3.5 Flash and Pro) to drive the entire experience:

Narrative Generation: Gemini acts as a dynamic storyteller, reacting to players' creative inputs and describing the unfolding adventure.
Structured Output Gating: Using Pydantic templates and constrained generation, Gemini validates player moves and execution results reliably.
Interactive Code Guide: If a player gets stuck on a cryptographic puzzle, Gemini provides contextual hints and debugging support without spoiling the answer.

🚀 Check Out Herclew

The code for Herclew is fully open-source:
👉 GitHub: aniruddhaadak80/herclew

Try hosting your own AI Game Master today!

Herclew: The Autonomous AI Game Master Living in Your DMs 🦞☤

ANIRUDDHA ADAK — Wed, 10 Jun 2026 12:34:19 +0000

Herclew: The Autonomous AI Game Master Living in Your DMs 🦞☤

🎮 What I Built: "Solstice Cage"

Solstice Cage is a terminal-based and chat-based cryptographic adventure. You play as a field agent in June 1941, working to decrypt intercepted transmissions.

The Interface: You play by simply DMing the Herclew agent on WhatsApp, Telegram, or Discord.
The Gameplay: The agent acts as the Game Master (GM), describing your environment, generating cryptographic ciphers, and spawning a secure, interactive bash shell where you can write and execute decryption scripts.
The Goal: Break the codes and escape the facility before the solstice sun sets.

☀️ Solstice Theme Relevance

The game is built around the passage of time and the duality of light and shadow:

Daylight Cycle: The game’s difficulty and environmental descriptions sync with the real-world daylight hours of the June Solstice. During the longest day of the year, players have more time, but the heat depletes their resources.
Visual Contrast: If played via the Web Canvas UI (A2UI), the interface transitions dynamically from deep shadow during night hours to bright, high-contrast layouts representing intense solstice daylight.

🏛️ How I Built It (The Herclew Architecture)

Herclew is structured as a decoupled monorepo:

TypeScript Gateway (gateway-node): Handles connection interfaces for WhatsApp, Telegram, and Discord, processes incoming DMs, and manages active session states.
Python Reasoning Core (core-agent): Manages the AI agent planning loop, memory recall, and tool execution.
The Secure Sandbox: Running untrusted code is dangerous. Herclew cages the agent and the player's scripts using Linux kernel-level enforcement:
- Landlock LSM: Strictly limits file system access.
- Seccomp BPF: Filters system calls (blocking dangerous actions like ptrace or mount).
- Open Policy Agent (OPA): Restricts outbound network calls.

👑 Best Ode to Alan Turing

As an entry in the "Best Ode to Alan Turing" category, the game directly incorporates his legacy:

The Imitation Game: The core puzzle mechanic is an "inverted Turing Test" where the player must convince the AI gatekeeper that they are human by solving algorithmic code-breaking challenges.
Bletchley Park Ciphers: Puzzles are modeled after the Enigma and Caesar ciphers. Players must write code in JavaScript or Python to decrypt complex patterns, mirroring the cryptanalysis work done during WWII.

🧠 Best Google AI Usage (Gemini 3.5)

Herclew uses the Google Gemini API (via Gemini 3.5 Flash and Pro) to drive the entire experience:

Narrative Generation: Gemini acts as a dynamic storyteller, reacting to players' creative inputs and describing the unfolding adventure.
Structured Output Gating: Using Pydantic templates and constrained generation, Gemini validates player moves and execution results reliably.
Interactive Code Guide: If a player gets stuck on a cryptographic puzzle, Gemini provides contextual hints and debugging support without spoiling the answer.

