DEV Community: Anmol Pawar

🐶 Can AI Understand Your Dog? Meet Dogesh — Your Dog, Decoded.

Anmol Pawar — Sun, 16 Aug 2026 17:38:50 +0000

*This is a submission for Weekend Challenge: Dog Days Edition

What I Built

Dogesh is an AI-powered dog behavior interpreter that helps people understand what their dog may be communicating through visible body language.

Users can upload a photo of a dog, and Dogesh uses Google Gemini to analyze the image for visible clues such as facial expression, eyes, ears, mouth, posture, and overall body language.

Instead of returning a fixed or fake mood, Dogesh generates a dynamic analysis based on the uploaded image. It provides:

🐶 The most likely current emotional state
📊 AI confidence
🐾 Visible behavioral clues
⚠️ A warning when visible behavior may suggest fear, aggression, discomfort, or possible illness

Dogesh is designed as a fun but responsible way to help people look more closely at their dog's body language. It is visual interpretation, not a medical diagnosis.

Demo

https://youtu.be/Q5eiGpyXtBY?si=OKrFHSSMOSKLciAQ

You can also include a short screen recording showing:

Uploading a dog photo
Clicking "Analyze this dog"
Gemini analyzing the image
Dogesh displaying the detected state, confidence, visible clues, and warning

Code

https://github.com/AI-Explorer786/dogesh-your-dog-decoded

How I Built It

Dogesh was built with Next.js, TypeScript, and Google Gemini.

The application uses a simple image-analysis pipeline:

The user uploads a dog photo through the frontend.
The selected image is sent to a Next.js API route.
The API converts the image into a format that can be provided to Gemini.
Google Gemini analyzes the actual uploaded image.
Gemini evaluates visible features including facial expression, eyes, ears, mouth, posture, tail when visible, and overall body language.
The model returns structured JSON containing the predicted state, confidence, explanation, visible clues, and optional warning.
The frontend displays that result as the Dogesh AI Report.

One important design decision was to avoid hard-coded responses. Earlier prototype behavior could return the same mood regardless of the uploaded image, so the final version uses Gemini's actual vision analysis to generate the result from the image itself.

Dogesh also avoids forcing an emotional label when there is not enough visual evidence. The model can return "Unclear" instead of inventing an emotion.

Tech Stack

Next.js
TypeScript
React
Google Gemini / Google AI
Next.js API Routes
Tailwind CSS
Browser image upload APIs

Prize Categories

Best Use of Google AI

Dogesh uses Google Gemini to perform the core image understanding and dog behavior interpretation.

Gemini analyzes the actual uploaded dog image and produces structured information about the dog's visible body language, including the likely emotional state, confidence, supporting clues, and potential warning signs.

#InternationalDogDay #DEVWeekendChallenge #GoogleAI #Dogesh #TypeScript

🐛 When My Autonomous AI Agent Refused to Cooperate

Anmol Pawar — Fri, 14 Aug 2026 17:23:16 +0000

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry.

I thought building an autonomous research assistant would be the exciting part.

I was wrong.

The real adventure didn't start when I wrote the prompt—it started when I tried to get Hermes Commander (my research assistant powered by Gemini) running locally. On paper, it was simple:

       "Task"⟶"Planning"⟶"Tool Execution"⟶"Result"

Then reality hit, and my codebase turned into a chaotic battlefield.

🎭 Act 1: The Invisible Environment Wall

Before Hermes could even think, the underlying infrastructure decided to test my sanity.

I spent hours untangling WSL and Ubuntu environment setup issues just to get the base dependencies running. And the moment I thought I was in the clear, the system hit me with a cold, immediate wall:

HTTP 429 — RESOURCE_EXHAUSTED

The Gemini API quota was completely wiped out.

The Confusion: When an AI agent stops responding, your instinct is to think: "The LLM prompt is broken!" But the actual culprit was a silent API throttling issue hiding behind the scenes.

🎭 Act 2: The Cascade of Falling Dominoes

Once the API quota settled, I gave Hermes its first real research task. That’s when the rabbit hole opened into pure chaos:

🌐 The Phantom Tool: Hermes tried to invoke web_search, only to discover the driver was completely missing from the execution context.

🖥️ Terminal Panic: Trying to adapt, it resorted to raw CLI commands (curl and grep). The terminal instantly filled with unparsed, chaotic HTML junk.

