DEV Community: Ansh Gupta

🚀 Hermes Agent: The Beginning of Autonomous AI Systems

Ansh Gupta — Sun, 24 May 2026 11:12:21 +0000

AI has evolved rapidly over the last few years.

At first, AI systems were only capable of answering questions. Then came advanced chatbots that could generate code, write essays, and hold human-like conversations.

But now, we are entering something much bigger:

🤖 AI systems that can think, plan, and execute tasks autonomously.

That’s where Hermes Agent comes in.

Hermes Agent is not just another chatbot or AI framework.
It represents a shift from conversation-based AI to action-based AI.

And honestly, this shift could completely redefine how humans interact with software in the future. 🌍

🔍 What Is Hermes Agent?

Hermes Agent is an open-source agentic AI system designed to perform multi-step tasks autonomously.

Instead of only generating responses, Hermes Agent can:

🧠 plan objectives
📌 break problems into steps
🛠️ use external tools
🔄 reason through workflows
⚡ adapt dynamically
🎯 execute actions toward a final goal

In simple words:

Traditional AI talks.
Hermes Agent works. ⚙️

⚡ How Does Hermes Agent Work?

Hermes Agent follows an “agentic workflow.”

Instead of giving a single response, it continuously reasons through a task step by step.

For example:

Suppose a user says:

“Research the latest AI trends and create a summarized report.”

Hermes Agent may perform these actions:

🎯 Understand the objective
🗂️ Plan the workflow
🌐 Search for information
📊 Analyze collected data
📝 Summarize important insights
✅ Generate the final report

This is called multi-step autonomous reasoning.

Unlike traditional AI systems, Hermes Agent does not stop after one reply.

It keeps working until the objective is completed. 🚀

⚔️ Traditional AI vs Hermes Agent

Traditional AI Systems	Hermes Agent
💬 Generates responses	⚙️ Executes tasks
🔹 Single-step interaction	🔄 Multi-step workflows
💤 Mostly reactive	🎯 Goal-oriented
📉 Limited reasoning chain	🧠 Continuous reasoning
⌨️ Depends heavily on prompts	🤖 Can plan independently
🪑 Passive assistant	🚀 Autonomous operator
💭 Conversation-focused	🛠️ Execution-focused

This difference is extremely important.

Hermes Agent changes AI from a “chat interface” into a “digital worker.” 👨‍💻

**_🕰️ Why Older AI Tools Were Limited

Earlier AI tools were powerful for generating text but weak in execution.

For example:

❌ they forgot context easily
❌ they struggled with long workflows
❌ they could not coordinate tools efficiently
❌ they relied heavily on human instructions
❌ they lacked planning ability

Older systems behaved more like calculators for language.

Hermes Agent introduces:

🧠 planning
💾 memory
🔗 tool orchestration
📂 workflow management
⚡ adaptive reasoning

That is why agentic systems feel fundamentally different.

🔄 Hermes Agent Pipeline
The internal pipeline of Hermes Agent can be understood like this:

🧾 Step 1 — User Goal Input

The user gives a high-level objective.

Example:

“Analyze cybersecurity threats and create a report.”

⬇️

🧠 Step 2 — Task Planning

The agent breaks the goal into smaller tasks.

⬇️

⚡ Step 3 — Reasoning Layer

The system decides:

what to do first
which tools to use
how to optimize execution

⬇️

🛠️ Step 4 — Tool Usage

The agent may:

🌐 browse the web
💻 run code
🔌 access APIs
📂 analyze files
📊 retrieve information

⬇️

💾 Step 5 — Memory & Context Handling
The system stores important context during execution.

⬇️

✅ Step 6 — Output Generation
The final result is generated after completing all required steps.

🏗️ Hermes Agent Architecture

Here’s a simplified architecture overview:

🔧 Main Components

1️⃣ Planning Engine

Breaks goals into executable steps.

2️⃣ Reasoning Core

Analyzes decisions and determines next actions.

3️⃣ Tool Integration Layer

Connects with:

🌐 browsers
🔌 APIs
🗄️ databases
💻 code environments

4️⃣ Memory System

Maintains workflow context across multiple stages.