🚀 Check Out Herclew

The code for Herclew is fully open-source:
👉 GitHub: aniruddhaadak80/herclew

Try hosting your own AI Game Master today!

fable 5 just changed everything we know about ai coding

ANIRUDDHA ADAK — Wed, 10 Jun 2026 05:51:16 +0000

what is fable 5

fable 5 is anthropic's newest ai model that is now available to everyone. it is called a mythos class model and it is the most capable model anthropic has ever made generally available. the company says it is safe for general use and works well for codin

what people are building

designers are using fable 5 to create websites that look professional and don't have the typical ai slop look. one designer said the lines between ai generated designs and human designs are blurring fast. developers are building mobile and web apps with just a few prompts. one person built a complete app in o

stripe tested fable 5 on a 50 million line ruby codebase and completed the migration in one day. before fable 5, this would have taken a team two months by hand. that is a huge time savings.

benchmarks and performance

fable 5 scores 86.3 percent on agentic coding tasks which is higher than claude opus 4.8 at 69.2 percent, gpt 5.5 at 58.6 percent, and gemini 3.1 pro at 54.2 percent. it is state of the art on nearly all tested benchmarks. the longer and more comp

user reviews and experiences

most people are impressed. one person said fable 5 is crazy good. another said it's operating in a completely different dimension compared to other models. the visual gap in 3d fluid dynamics and complex physics is insane. however some users have reported issues. one person said it took 7 minutes and was still thinking. another developer had a long sigh when tryin

community thoughts

people inside anthropic say this is a major milestone like previous breakthrough releases. an anthropic employee who was there for every model launch said fable 5 stands out as a step change in how we use models. the community is excited but also testing it critically. some are comparing it to previous mo

where to access it

fable 5 is available today on all paid plans, in claude code, on the claude api, and on all major cloud platforms. it is also available on gspark code agent, gspark claw, blink, venice ai, and gmi cloud. the model is designed for extended autonomous coding and knowledge tasks with built in safety classifiers.

final thoughts

fable 5 represents a big step forward in ai capabilities. it can build apps, write code, do research, and handle complex tasks better than previous models. while there are some performance concerns, the overall sentiment is very positive. developers are already building real products with it and seeing impressive results.dels and finding it superior. others are finding it slow for certain tasks.g to work on fixing his project with fable 5.lex the task, the larger fable 5's lead over other models.nly 2 prompts and it worked better than the original.g, research, knowledge work, and vision tasks.

The Future is Now: Navigating Web Development Trends in 2026

ANIRUDDHA ADAK — Sat, 06 Jun 2026 12:31:47 +0000

The Future is Now: Navigating Web Development Trends in 2026

Introduction

The web development landscape is in a constant state of evolution, with new technologies and methodologies emerging at a rapid pace. As we delve deeper into 2026, several key trends are shaping how we build and interact with the web. This article explores some of the most impactful developments, with a particular focus on the transformative role of Artificial Intelligence and Large Language Models.

Key Trends in Web Development for 2026

1. AI-Driven Development and LLM Integration

Artificial Intelligence, particularly Large Language Models (LLMs), is no longer a futuristic concept but a present-day reality deeply integrated into the development workflow. From intelligent code completion and automated testing to generating entire code snippets and documentation, AI is significantly boosting developer productivity. LLMs are also being leveraged for dynamic content generation, personalized user experiences, and advanced conversational interfaces, making applications more intuitive and responsive.

2. Server-First Architectures and Edge Computing

The push for enhanced performance and reduced latency continues with the rise of server-first architectures and the increasing adoption of edge computing. By processing data closer to the user, applications can deliver faster response times and a more seamless experience. This trend is complemented by frameworks that prioritize server-side rendering and static site generation, optimizing for speed and efficiency.

3. Advanced Frontend Frameworks and UI/UX

Frontend development continues to innovate with frameworks like React, Next.js, and others pushing the boundaries of what's possible in the browser. The focus is on creating highly interactive, performant, and visually stunning user interfaces. Expect to see more sophisticated animations, 3D elements, and immersive experiences becoming standard, often powered by WebAssembly and advanced graphics APIs.

4. Enhanced Security and Privacy by Design

With increasing data breaches and privacy concerns, security and privacy are paramount. Developers are adopting a
privacy-by-design approach, integrating robust security measures from the initial stages of development. This includes stricter data handling protocols, advanced authentication methods, and the use of privacy-enhancing technologies.

5. Web3 and Decentralized Applications (dApps)

While still in its nascent stages, Web3 continues to gain traction, promising a more decentralized and user-controlled internet. Blockchain technology, cryptocurrencies, and NFTs are foundational to dApps, offering new paradigms for ownership, identity, and data management. Developers are exploring innovative ways to integrate these technologies, creating applications that are transparent, secure, and resistant to censorship.