🤖 UI Flakes & Missing Skills: Browser automation kept stumbling on DOM elements, and the agent was missing the autonomous-ai-agents skill context needed to recover gracefully.

🐋 The Docker Layer: To top it off, WSL and Docker networking issues started dropping local connections mid-execution.

It wasn't just one big error—it was a stack of five small, sneaky failures hiding inside each other.

🎭 Act 3: Unstacking the Error Chain

Sitting in front of a frozen terminal, I realized I was fighting two battles: the AI itself, and the broken pipeline around it.

Instead of treating it as one mysterious "Hermes isn't working" bug, I separated the system into distinct layers:

The Endpoint Layer: Hard-stopped API retries on HTTP 429 errors instead of letting the agent burn quota in infinite loops.
The Tooling Layer: Ensured tool availability is validated before the planner assumes it can use them.
The System Layer: Fixed Docker/WSL network bindings so CLI scraping doesn't return garbage.

💡 Act 4: The Ultimate Lesson & Harmony Restored

Debugging Hermes Commander completely changed how I look at AI engineering:

Building an autonomous agent isn't just about making the AI smart. It's about making the system around it indestructible.

When an agent fails, don't just blame the model. Debug the chain. Check the environment, check the drivers, and isolate the quota.

Hermes Commander started as a chaotic mess of failing scripts, but untangling those stacked layers brought harmony back to the codebase — and taught me how real agentic systems behave in the wild! 🚀🐛🔨

#bugsmash #AI

🛠️Building My First AI Agent with Hermes Agent 🤖

Anmol Pawar — Sat, 30 May 2026 23:56:18 +0000

*This is a submission for the [Hermes Agent Challenge]

When I first heard about agentic AI systems, I imagined something much more capable than a traditional chatbot.

A chatbot answers questions.

An AI agent plans, reasons, uses tools, and works toward a goal.

That idea led me to explore Hermes Agent and build my first autonomous AI project: Hermes Commander.

Why I Chose Hermes Agent

As someone interested in AI, prompt engineering, and autonomous systems, I wanted to understand how modern AI agents actually work.

Most tutorials focus on prompts and conversations. Hermes Agent felt different because it provides a framework for building systems that can use tools, navigate workflows, and perform multi-step tasks.

I wanted to move beyond simple chat interactions and experiment with agentic behavior.

Setting Up Hermes Agent

My journey was not completely smooth.

I installed Hermes Agent locally, configured providers, experimented with Gemini models, and connected browser capabilities.

Along the way I encountered:

Provider configuration issues
Authentication challenges
API quota limitations
Model selection decisions

While these issues were sometimes frustrating, they helped me understand how real-world AI systems operate beyond simple demos.

One of the biggest lessons was that building AI agents involves infrastructure, tooling, and workflow design—not just prompts.

What Makes Hermes Agent Interesting?

The most interesting aspect of Hermes Agent is its focus on actions instead of only responses.

For example, the agent can:

Create plans
Manage tasks
Access tools
Navigate browser environments
Execute multi-step workflows

This shifts the experience from:

"Ask a question, get an answer"

"Give a goal, let the agent determine how to approach it."

That difference is what makes agentic systems exciting.

Building Hermes Commander

To explore these capabilities, I created Hermes Commander.

Hermes Commander is an autonomous research assistant designed to:

Understand research goals
Generate structured plans
Organize tasks
Use available tools
Assist with technical investigations

One of my favorite moments was seeing the agent generate a complete research workflow for investigating AI agent frameworks.

Instead of immediately answering, it first created a plan, organized tasks, and structured the work.

That felt much closer to working with an assistant than a traditional chatbot.

Lessons Learned

Building my first AI agent taught me several important lessons:

1. Tool Use Matters

An AI agent becomes far more useful when it can interact with tools and external systems.

2. Planning Is Powerful

The ability to break a large goal into smaller tasks is one of the most valuable agent capabilities.

3. Infrastructure Matters

Provider setup, quotas, APIs, and environment configuration are all critical parts of the development process.

4. Agentic AI Is Still Evolving

We are only beginning to see what autonomous AI systems can accomplish.

Looking Ahead

My next goal is to continue improving Hermes Commander and explore local model support through Ollama.