5️⃣ Execution Layer

Performs actions until the objective is achieved.

This architecture allows Hermes Agent to behave more like an autonomous system rather than a static chatbot. 🤖

🎓 Why Hermes Agent Matters for Students

Hermes Agent could completely transform learning.

Students can use agentic systems for:

📚 research assistance
💻 coding help
🗓️ personalized study plans
⚙️ project automation
📊 data analysis
📝 note summarization
🚀 productivity management

Imagine an AI system that not only explains concepts but also:

collects resources
organizes information
creates schedules
generates reports
assists in projects autonomously

That would save enormous amounts of time. ⏳

For students, this means:

less repetitive work and more focus on creativity, learning, and innovation. ✨

🌍 How Hermes Agent Could Change the World

The impact goes far beyond students.

Agentic AI systems could transform:

🏥 healthcare
💰 finance
🔐 cybersecurity
📖 education
🏢 business automation
💻 software engineering
🔬 scientific research

Future systems may no longer require humans to manually coordinate every workflow.

Instead:

👨 Humans define goals
🤖 AI handles execution

This could dramatically increase productivity worldwide. 🚀

🌍 Real-World Example: How Hermes Agent Can Work in Practice

Let’s understand Hermes Agent with a practical scenario.

Imagine a student wants to research “Impact of Artificial Intelligence in Education” for a college assignment.

Instead of just giving a basic answer like a chatbot, Hermes Agent can handle the entire workflow step-by-step:

🧠 Step 1: Understanding the Task
The user gives a single instruction:

“Prepare a detailed report on AI in education with recent trends and examples.”

Hermes Agent first understands the goal and breaks it into smaller tasks.

🔍 Step 2: Planning the Workflow

It creates a plan like:
Search recent information on AI in education
Identify key benefits and challenges
Collect real-world examples
Organize structured sections for a report

🌐 Step 3: Information Gathering

Instead of relying only on pre-trained knowledge, it actively gathers updated information from sources, ensuring the content is relevant and recent.

📊 Step 4: Analysis & Structuring

It then processes the collected data and organizes it into:

Introduction
Use of AI in classrooms (personalized learning, smart tutoring systems)
Benefits for students and teachers
Challenges (data privacy, over-dependence on AI)
Future scope

📝 Step 5: Final Output Generation

Finally, Hermes Agent generates a complete, well-structured report that the student can directly use or submit with minimal editing.

“Why Hermes Agent is Different from Traditional AI Chatbots”

Traditional AI chatbots are designed to answer questions and generate content based on user prompts. While they are powerful conversational tools, they generally work in a reactive way—waiting for instructions and responding one step at a time. 💬

Hermes Agent takes a completely different approach. Instead of simply responding, it can plan, reason, and execute tasks autonomously. 🧠⚡ It is built to handle complex, multi-step workflows by breaking large goals into smaller actions, using tools when necessary, and adapting its strategy as new information becomes available.

Imagine asking an AI to research a topic. A traditional chatbot might provide a summary based on its existing knowledge. Hermes Agent, however, can actively gather information, analyze findings, organize results, and generate a structured report with minimal human intervention. 🔍📊📝

✨ Key Differences

🔹 Autonomous Task Execution – Completes multi-step objectives without requiring constant guidance.

🔹 Tool Integration – Connects with external tools and services to expand its capabilities.

🔹 Planning & Reasoning – Creates action plans and dynamically adjusts them as tasks evolve.

🔹 Open Source & Self-Hosted – Provides greater transparency, customization, and control for developers.

🔹 Scalable Automation – Ideal for research, software development, productivity workflows, and business automation.

🌟 The Future of AI Agents

As artificial intelligence continues to evolve, we are moving beyond simple chat-based interactions toward intelligent autonomous systems. 🤖🚀 Hermes Agent represents this next step by enabling AI not only to answer questions but also to take action, solve problems, and accomplish meaningful tasks independently.