Conclusion

The web development landscape in 2026 is characterized by rapid innovation and a strong emphasis on performance, security, and intelligent automation. AI and LLMs are not just tools but integral components shaping the future of how we build and interact with digital experiences. Staying abreast of these trends will be crucial for developers looking to build impactful and future-proof applications.

The Cloud Just Got a Brain. Google NEXT '26 Changed Everything.

ANIRUDDHA ADAK — Tue, 28 Apr 2026 16:06:00 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

"The Agentic Cloud."

Two words. Thomas Kurian said them. And 40,000 developers in Las Vegas went quiet.

I set an alarm for a keynote.

That has never happened before in my life.

But April 22, 2026 felt different. Google Cloud NEXT '26 was not just another product dump. It was a declaration. A line drawn in the sand.

The cloud stopped being a storage locker. It became a thinking, acting, collaborating system.

Here is what happened -- and why it matters to every developer alive right now.

// CHAPTER ONE

The Two Words That Started It All

"The Agentic Cloud."

Not AI-powered. Not AI-assisted.

Agentic.

Meaning: the cloud does not wait for you anymore.

It plans. It acts. It corrects itself.

You give it a goal. It builds the path.

Think of it this way -- you stop being the driver. You become the destination.

This is Google's bet for the next decade of computing. And after watching the entire keynote, I think they are right.

// CHAPTER TWO

Vertex AI is Gone. Meet Its Replacement.

Vertex AI -- the platform millions of developers have been using -- got a full rebrand and rebuild.

It is now called the Gemini Enterprise Agent Platform.

And it is not just a name change.

Here is what actually changed:

What Was There	What Is There Now
Vertex AI (single brand)	Gemini Enterprise Agent Platform
~30 model options	200+ models including Claude
Manual API integrations	Managed MCP servers built-in
Isolated agents	Agent2Agent (A2A) protocol

The A2A protocol is the quietly massive one.

It means agents built on Gemini can talk to agents built on Claude. Or any other model. In an open, standardized way.

# Before A2A:
Gemini Agent ----X---- Claude Agent   (incompatible)

# After A2A:
Gemini Agent <---A2A---> Claude Agent <---A2A---> Your Custom Agent
               seamless. open. production-ready.

No more building walls between your AI tools.

The whole ecosystem just became one conversation.

// CHAPTER THREE

The Agent That Browses the Internet For You

This is the one I could not stop thinking about.

Project Mariner.

Google DeepMind built an AI agent that uses the web the way you do -- it reads pages, clicks buttons, fills forms, and completes tasks.

The numbers:

83.5% score on the WebVoyager benchmark
10 tasks running simultaneously on cloud VMs
Handles shopping, research, form-filling -- all in the background

You open your laptop. You tell Mariner what you need. You go make chai. It is done by the time you are back.

The roadmap is already laid out:

Q2 2026 -- Mariner Studio launches (visual builder for web agents)

Q3 2026 -- Cross-device sync (your agents follow you everywhere)

Q4 2026 -- Agent marketplace (buy, sell, and share agents like apps)

This is not a research project anymore. This is infrastructure.

// CHAPTER FOUR

The Chip Built For This Exact Moment

Every software revolution needs new hardware under it.

Google delivered.

8th generation TPUs. Two flavors, each purpose-built:

TPU 8t -- built for training. Frontier models. The heavy lifting.

TPU 8i -- built for inference. Real-time. Low latency. Production workloads.

Both run on Google's own Axion ARM-based processors for the first time. Chip-to-API, fully co-designed.

The result is hard to argue with:

Gemini 2.0 Flash achieves 24x higher intelligence per dollar vs GPT-4o.

And 5x higher than DeepSeek R1.

That is not a benchmark slide. That is a cost structure shift for every team building on AI.

// CHAPTER FIVE

One App. Every Employee. No More Tool Chaos.

Google also killed the fragmentation.

Agentspace got absorbed. The result is a unified product called Gemini Enterprise.

One interface. For everyone. For everything.

# The old reality for enterprise teams:

search   = GoogleSearch()
assistant = DuetAI()
agents    = VertexAgentspace()
connectors = ManualAPISetup()  # weeks of work

# The new reality:

gemini_enterprise = GeminiEnterprise()
# search + assistant + agents + 50+ connectors
# all in one. ships in days.