I am particularly interested in building agents that can:

Perform deeper research
Generate reports
Maintain memory
Coordinate multiple workflows

Hermes Agent provided an excellent introduction to this space and helped me take my first practical step into agentic AI development.

Final Thoughts

Building Hermes Commander was more than a technical project.

It was an opportunity to understand how AI agents think, plan, and interact with the world through tools.

The experience reinforced my belief that the future of AI is not only about better conversations.

It is about systems that can take action.

And for me, Hermes Agent was the starting point of that journey.

AutonomousAI #TechInnovation #HermesCommander #AgenticAI

Hermes Commander: An Autonomous Research Assistant Powered by Hermes Agent 🧠

Anmol Pawar — Sat, 30 May 2026 23:47:52 +0000

*This is a submission for the [Hermes Agent Challenge]

What I Built

Hermes Commander is my first AI agent project built using Hermes Agent.

The goal of this project was to explore how an AI agent can go beyond simple question-answering and perform structured tasks using tools. Hermes Commander acts as an autonomous research assistant that can understand requests, create research plans, access available tools, and organize workflows.

As someone interested in AI, prompt engineering, and research, I wanted to build an agent that could help investigate technical topics in a more structured and agentic way.

Current capabilities include:

Agent identity and interaction
Agentic task planning
Tool discovery and management
Research workflow generation
Browser-assisted information gathering
Structured task execution

This project represents my first step into building autonomous AI systems and serves as the foundation for future research-focused agents

Demo

https://www.youtube.com/watch?v=YatyrfyPq_U
Screenshots

Agent Introduction

Shows Hermes Commander identifying itself as an AI agent.

Agentic Capabilities

Demonstrates how the agent understands and explains its autonomous workflow.

Tool Access

Displays the tools available to the agent for planning and task execution.

Research Planning

Shows the agent creating a structured research plan for investigating AI agent frameworks.

Browser Navigation

Demonstrates browser-assisted navigation and information analysis.

Code

GitHub Repository:
https://github.com/AI-Explorer786/Hermes-Commander

My Tech Stack

Hermes Agent
Google Gemini
Ubuntu (WSL)
Browser Automation Tools
GitHub
Markdown Documentation

How I Used Hermes Agent

Hermes Agent serves as the core engine behind Hermes Commander.

I used Hermes Agent to:

Create structured research plans
Manage task workflows
Access and utilize available tools
Interact with browser capabilities
Execute agent-driven reasoning processes

One of the most interesting aspects of Hermes Agent is its ability to act as more than a chatbot. Instead of only generating responses, it can plan actions, select tools, and perform multi-step workflows.

For this project, I focused on agentic capabilities such as:

Autonomous planning
Tool awareness
Workflow generation
Browser-assisted exploration
Research task organization

This allowed me to transform a standard AI interaction into a more agent-oriented experience where the system can reason about tasks and determine how to approach them.

Future Improvements

Future versions of Hermes Commander will include:

Local model support through Ollama
Advanced autonomous research workflows
Multi-step web research and summarization
Report generation
Knowledge memory and retrieval
Enhanced research automation

Building Hermes Commander was an exciting learning experience and my first practical step toward creating more capable AI research agents.

HermesAgent #AgenticAI #HermesCommander #BuildInPublic

Why Gemma 4 Feels Like an Important Moment for AI Developers✨

Anmol Pawar — Sun, 24 May 2026 19:32:18 +0000

*This is a submission for the [Gemma 4 Challenge: Write About Gemma 4]

Over the past few months, I’ve been spending a lot of time exploring AI development through hackathons, Flask-based AI experiments, prompt engineering workflows, and developer tools surrounding large language models.

And during that process, I noticed something interesting.

Most people interact with AI through applications like ChatGPT or Gemini, but far fewer understand the ecosystem powering those experiences — the models themselves, cloud infrastructure, open-model ecosystems, GPUs, inference pipelines, and developer-focused tooling behind modern AI systems.

That curiosity is what eventually led me to explore Gemma 4.

From AI Apps to AI Models

One of the biggest things I realized while exploring modern AI tools is how easy it is to confuse AI applications with the actual models behind them.

For a while, I even found myself mixing up Gemini and Gemma because most online discussions focus heavily on the user-facing side of AI rather than the underlying developer ecosystem.