In a world increasingly driven by automation, Hermes Agent demonstrates how AI can become a true digital assistant rather than just a conversational partner. 🌍✨

The future of AI belongs to systems that can think, learn, adapt, and act. Hermes Agent is not just another chatbot—it is a glimpse into the next generation of autonomous intelligence

hermesagentchallenge #devchallenge #agents #ai #opensource #machinelearning

I happy to share that i have completed this challenge ad grateful for this challenge

Ansh Gupta — Fri, 22 May 2026 14:16:58 +0000

Ansh Gupta

May 22

Google I/O 2026 Wasn’t A Tech Event. It Was Google Declaring War On Traditional Software.

#devops #googlecloud #webdev #google

3 min read

Google I/O 2026 Wasn’t A Tech Event. It Was Google Declaring War On Traditional Software.

Ansh Gupta — Fri, 22 May 2026 13:49:43 +0000

Every year, tech companies promise “the future.”
Most launch incremental updates wrapped in cinematic trailers.
Google I/O 2026 felt different.
This time, Google did not showcase features.
Google showcased a future where AI quietly replaces the way humans interact with computers forever.
And if you watched closely…
you saw the beginning of the post-app era.

The Biggest Lie In AI Right Now

Most people still think AI is a chatbot.
Google just proved that assumption is already outdated.
At I/O 2026, Google introduced something much bigger:
AI that does not wait for instructions.
AI that understands intent.
AI that takes action.
AI that works continuously in the background.
That changes the internet completely.

Gemini Is No Longer “An AI Model”

It’s becoming an execution layer for Google’s entire ecosystem.
That’s the real story nobody is talking about.
Google integrated Gemini deeper into:

Android
Chrome
Workspace
Search
Gmail
YouTube
Maps

This matters because the next AI winner will not be the smartest company.
It will be the company closest to user behavior.
And nobody owns more digital behavior than Google.

The Most Important Launch Wasn’t The Smartest Model

Everyone expected bigger benchmarks.
Google delivered faster and cheaper intelligence instead.
And honestly?
That may become the smartest move of the entire AI race.
Because developers do not scale benchmark screenshots.
Developers scale economics.

The Shift Developers Must Understand

Old AI strategy

ask()
respond()
end()

New AI strategy:
**_

observe()

reason()
act()
improve()
repeat()
_**
That tiny difference changes software forever.

Because now AI systems can:

manage workflows
operate tools
automate tasks
coordinate apps
monitor context
execute decisions
adapt continuously

without waiting for humans every five seconds.
This is the real meaning of the “Agent Era.”
And most startups still build like it’s 2023.

Search Just Entered Its Most Dangerous Phase

Google called this the biggest transformation in Search in decades.

Most people heard “AI summaries.”

Smart people heard something else:

Google wants answers to replace websites.

That single shift threatens entire industries.

Because traditional search created winners through:

clicks
rankings
SEO
backlinks
content farms

Agentic search changes the model completely.
Now the flow becomes:
Old Internet
User → Search → Website → Action
New Internet
User → AI → Outcome
The website disappears from the center of the experience.
That is a terrifying sentence for half the internet economy.

Google’s Real Advantage Is Not AI

OpenAI has incredible models.
Anthropic has elite reasoning.
Meta has open-source scale.
But Google has something more powerful than all of them combined:
infrastructure + distribution + data + ecosystem
That combination is brutal.
Because Google can deploy AI instantly across billions of devices.

No startup can replicate that distribution layer.
And history shows one thing clearly:
Platforms beat products.
Every single time.

The Most Underrated Announcement Of I/O 2026

Multimodal real-time AI.

This changes everything.

Your phone no longer becomes:

an app launcher
a browser
a search tool
It becomes a live intelligence interface.
You speak.
It understands.
It sees.
It acts.
No friction.
No navigation.
No menus.
That sounds futuristic.
But Google demonstrated pieces of this future already working.

Most Developers Are Still Thinking Too Small

People ask:
“How do I build an AI app?”
Wrong question.
The new question is:
“What human work disappears because AI exists?”
That’s where the next billion-dollar companies emerge.
Not from wrappers.
Not from prompt templates.
From workflow replacement.