It connects out of the box to:

Confluence and Jira (for eng teams)
SharePoint and ServiceNow (for enterprise ops)
BigQuery (for data teams)
And more rolling out through 2026

No custom integrations. No brittle webhooks. Just connect and go.

// INTERLUDE

The Announcement Everyone Missed

Everyone was talking about Mariner and the A2A protocol.

Almost nobody mentioned managed MCP servers.

MCP stands for Model Context Protocol. It is the standard that lets AI models securely plug into your data and tools.

Google is now offering managed MCP servers for:

Google Security Operations
Google Workspace
BigQuery
More through 2026

What this means in practice:

# Old way -- weeks of custom integration work:
curl -X POST https://your-api.com/endpoint \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"query": "show me threats from last week"}'
# ...debug, retry, maintain forever

# New way -- one line, managed, secure, done:
agent.connect(mcp_server="google-security-ops")
agent.ask("Summarize critical threats from the last 7 days")

Security teams will sleep better. Developers will ship faster.

This is the unsexy announcement that changes the most workflows.

// CHAPTER SIX

My Real Take. No Hype.

I have been building with AI since GPT-3 in 2020.

I have watched the hype cycles.

I know the difference between a keynote flex and an actual shift.

This felt like an actual shift.

Here is why I believe it:

The stack is complete -- for the first time, hardware (TPU 8t/8i) + runtime (Gemini Agent Platform) + protocol (A2A + MCP) + interface (Gemini Enterprise) are all aligned and shipping together.

The open standards are real -- A2A and MCP are not proprietary traps. Google is betting on ecosystem growth. That is a confident, mature move.

The DX is genuinely better -- 200+ models, no-code builders and pro-code APIs, managed infra for the messy parts. This is developer-first done properly.

But I am still watching a few things closely:

A2A in production -- beautiful in theory. I want to see Claude agents and Gemini agents passing real complex state at scale before I fully trust it.

Mariner error handling -- 10 concurrent tasks is cool. What happens at 1,000 when one fails mid-flow?

MCP governance in regulated industries -- healthcare and finance will ask very hard questions about access logs, data residency, and auditability.

These are not dealbreakers. They are the right questions.

Skepticism is how we build better things.

// START HERE

Your Getting Started Map

For Agent Builders:

Explore the Gemini Enterprise Agent Platform
Read the A2A Protocol spec
Try the no-code agent builder inside Google Workspace today

For ML Engineers:

Check out the 8th Gen TPU details
Run your inference workload benchmarks against Gemini 2.0 Flash
Explore the 200+ model catalog -- Claude, Gemini, and more in one place

For Security Teams:

Look into MCP server support for Google Security Operations
Start building custom security agents with the new agent builder

// END CREDITS

A Personal Note

I build AI tools for a living.

I watch this space every single day.

But this morning -- April 22, 2026, watching Thomas Kurian on a livestream from Kolkata -- I felt something I do not feel often.

The ground got more solid.

The stack that developers have been waiting for is finally here. The protocols are open. The hardware is purpose-built. The developer experience is genuinely better.

The agentic era did not announce itself with fireworks.

It arrived quietly, in Las Vegas, on a Tuesday morning.

And I think the developers who start building on it today are going to look very smart in 18 months.

What was the announcement that hit you hardest from NEXT '26?

Drop it below. I am reading every comment.

I watched AI Agents Take Over the Cloud Live from Google NEXT '26, and Nothing Will Be the Same

ANIRUDDHA ADAK — Sat, 25 Apr 2026 13:09:00 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

Las Vegas, April 22, 2026. The lights are bright. The room holds thousands of developers. And up on stage, Google is about to change the way we think about software forever.

The Morning Everything Shifted

I woke up at 6:30 AM just to watch a keynote.

That sentence alone should tell you something.

I have watched hundreds of tech keynotes over the years. Product launches. "One more thing" moments. Slides full of benchmarks. But Google Cloud NEXT '26 felt different from the first minute.

Google CEO Thomas Kurian walked onto the stage and said two words that set the tone for everything that followed:

"The Agentic Cloud."