But eventually the distinction became much clearer:

ChatGPT is an application powered by GPT models
Gemini is Google’s AI assistant powered by Gemini models
Gemma, however, is part of Google’s open model ecosystem designed more directly for developers and experimentation

That realization completely changed how I viewed modern AI development.

Instead of seeing AI only as polished chatbot interfaces, I started seeing the larger ecosystem behind them — one built around models, infrastructure, experimentation, and developer workflows.

Why Gemma 4 Caught My Attention

What initially drew my attention toward Gemma 4 wasn’t just model performance.

It was the broader conversation surrounding it.

While exploring AI workflows online, I came across discussions involving:

local AI experimentation
serverless GPU infrastructure
NVIDIA-powered cloud environments
model fine-tuning workflows
rapid prototyping with open models

That ecosystem felt incredibly different from the way AI is usually presented to everyday users.

Instead of AI feeling like a closed system controlled only by major companies, open models like Gemma 4 made the space feel more accessible to independent developers, students, researchers, and hackathon teams.

As someone actively experimenting with AI projects and developer tools, I found that shift genuinely exciting.

The Growing Accessibility of AI Development

One thing that stands out about the current AI ecosystem is how quickly experimentation is becoming more accessible.

A few years ago, many advanced AI workflows felt distant from smaller developers.

Today, developers can:

prototype AI applications rapidly
experiment with open models
test reasoning workflows
explore local inference
integrate AI into smaller products
learn through practical experimentation

Even the conversations around modern infrastructure — from cloud deployment pipelines to GPU acceleration — are becoming more visible and approachable to developers outside large research organizations.

That accessibility matters.

Because innovation often starts with experimentation.

Why Open Models Matter

What makes open models particularly interesting is the freedom they create for builders.

During hackathons and AI project exploration, I’ve noticed how much faster developers learn when they can directly experiment with prompts, models, workflows, and integrations themselves.

That freedom encourages curiosity.

And curiosity is an important part of learning modern AI systems.

Instead of only consuming AI products, developers can now better understand:

how models behave
how prompts influence outputs
how infrastructure supports AI workloads
how AI systems can be integrated into real applications

To me, that’s one of the most exciting aspects of the current AI landscape.

AI Feels More Buildable Than Before

What surprised me most while exploring Gemma 4 and the surrounding ecosystem is that AI development feels far more approachable than it initially seemed from the outside.

The more I explored:

open models
cloud GPU workflows
developer tooling
rapid prototyping environments

the more AI started feeling less like a distant research field and more like something developers can actively experiment with and build around.

And I think that shift is important.

Because the future of AI will not only be shaped by large companies or research labs.

It will also be shaped by students, independent developers, open-source contributors, startup builders, and curious people experimenting with ideas for the first time.

Final Thoughts

Exploring Gemma 4 helped me better understand that modern AI development is much bigger than consumer chatbots.

Behind every polished AI interface is a rapidly evolving ecosystem of models, infrastructure, experimentation, and developer innovation.

And what excites me most is that this ecosystem is becoming increasingly accessible.

Open models like Gemma 4 are helping more people move from simply using AI tools to actually understanding, experimenting with, and building around them.

For developers, that may end up being one of the most important shifts in the entire AI space.

opensource #googleai #developers #gemma #machinelearning

🚀 “From Prompts to Autonomous Agents: What Google I/O 2026 Changed”

Anmol Pawar — Fri, 22 May 2026 14:08:42 +0000

From Prompts to Autonomous Agents: What Google I/O 2026 Changed

This is a submission for the Google I/O Writing Challenge

What if the future of AI isn’t about asking better questions…

…but about AI taking action before we even ask?

That was the biggest feeling I got after watching Google I/O 2026.

For the last few years, most AI tools have behaved like assistants. You type a prompt, ask a question, or upload a file — and the AI responds.

But this year, Google showcased something bigger: systems that can reason, plan, remember context, and complete multi-step workflows with minimal human involvement.

This shift is what many people are calling the Agentic AI Era.

And honestly, it feels like one of the biggest changes in computing since the rise of smartphones.

From “Answer Engines” to “Action Engines”

The simplest way to understand this shift is through an everyday example.

Traditional AI

You ask:

“Find me the best flight to San Francisco.”

The AI gives you:

links
prices
recommendations

But you still compare options, open tabs, book tickets, and organize everything manually.