Here’s The Scary Part Nobody Wants To Admit

As AI agents improve…

software itself becomes less visible.
Users stop caring:

which app runs
which API responds
which model generates output

They only care about:

speed
reliability
outcomes
This destroys traditional software moats.
The interface layer weakens.
The intelligence layer dominates.

The New Startup Formula After Google I/O

Small teams.
Massive leverage.
AI-native operations.

One developer with AI agents may soon outperform entire departments from five years ago.
That is not hype anymore.
That is economics.
Final Thought

Google I/O 2026 did not feel like a product keynote.

It felt like watching the operating system of the future assemble itself in public.

And the message became painfully clear:
The companies that survive the AI era
will not be the ones that use AI.
They will be the ones rebuilt around it.

Gemma 4 Isn’t Just Another AI Model — It’s A Shift In How We Build AI

Ansh Gupta — Wed, 20 May 2026 15:28:20 +0000

🧠What is Gemma 4?

Artificial Intelligence is evolving fast, and every few months we see a new model promising better performance, bigger benchmarks, and smarter capabilities.

But not every model release actually changes how developers build.

That’s why Google Gemma 4 stands out.

Gemma 4 is Google’s latest open AI model designed to make advanced AI more accessible, efficient, and practical for real-world development. Instead of focusing only on larger model sizes and benchmark competition, Gemma 4 represents a shift toward lightweight, deployable, and developer-friendly intelligence.

For developers, this is important because it opens the door to building smarter applications without depending entirely on massive infrastructure or expensive closed APIs.

More than just another AI release, Gemma 4 signals a bigger transition in the AI ecosystem — from simply using AI as a tool to building systems where AI becomes part of the architecture itself.

And that’s what makes it worth paying attention to.

1.🤖_ Before Gemma 4_
Traditional architecture
User Input
↓
Backend Logic
↓
Fixed Workflow
↓
LLM Call
↓
Response

⚙️how it work?

.Workflow is fixed
.AI is used only at the end
.System Decide Step
.Harder to addapt new situation
.change require code update

2.🤖After Gemma 4
Agentic architecture
User Goal
↓
Gemma 4 Understands Context
↓
Reasons About The Task
↓
Chooses Tools / APIs
↓
Decides Next Action
↓
Generates Output
↓
Adapts Based On Results

Workflow after🧩

.A dynamic sequence of decision by the agent
.Example: understand-plan-act-learn-adapt
.The agent decide the workflow

.We’ve been thinking about AI in the wrong way
For a while, I also looked at AI models like this:
bigger model = better model
That’s how most of us think.
.If a model has insane benchmark numbers, huge context windows, and needs heavy infrastructure, we automatically assume it’s the future.
But lately it feels like the industry is moving in another direction.
The real question is no longer:
“Which model is the biggest?”
It’s:
“Which model can developers actually build useful things with?”
.That’s where Gemma 4 feels important.
.What caught my attention about Gemma 4
.What stands out to me is that Google seems to be focusing on something practical.
Not just raw power.
But usable power.
.🛜That matters because most developers are not sitting on massive GPU clusters.
A lot of us are:
👨‍💻students
.indie builders
.hackathon participants
.people experimenting late at night with random ideas
We don’t need a model that wins every benchmark
We need something we can actually work with.
.Something flexible enough to build real projects.
Gemma 4 feels like it’s aiming for that.
.This changes how I think about building AI projects
Before this, my idea of using AI in projects was pretty simple.
The architecture usually looked like:
App logic → API call → AI response
Basically, AI was just the last step.
You build everything first, then call the model for some smart output.
.But models like Gemma 4 make me think differently.
What if AI isn’t just the response layer?
What if it becomes part of the decision-making layer itself?
That changes everything.
.Instead of hardcoding every workflow, systems can reason through context and decide what should happen.
.That feels like a much bigger shift than people are talking about.

🔧Reimagining AI Apps With Gemma 4

So I started thinking:
What if Gemma 4 wasn’t just a model… but the reasoning layer of the entire app?