Not AI-assisted. Not AI-powered. Agentic.

Meaning: the cloud does not just store your data or run your code anymore. It acts. It decides. It coordinates. It works while you sleep.

This is the shift I have been waiting to see articulated clearly, and Google just drew the map.

What Even Is an "Agentic Cloud"

Let me break this down in plain language before we go deep.

Traditional cloud = you write code, deploy it, it runs when called.

Agentic cloud = you describe a goal, and a network of AI agents figures out the steps, executes them, monitors results, and corrects itself.

Think of it like the difference between hiring a contractor who waits for your instructions versus hiring a project manager who runs the whole thing and only loops you in when needed.

Google is betting the entire next era of cloud computing on this model.

And based on what they showed at NEXT '26, the bet is already paying off.

The Announcements That Stopped Me Mid-Coffee

1. Vertex AI is Dead. Long Live the Gemini Enterprise Agent Platform.

This was the biggest rename in Google Cloud history, and it was not just cosmetic.

Vertex AI has been rebranded and rebuilt as the Gemini Enterprise Agent Platform.

What changed:

Over 200 models available, including third-party ones like Anthropic's Claude
A visual, no-code agent builder for Google Workspace
Managed MCP (Model Context Protocol) servers across Google Cloud services
Production-grade Agent2Agent (A2A) protocol for cross-platform agent communication

The A2A protocol is the piece that matters most to me as a developer.

# What A2A enables:
Agent_A (built on Gemini) <---> Agent_B (built on Claude) <---> Agent_C (custom model)
     All communicating in a shared, open standard

No more vendor lock-in at the agent layer. This is huge.

"We are leading the industry with open standards like the Agent2Agent protocol, ensuring agents can communicate and interoperate regardless of their underlying model or platform."
-- Google Cloud documentation

2. Meet Project Mariner: The Agent That Browses the Web For You

This one made me put my coffee down.

Project Mariner is Google DeepMind's web-browsing AI agent, powered by Gemini 2.0.

Here is what it can do:

Scores 83.5% on the WebVoyager benchmark (the standard test for web agents)
Handles 10 concurrent tasks simultaneously on cloud-based virtual machines
Automates shopping, form-filling, and information retrieval
Runs in the background while you do other work

The roadmap alone is exciting:

Quarter	Feature
Q2 2026	Mariner Studio (visual builder)
Q3 2026	Cross-device synchronization
Q4 2026	Agent marketplace

Imagine telling your AI: "Book me a flight under $400, compare hotel reviews in the area, and add the best option to my calendar."

That is not science fiction anymore. That is Project Mariner on a Tuesday morning.

3. The Chip That Powers It All: 8th Gen TPU

Every software leap needs a hardware foundation.

Google announced their 8th generation TPU family with two purpose-built architectures:

TPU 8t -- optimized for training frontier models
TPU 8i -- optimized for real-time inference

Both are hosted on Google's own Axion ARM-based processors for the first time, creating a fully co-designed stack from chip to API.

This is not just a speed upgrade. It is a philosophy shift: specialized hardware for specialized workloads.

The result: Gemini 2.0 Flash running on this infrastructure achieves 24x higher intelligence per dollar compared to GPT-4o, and 5x higher than DeepSeek R1.

Those numbers are hard to ignore when you are building production applications at scale.

4. Gemini Enterprise: One Product, Every Employee

Google also consolidated Google Agentspace into a unified product called Gemini Enterprise.

What this means for developers and businesses:

Single interface for intranet search, AI assistance, and agentic workflows
Prebuilt connectors for Confluence, Jira, SharePoint, ServiceNow
No-code agent creation in Google Workspace
Custom agents deployable in days, not months
Multimodal search across all your organization's data

# The old way:
search_tool = IntranetSearch()
ai_assistant = DuetAI()
agent_builder = VertexAI()
# 3 separate products, 3 separate learning curves

# The new way:
gemini_enterprise = GeminiEnterprise()
# One platform. Everything connected.

The consolidation removes friction. And in enterprise software, friction is the enemy of adoption.

The Part Nobody Is Talking About: MCP Servers

Buried in the announcements but enormous in impact: managed MCP servers across Google Cloud services.