Agentic AI

Now imagine saying:

“I need to be in San Francisco next Tuesday for a meeting. Find the best flight under my budget, add it to my calendar, and summarize the trip details.”

Instead of simply answering, the AI coordinates tasks and executes them.

That’s the difference.

The future of AI is slowly moving from:

responding to
acting

The Biggest Ideas From Google I/O 2026

1. Gemini 3.5 Flash — Speed Matters More Than Ever

One of the most important announcements was Gemini 3.5 Flash.

At first glance, “faster AI” may not sound revolutionary. But in an agent-driven world, speed becomes critical.

An AI agent may need to:

scan documents
analyze emails
search the web
make decisions
coordinate tools

…all in real time.

Latency breaks the illusion of intelligence.

Fast models like Flash make AI interactions feel less like waiting for software and more like collaborating with an active system.

2. Gemini Omni and the Rise of Multimodal AI

This was probably the most futuristic part of the keynote for me.

Gemini Omni represents a future where AI doesn’t just process text — it understands multiple forms of information together:

video
images
audio
text
environmental context

That changes the relationship between humans and machines completely.

Instead of interacting with AI through isolated prompts, we are moving toward systems that understand situations more holistically.

For example:

understanding visual scenes
reacting to spoken instructions
interpreting live environments
assisting in real-world workflows

This is where AI starts feeling less like a chatbot and more like a collaborative digital partner.

3. Search Is Changing for the First Time in Years

Google Search has historically been built around links.

You search.
You browse.
You collect information manually.

But Google’s newer AI-powered search experience signals a major shift.

Instead of just displaying information, AI can now:

summarize research
organize answers
generate plans
reduce repetitive searching

The interesting part is not convenience.

It’s the idea that search may evolve from:

“finding information”

to:

“helping complete objectives.”

That is a massive change in how people interact with the internet.

4. Android XR and Ambient Intelligence

Another underrated moment from I/O 2026 was Android XR.

The long-term vision here seems clear:
AI is moving beyond phones and becoming part of our environment.

Smart glasses and wearable AI systems could eventually provide:

live translation
contextual navigation
memory assistance
real-time information overlays

Instead of opening apps constantly, AI may become something that quietly exists in the background and assists when needed.

It’s a subtle shift, but an important one.

5. Why This Matters for Developers

As someone learning AI development and experimenting with projects, this part genuinely excites me the most.

The role of developers may slowly evolve from:

building isolated features

to:

orchestrating intelligent systems.

Future applications may include:

multiple AI agents
memory systems
reasoning pipelines
tool integrations
autonomous workflows

In other words:
developers may spend less time designing static interfaces and more time designing intelligent behavior.

That changes software development itself.

Final Thoughts

Google I/O 2026 didn’t feel like a normal product event.

It felt like a preview of a larger transition happening across the AI industry.

The most important takeaway wasn’t simply that models are becoming smarter.

It’s that AI systems are beginning to move beyond conversation and toward execution.

We are entering a world where AI may:

organize information
coordinate tools
automate workflows
assist continuously in the background

The future of AI may not be about replacing humans.

It may be about reducing digital friction so humans can focus more on creativity, decision-making, and meaningful work.

And honestly?

That future feels closer than ever.

“From Narrow AI to Agentic AI: The Shift Highlighted at Google Cloud Next 2026”

Anmol Pawar — Sun, 26 Apr 2026 10:37:09 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

What if everything we call “advanced AI” today is just the beginning?

I’ve always been a very imaginative person — constantly thinking about futuristic scenarios and how AI might evolve. Earlier, I believed AI was mostly about tools like ChatGPT and Google Gemini — systems designed to answer questions or generate content.

But after exploring insights from Google Cloud Next 2026, my perspective shifted. It made me realize that we are not at the peak of AI — we are standing at a transition point.

🧠 We Are Still in the “Narrow AI” Era

Most AI today is powerful, but still limited. It performs specific tasks very well, yet it cannot truly act independently or handle full real-world responsibility.

For example, current AI can:

generate content quickly
assist in solving problems
support decision-making

But it still depends heavily on human input.

👉 It feels intelligent, but it isn’t truly autonomous yet.

🚀 The Rise of Agentic AI

This is where things become interesting.

At Google Cloud Next 2026, one major idea stood out — Agentic AI. This shift is not just about improving AI, but changing its role completely.