⚙️Workflows → become decisions Today: user_input → backend logic → workflow → LLM → output The system already knows: what to do which step comes next which API gets called AI only generates the response. With Gemma 4: user_goal → Gemma 4 reasons → decides actions → uses tools Same request. Completely different architecture. The workflow stops being fixed. It becomes adaptive.
Prompts → become context systems
Today, most apps work like this:
collect data
inject into prompt
send to model
generate answer
Basically:
“here’s context, now respond”
With Gemma 4-style systems:
understand the situation
decide what context matters
retrieve relevant iformation
reason before acting
Instead of:
“generate text”
It becomes:
“understand what should happen”
That feels much closer to intelligent software.
APIs→ become tools for the agent
Traditional systems:
if task = weather:
call weather API
if task = finance:
call finance workflow
Everything is predefined.
Agent-style systems with Gemma 4:
analyze intent
choose tools dynamically
decide execution order
adapt based on results
The model is no longer just answering.
It’s coordinating actions.

4.RAG → becomes reasoning memory
Normally RAG is treated like:
retrieve documents → attach to prompt
Simple pipeline.
But with Gemma 4, retrieval can become part of reasoning itself.
The model can:
decide what information matters
ignore irrelevant context
refine searches dynamically
build understanding progressively
That’s a very different way of thinking about AI systems.

The real shift: AI becomes part of the architecture I think this is the biggest thing people are missing. Gemma 4 isn’t interesting only because it’s another open model. It’s interesting because it pushes developers toward: AI-native architecture Where intelligence is not added at the end. It exists inside the system itself _**

1.🧠How Gemma 4 Could Change Hackathons Forever

**_

Every hackathon starts the same way.
Someone says:
“Let’s add AI.”
And usually that means:

calling an API
generating responses
adding a chatbot UI
hoping it feels innovative

But after looking into Google Gemma 4, I think hackathons are about to change in a much bigger way.
Not because AI models are getting larger.
But because they’re becoming more usable for smaller teams.
And honestly, that changes everything.
For years, powerful AI development felt limited to startups with huge budgets, cloud infrastructure, and dedicated ML engineers.
Most hackathon teams simply glued APIs together and prayed the demo wouldn’t crash before judging ended.
But Gemma 4 changes the equation
Smaller, faster, and more efficient open models mean students, indie developers, and weekend builders can now experiment with ideas that previously felt impossible.
Instead of building “AI wrappers,” teams can focus on creating real products.
Imagine a 24-hour hackathon where teams can build:

AI medical assistants
offline educational tools
intelligent coding systems
smart research agents
real-time language translators
personalized productivity platforms without needing enterprise-level infrastructure. That’s the shift people are underestimating.

Hackathons are no longer becoming competitions about who can connect APIs the fastest.
They’re slowly turning into environments where small teams can prototype products that actually feel production-ready.
And that changes creativity itself.
Because when infrastructure becomes lighter, imagination becomes bigger.

I think the next generation of hackathon winners won’t just be the teams with the best frontend or the fanciest pitch deck.

They’ll be the teams that know how to combine efficient AI models like Gemma 4 with strong product thinking and rapid execution.
The barrier to building intelligent software is dropping fast.
And the teams who understand this early are going to build things that feel impossible today.

2.🪖How Gemma 4 Could Help the Army and Defense Sector🪖

1.Faster Intelligence Analysis
Gemma 4 can quickly analyze:
Large intelligence reports
Satellite imagery
Communication data
Mission information
This could help defense teams make faster and smarter decisions.
2.AI-Powered Military Training
Gemma 4 could assist in:
Tactical simulations
Language translation
Mission preparation
Training support systems
This may improve learning speed and operational readiness for soldiers.
Gemma 4 could assist in:
Tactical simulations
Language translation
Mission preparation
Training support systems
This may improve learning speed and operational readiness for soldiers.

3.☠️ Why Gemma 4 Is Dangerous☠️

Most AI models still depend heavily on massive cloud infrastructure.

Gemma 4 changes that.

Its lightweight architecture allows developers to run powerful AI systems with lower cost, faster speed, and more flexibility.

That means AI no longer stays limited to giant tech companies.