MCP stands for Model Context Protocol. It is the standard that lets AI models connect to external tools and data sources in a secure, structured way.

Google is now offering managed MCP servers for:

Google Security Operations
Google Workspace
BigQuery
And more services rolling out through 2026

This means you can build a custom security agent, point it at your Google Security Operations MCP server, and it instantly has context-aware access to your threat data.

No custom API integrations. No brittle webhooks. Just:

# Connect your agent to Google Security Operations
agent.connect(mcp_server="google-security-operations")
agent.run("Analyze all anomalies from the last 7 days and summarize critical threats")

Clean. Powerful. The kind of thing that makes security engineers sleep better.

My Honest Take: What This Means for Developers Like Us

I have been building with AI tools since the early days of GPT-3. I have seen the hype cycles. I have also seen the real breakthroughs.

NEXT '26 felt like a real breakthrough moment.

Here is why I believe that:

The stack is finally complete. Hardware (TPU 8t/8i), runtime (Gemini Enterprise Agent Platform), protocol (A2A + MCP), and interface (Gemini Enterprise app) are all aligned and shipping together.

The open standards matter. A2A and MCP are not proprietary lock-in plays. They are Google betting on ecosystem growth over short-term control. That is a mature, confident move.

The developer experience is genuinely better. 200+ models in one place. No-code builders alongside pro-code APIs. Managed infrastructure for the messy parts. This is what developer-first actually looks like.

The Things I Am Still Watching

Not everything from NEXT '26 has me fully convinced yet.

A few honest questions I am sitting with:

A2A interoperability sounds great in theory. Does it hold up when Claude agents and Gemini agents are actually passing complex state to each other in production?
Project Mariner at scale -- 10 concurrent tasks is impressive, but enterprise workflows often involve 100x that. What happens to error handling and recovery at that volume?
MCP server governance -- who controls access, who logs what, and how does this work in regulated industries like healthcare or finance?

These are not dealbreakers. They are the right questions to be asking as we move from keynote excitement to production reality.

Getting Started Right Now

If you want to dive in today, here is your starting map:

For Agent Builders

Explore the Gemini Enterprise Agent Platform
Read the Agent2Agent Protocol spec
Try the no-code agent builder in Google Workspace

For ML Engineers

Check out the 8th Gen TPU page
Benchmark Gemini 2.0 Flash for your inference workloads
Explore the 200+ model catalog on the new platform

For Security Teams

Look into MCP server support for Google Security Operations
Test the new security agent builder capabilities

A Personal Note

I build AI content and tools for a living. I watch this space every single day.

But watching the Google Cloud NEXT '26 opening keynote this morning gave me a feeling I do not get often: the sense that the foundation just got a lot more solid under my feet.

The agentic era is not coming. It arrived this morning in Las Vegas.

And for developers who are ready to build on it, the tools have never been better.

What announcement from NEXT '26 has you most excited?

Drop it in the comments. I read every single one.

Tags: googlecloud gemini ai agents devops machinelearning cloudnextchallenge devchallenge

Everyone Talked About Gemini. Nobody Talked About the Thing That Will Actually Change Your Work.

ANIRUDDHA ADAK — Wed, 22 Apr 2026 19:27:22 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

It is 11 PM in Kolkata. The keynote ended hours ago.

My Twitter feed is full of "Gemini is insane" and "A2A protocol is huge."

And I am sitting here thinking about something nobody seems to be writing about.

I build AI tools and content for a living.

I have watched every major AI announcement since GPT-3 dropped in 2020.

I know the difference between a slide deck flex and something that actually changes how I work next Monday.

The MCP servers announcement from Google Cloud NEXT '26 is the second kind.

And almost no one is talking about it.

before i explain -- let me tell you what i used to do

Every time I wanted an AI agent to talk to an external service -- a database, a security dashboard, a calendar -- I had to build the bridge myself.

Custom API calls. Auth tokens stored somewhere sketchy. Error handling that breaks at 2 AM. A webhook that works perfectly in staging and explodes in production.

I would spend days building the plumbing before I could even start building the thing I actually wanted.

Sound familiar?