Instead of only responding, AI is now moving towards:

taking actions
completing multi-step tasks
working more like a system than a simple tool

For instance, instead of manually analyzing data and writing reports, an AI agent could handle the process end-to-end — from analysis to insights and even suggestions.

👉 This changes the role of developers — from building tools to designing intelligent systems.

Google has clearly emphasized this shift — the “Agentic Era” has begun.
Interestingly, Google Cloud NEXT 2026 included over 260 announcements, all pointing toward one clear direction: the rise of Agentic AI and intelligent systems that can actively perform tasks.

💖 My Experience with Gemini Nano

I personally explored
👉 Gemini Nano

What surprised me most was its creative capability. I experimented with generating Pixar-style visuals, and the results felt expressive and engaging.

As someone interested in creative AI, this experience made me realize that AI is not just functional — it can also be creative and expressive.

🔄 The Bigger Shift

This is where my thinking evolved.

We are not just improving AI tools — we are redefining how we interact with them.

Earlier, AI was:

a tool we used

Now, it is becoming:

a system we interact with
a collaborator that assists in thinking and decision-making

👉 This shift is important because it changes how humans and machines work together.

⚖️ My Honest Take-

AI is growing rapidly — there is no doubt about that. In the coming years, it will become even more powerful.

But this progress also brings challenges.

Right now:

tools can still feel complex for beginners
real-world usability is not always simple
full autonomy is still far away

👉 There is still a gap between what AI promises and what users can actually achieve today

At the same time, we should not only talk about AI limitations, but also human limitations. As AI becomes more powerful, issues like misuse, over-dependence, and misunderstanding can increase.

👉 This means we need not just better AI systems, but also better awareness, responsibility, and clear usage guidelines.

🔚 Conclusion

Google Cloud NEXT 2026 made one thing clear — AI is no longer just about generating answers.

👉 It is becoming something we can work with, not just use.

For someone like me — still learning and exploring — this shift feels both exciting and challenging.

👉 The future of AI is not just smart tools, but intelligent, action-driven systems.

promptengineering #FutureOfAI #AgenticAI #NextGenAI

# 🌌 SkyGuardian Lite: We’re Losing the Night Sky 🌍/

Anmol Pawar — Sun, 19 Apr 2026 22:03:01 +0000

This is a submission for Weekend Challenge: Earth Day Edition

🌍 What I Built

Have you ever looked up at the night sky… and noticed something missing?

In many cities, stars have slowly disappeared — not because they’re gone, but because we’ve hidden them behind artificial light.

SkyGuardian Lite is a simple AI-powered web app that helps users:

🌕 Understand light pollution levels in their area
🌌 See how it affects star visibility
🤖 Get AI-generated insights and suggestions
🌍 Build awareness about environmental impact

👉 The goal is to turn an invisible problem into something visible, emotional, and actionable

🎬 Demo

👉 Enter a city → Get instant analysis

Example:

📍 Location: Bhopal
🌕 Pollution Level: Medium
✨ Stars Visibility: Partially Visible
🤖 AI Insight: Explains impact + suggestions

⚙️ How I Built It

SkyGuardian Lite follows a simple but effective flow:

User Input → Analysis → AI Insight → Actionable Suggestions

🧠 Backend

Python + Flask
Generates a simplified light pollution level (Low / Medium / High)

🤖 AI Integration

I used Google Gemini to:

Explain pollution levels in simple language
Describe environmental and human impact
Suggest practical actions

👉 Instead of just showing data, the app translates it into understanding

🎨 Frontend

HTML + CSS (dark cinematic theme 🌌)
Glassmorphism UI for modern feel
Emotional visual (tree vs human impact 🌳)

💡 Design Philosophy

Keep it simple
Make it interactive
Add emotional storytelling

🏆 Prize Categories

✅ Best Use of Google Gemini

Used Gemini to generate real-time environmental insights and actionable suggestions

✅ Best Use of GitHub Copilot

Assisted in speeding up development and improving code structure

🎨 Visual Storytelling

To make the impact more understandable, I added a storytelling layer:

😞 Polluted Sky → Stars hidden
😊 Clean Sky → Stars visible

👉 This transforms data into a story — making the problem not just visible, but felt

🚧 Challenges I Faced

Building a meaningful project within a short weekend
Simplifying a complex environmental issue
Balancing creativity with functionality

🚀 What’s Next

SkyGuardian Lite is part of a bigger vision:

Real-time data integration
Image-based pollution detection
Location-based live tracking
Community awareness features

🌍 Final Thought

We didn’t lose the stars.
We just stopped seeing them.