Now students, indie developers, and startups can build advanced AI products without huge infrastructure budgets.

| Traditional AI  | Gemma 4              |
| --------------- | -------------------- |
| Cloud dependent | Local deployment     |
| Expensive APIs  | Affordable AI        |
| Limited access  | Open experimentation |

Traditional AI Flow

User → Cloud → Data Center → Response

Gemma 4 Flow

User → Local Device → Gemma 4 → Instant AI

from transformers import pipeline

ai = pipeline(
    "text-generation",
    model="google/gemma-4"
)

print(ai("Why is lightweight AI important?"))

The dangerous part is not just intelligence.
The dangerous part is accessibility.
Because once powerful AI becomes lightweight enough to run everywhere…
innovation becomes impossible to control.

4.🚀 Why This Matters More Than Benchmarks

For years, the AI industry has been obsessed with one thing: benchmarks 📊

Which model scores higher.
Which model solves more tasks.
Which company owns the biggest infrastructure.

But Gemma 4 represents a shift in something far more important than benchmark numbers.

It changes where AI can exist 🌍

Traditional large AI systems depend heavily on:

massive cloud infrastructure ☁️
expensive GPUs 💰
constant internet access 🌐

That model works for large corporations — but not always for developers, startups, students, or smaller teams.

Gemma 4 points toward a different future ⚡

AI that is:

lightweight 🪶
deployable 🚀
efficient ⚙️
accessible 🌎

This matters because the next major AI revolution may not come from building larger models.
It may come from making AI available everywhere.

💻 On laptops
📱 On phones
🌐 Inside browsers
🖥️ Inside operating systems
📦 Inside offline applications

The real breakthrough is not just intelligence.
It is portability 🔥
Benchmarks measure performance in controlled environments.
But real-world impact is measured by:

accessibility 🌍
deployment 🚀
latency ⚡
affordability 💸
usability 🧠

A model that can run efficiently for millions of developers may ultimately matter more than a model that is slightly smarter but locked behind expensive infrastructure.

That is why Gemma 4 feels important.

It suggests the future of AI may be less about centralized intelligence 🏢
—and more about distributed intelligence 🌐

    OLD AI ERA 🏢
┌─────────────────────────────┐
│ Bigger Models 📈            │
│ Massive Data Centers 🖥️     │
│ Expensive GPUs 💰           │
│ Cloud Dependency ☁️         │
│ High Cost 💸                │
│ Limited Accessibility 🚫    │
└─────────────┬───────────────┘
              │
              ▼
        NEW AI ERA 🚀
┌─────────────────────────────┐
│ Smaller Efficient Models ⚡ │
│ Local Deployment 💻         │
│ Edge AI 📱                  │
│ Faster Inference 🚀         │
│ Lower Cost 💵               │
│ Wider Accessibility 🌍      │
└─────────────────────────────┘

5. 📊 Benchmark Results That Actually Matter

Gemma 4 isn’t just competing — it’s outperforming models much larger than itself across reasoning, coding, mathematics, and multimodal tasks.

Some standout numbers:

89.2% on AIME 2026 → insane mathematical reasoning
80% on LiveCodeBench v6 → strong competitive coding performance
76.9% on MMMU Pro → impressive multimodal understanding
1452 Arena AI score → pushing close to frontier proprietary models

The crazy part?
Gemma 4 31B is achieving these results while staying relatively lightweight compared to giant closed-source systems.
This is where Gemma 4 stops feeling like an ‘open-source alternative’ and starts feeling like a serious frontier model.

🚀 6.Final Thoughts

Gemma 4 isn’t just another AI model — it’s a shift toward smarter, faster, and more accessible AI. Instead of depending only on massive cloud systems, it brings powerful intelligence closer to real users through efficient and deployable AI.
What truly matters isn’t just benchmarks — it’s usability. ⚡
Lower costs, faster deployment, better privacy, and AI that can run almost anywhere.
The future of AI won’t belong only to the biggest models.
It will belong to the models people can actually build, run, and innovate with.
And that’s exactly where Gemma 4 changes the game. 🤖✨