That is the tax every developer pays. The invisible work. The part nobody puts in the demo.

what mcp actually is -- no jargon

MCP stands for Model Context Protocol.

Think of it like USB-C for AI agents.

Before USB-C, every device had a different port. You needed a different cable for everything. It was a mess.

MCP is the standardized port. It is the agreed-upon interface that lets AI agents plug into data sources and tools without custom wiring every single time.

Your agent describes what it needs. The MCP server provides it. Securely. Consistently. Without you writing 200 lines of integration code.

Google did not invent MCP. But what they announced at NEXT '26 is something different.

what google just changed

Google announced managed MCP servers -- running natively inside Google Cloud -- for:

Service	What it means for you
Google Security Operations	Agents can query your threat data without custom auth
Google Workspace	Agents read your docs, calendar, email -- securely
BigQuery	Agents run analytics queries as a natural conversation

Before this: you had to build the MCP server yourself, host it, maintain it, handle auth, deal with rate limits, monitor it.

After this: Google runs it. You just point your agent at it and go.

# The old way -- days of work:
# 1. Build OAuth flow for Google Workspace
# 2. Set up token refresh logic
# 3. Write endpoint wrappers for Docs, Calendar, Gmail
# 4. Handle errors, retries, rate limits
# 5. Deploy and monitor forever
# -- 3 days minimum. Ongoing maintenance. --

# The new way -- one line:
agent.connect(mcp_server="google-workspace")
agent.ask("Summarize all unread emails from the last 48 hours and add any deadlines to my calendar")
# Done. In production. Today.

That is not a minor improvement. That is the removal of an entire category of work.

why this matters more than the gemini rename

Everyone is writing about Vertex AI becoming the Gemini Enterprise Agent Platform.

It is a big deal. 200+ models. A2A protocol. No vendor lock-in at the agent layer. I get it.

But here is my honest take:

The rename changes your options. Managed MCP servers change your daily workflow.

The difference between those two things is enormous.

Options sit in a docs page until you need them. Workflow changes land in your backlog on Monday morning.

I counted. The managed MCP server for Google Security Operations alone could replace about 2 weeks of integration work I have personally done in the last year. That is real hours. Real money. Real focus time redirected toward the actual product.

my honest critique -- because hype helps nobody

I want to love this completely. I cannot yet. Here is what I am still watching:

The governance question is unanswered.

Who controls access to the MCP server? Where are the access logs? What happens when an agent reads something it should not?

For teams in regulated industries -- healthcare, finance, legal -- this is not a minor concern. It is a blocker. Google has not given detailed answers yet.

The pricing is still unclear.

"Managed" usually means "metered." I do not know yet if this becomes expensive at scale. Worth watching before you architect your entire product around it.

The vendor dependency is real.

MCP is an open protocol. But Google's managed MCP servers are Google's infrastructure. If you build deep integrations with these, switching costs go up. Eyes open.

what i am building with this

I am an AI content creator. I spend about 3 hours every week on a completely manual process:

Check analytics across 3 platforms
Pull engagement numbers into a spreadsheet
Write a weekly performance summary
Update my content calendar

That is 3 hours of paste, format, paste, format.

With a Gemini agent connected to Google Workspace via managed MCP, I can describe that workflow once and never do it manually again.

Not someday. Now.

That is the part that made me sit up straight during the keynote.

how to start today

If you want to try this yourself:

Step 1: Read the MCP server docs for Google Security Operations -- it is the most mature of the managed offerings right now.

Step 2: Check out the Gemini Enterprise Agent Platform -- specifically the section on managed connectors.

Step 3: Pick one workflow in your current job that involves pulling data from somewhere and summarizing it. That is your first MCP experiment.

Step 4: Watch the Developer Keynote -- the MCP demos are more detailed there than in the opening keynote.

Start with one workflow. One data source. One agent. See if the time savings are real for your specific case before you redesign anything.

the one sentence that sums this up

Managed MCP servers do not make AI more powerful.

They make you more powerful by removing the wall between AI and the data it needs to actually help you.

That is the announcement from NEXT '26 I will still be talking about in 6 months.

What is the one integration you always wanted to build but never had time for the plumbing?

Drop it below. I am genuinely curious what people will do with this.