Maybe it’s time to look up again — before the sky forgets us. ✨

SkyGuardian Lite — because it’s not too late.

I Built an AI That Makes Everything Overcomplicated 😅

Anmol Pawar — Sun, 12 Apr 2026 21:15:02 +0000

🤖 What I Built

I built Overthinking Machine, a useless AI that takes simple things… and makes them unnecessarily complicated 😌

Instead of helping, it “improves” your input in a slightly over-smart way.

Example:

Input: hi

Output:
Hi 🙂

But I improved it:
Hello, hope you are having a productive day 😌

🎥 Demo

👉 Watch here: https://youtu.be/Dm2Oal9MByk?si=28aHpKPnU_MzyUJy

💻 Code

Built using Google AI Studio (Gemini) with custom prompt design.

⚙️ How I Built It

Google AI Studio (Gemini)
Prompt engineering for personality
Simple UI for interaction

Main focus was:

Keep responses short, simple, and slightly overthinking

🏆 Prize Category

Best Google AI Usage
Because the project uses prompt engineering to control AI personality and behavior in a fun and creative way.

😅 Why it’s funny

It turns simple things into unnecessary complexity
Feels like overthinking in real life
Relatable and slightly annoying… in a fun way 😂

🚀 Final Thought

Why say simple… when you can overthink? 🤖

DEV Community: Anmol Pawar

🐶 Can AI Understand Your Dog? Meet Dogesh — Your Dog, Decoded.

What I Built

Demo

Code

How I Built It

Tech Stack

Prize Categories

Best Use of Google AI

🐛 When My Autonomous AI Agent Refused to Cooperate

🛠️Building My First AI Agent with Hermes Agent 🤖

Why I Chose Hermes Agent

Setting Up Hermes Agent

What Makes Hermes Agent Interesting?

Building Hermes Commander

Lessons Learned

1. Tool Use Matters

2. Planning Is Powerful

3. Infrastructure Matters

4. Agentic AI Is Still Evolving

Looking Ahead

Final Thoughts

AutonomousAI #TechInnovation #HermesCommander #AgenticAI

Hermes Commander: An Autonomous Research Assistant Powered by Hermes Agent 🧠

What I Built

Demo

Code

My Tech Stack

How I Used Hermes Agent

HermesAgent #AgenticAI #HermesCommander #BuildInPublic

Why Gemma 4 Feels Like an Important Moment for AI Developers✨

From AI Apps to AI Models

Why Gemma 4 Caught My Attention

The Growing Accessibility of AI Development

Why Open Models Matter

AI Feels More Buildable Than Before

Final Thoughts

opensource #googleai #developers #gemma #machinelearning

🚀 “From Prompts to Autonomous Agents: What Google I/O 2026 Changed”

From Prompts to Autonomous Agents: What Google I/O 2026 Changed

From “Answer Engines” to “Action Engines”

Traditional AI

Agentic AI

The Biggest Ideas From Google I/O 2026

1. Gemini 3.5 Flash — Speed Matters More Than Ever

2. Gemini Omni and the Rise of Multimodal AI

3. Search Is Changing for the First Time in Years

4. Android XR and Ambient Intelligence

5. Why This Matters for Developers

Final Thoughts

Tags

“From Narrow AI to Agentic AI: The Shift Highlighted at Google Cloud Next 2026”

promptengineering #FutureOfAI #AgenticAI #NextGenAI

# 🌌 SkyGuardian Lite: We’re Losing the Night Sky 🌍/

🌍 What I Built

🎬 Demo

⚙️ How I Built It

🧠 Backend

🤖 AI Integration

🎨 Frontend

💡 Design Philosophy

🏆 Prize Categories

🎨 Visual Storytelling

🚧 Challenges I Faced

🚀 What’s Next

🌍 Final Thought

I Built an AI That Makes Everything Overcomplicated 😅

🤖 What I Built

🎥 Demo

👉 Watch here: https://youtu.be/Dm2Oal9MByk?si=28aHpKPnU_MzyUJy

💻 Code

⚙️ How I Built It

🏆 Prize Category

😅 Why it’s funny

🚀 Final Thought