Delay in result Google's next gen 26 writting result

Ansh Gupta — Thu, 07 May 2026 13:27:10 +0000

Now today is the date of announcement result but still not come I am disappoint from Dev community 😞

GFG technical scripter winner 🏆

Ansh Gupta — Sun, 03 May 2026 09:57:30 +0000

🧿💫😄Excited to share that I’ve secured Rank 33 and a position among the Top 50 performers in Technical Scripter 2026 (GFG Connect Edition). 🚀
Grateful to have received a cash reward 💰 as recognition for consistent effort and learning.
This journey reinforced one key lesson — showing up every day matters more than anything else.
Looking forward to pushing my limits even further. 🎯

GeeksforGeeks #GFGConnect #Consistency

[Boost]

Ansh Gupta — Sun, 03 May 2026 09:54:47 +0000

TCS HACKATHON WINNER 2026 🏆

Ansh Gupta — Fri, 01 May 2026 17:10:28 +0000

Proud Moment for Me! 🏆
I am thrilled to share that I have won the AI Track at Hackathon 2026 (AI & Cyber Security Challenge) organized at Maharana Pratap Institute of Technology, Gorakhpur, supported by TCS Foundation (Tata Group).
It was truly an honor to receive the certificate and prize from the Hon’ble Chief Minister of Uttar Pradesh, Yogi Adityanath, and Natarajan Chandrasekaran, Chairman of Tata Group.
This achievement means a lot to me as a B.Tech (2nd Year) student at MPIT, and it motivates me to keep pushing my limits in the field of AI and technology.
I would like to express my heartfelt gratitude to our respected Director, Sudhir Agrawal Sir, and our HOD, Sachchidanand Chaturvedi Sir, for their constant support, guidance, and encouragement throughout this journey. 🙏
Grateful for this opportunity and looking forward to achieving many more milestones ahead! 🚀
MAHARANA PRATAP INSTITUTE OF TECHNOLOGY GORAKHPUR
Sachidanand Chaturvedi

Hackathon2026 #AI#TCS #TataGroup

ProudMoment #StudentAchievement #Innovation #Gratitude #TechJourney

Google Cloud Next '26 — One Announcement That Actually Got Me Excited

Ansh Gupta — Tue, 28 Apr 2026 12:25:16 +0000

I'll be honest. I usually skim through cloud conference announcements. Most of them feel the same — new model, new chip, big numbers, move on.
But something at Google Cloud Next '26 genuinely caught my attention, and I want to talk about it.

The Agent Platform Thing Is Real This Time
Google announced the Gemini Enterprise Agent Platform — basically a complete setup to build, deploy, and manage AI agents inside your organization.
Now I know "AI agents" has become a buzzword that means everything and nothing. But what stood out to me here was one specific feature: agents now have persistent memory per project.
Think about it. "I actually built a medical AI tool called MediScan at HackIndia earlier this year. One thing that frustrated me throughout was that the AI had zero memory between sessions — every time we tested, we had to re-feed all the patient context from scratch. When Google announced persistent memory for agents, that was the exact pain point they were solving". Every AI tool I've used so far forgets everything the moment the session ends. You explain your project, your codebase, your preferences — and next time, you start from zero. It's exhausting.
With Projects, agents remember. Your context stays. That's not a small thing. That's the difference between a fancy autocomplete and something that actually feels like a working tool.

The Hardware Split That Makes Sense
Google also announced their 8th generation TPUs — but what's interesting is they made two separate chips this time.
One for training (TPU 8t), one for inference (TPU 8i).
For a long time these were the same chip doing two very different jobs. Google basically admitted: training and inference are different problems, they need different hardware. That's an honest engineering decision and I respect it.

My Honest Take
Is everything perfect? No.
Governance and security for agentic systems is still going to be a headache. Deploying agents inside a real company with compliance requirements is way harder than any keynote demo makes it look.
But the direction feels right. Less "look at our cool demo", more "here's how you actually run this in production."
That's what I've been waiting to see from a cloud platform.

If you're a developer, I'd say: don't wait for this to be perfect. Pick one boring, repetitive workflow in your work and try building an agent for it. That's where this stuff actually proves itself.