DEV Community: Aman Yadav

Your Content Deserves a Stunning Cover—Let AI Handle It

Aman Yadav — Sun, 26 Jan 2025 16:41:23 +0000

This is a submission for the Agent.ai Challenge: Assembly of Agents (See Details)

What I Built

I developed Cover Image Generator, an AI-powered agent that creates professional, visually appealing cover images for blogs and websites. By analysing website data (e.g., content, theme, and branding), this agent generates custom cover images tailored to the user’s needs, ensuring a perfect match for their online presence.

The cover image used in this blog is also generated by the ai agent. (link to run)

Why?

A compelling cover image is crucial for capturing the audience's attention, but designing one can be time-consuming and require design expertise. This agent simplifies the process by automating cover image creation, making it accessible to everyone.

Envisioned Use Cases:

Helping bloggers create eye-catching cover images for their posts.
Enabling businesses to generate branded headers for their websites.
Providing marketers with quick, professional visuals for campaigns.

Demo

Link to run: https://agent.ai/agent/cover?rid=5bab0d0539ed4569a9a029859a24f8a5

Check out the covers of these blogs for more examples

Try it yourself: https://agent.ai/agent/cover

Agent.ai Experience

Delightful Moments:

Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.

Challenges:

Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.

Your Brand, Your Logo—Created in Seconds.

Aman Yadav — Sun, 26 Jan 2025 16:20:19 +0000

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built

I developed Logo Maker Pro, an AI-powered agent that creates stunning logos tailored to user preferences. Using the Flux Pro AI model, this agent generates logos in various styles, including Flashy, Tech, Modern, Playful, Abstract, and Minimal.

Why?

Designing a professional logo can be time-consuming and expensive. Logo Maker Pro democratizes logo creation by offering a fast, affordable, and customizable solution for businesses, startups, and individuals.

Envisioned Use Cases:

Helping startups create brand identities quickly.
Enabling small businesses to design logos without hiring a designer.
Providing creative professionals with inspiration and templates.

Demo

Agent link: https://agent.ai/agent/logo-maker

Scenario: Minimal style logo with purple primary colour, orange background colour and postman name in logo

Link to run: https://agent.ai/agent/logo-maker?rid=6422c9a40aaf44c4b4d57518a257f0e9

Some other runs:

Agent.ai Experience

Delightful Moments:

Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.

Challenges:

Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.

Find the Right Agent for the Job—Fast

Aman Yadav — Sun, 26 Jan 2025 16:14:34 +0000

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built

I developed I Know a Guy Who Knows a Guy, an AI agent designed to help users discover the perfect AI agents for their specific use cases. By searching through the Agent.ai agent network, this agent provides a curated list of AI agents that match the user’s requirements, saving time and effort in finding the right tools.

Why?

With the growing ecosystem of AI agents, finding the right one for a specific task can be overwhelming. This agent simplifies the process by acting as a “matchmaker” between users and the agents they need.

Envisioned Use Cases:

Helping developers find agents for specific tasks (e.g., summarization, research, image generation).
Assisting businesses in identifying agents for automation or analytics.
Enabling users to explore the Agent.ai ecosystem effortlessly.

Demo

Scenario: A user asks, “Find agents that can help in research of complex topics”

Link to run: https://agent.ai/agent/agent-finder?rid=8ec34a129ea54cda8684de65bc47f11c

Try it yourself: https://agent.ai/agent/agent-finder

Some agent runs:

Agent.ai Experience

Delightful Moments:

Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.

Challenges:

Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.

Transform Text into Intelligence— No Fluff, Just Insight.

Aman Yadav — Sun, 26 Jan 2025 14:15:12 +0000

This is a submission for the Agent.ai Challenge: Productivity-Pro Agent (See Details)

What I Built

I developed Summarizer Pro, an AI agent designed to deliver intelligent, context-aware summaries of any text input while meticulously preserving the original content's core meaning and critical details. Recognising the need for efficient information distillation in workflows, I built this agent to act as a "smart summarisation assistant" that can seamlessly integrate into automated systems or be used by other AI agents.

Why?

In today’s data-driven world, extracting actionable insights from large volumes of text is a bottleneck. This agent tackles that by providing concise, coherent summaries without losing the essence of the content—perfect for professionals, researchers, or other AI agents needing preprocessed data.

Technical Details:

Control Flows: The agent uses advanced control flows to orchestrate seamless transitions between text analysis, summarisation, and output generation.
Google LLM with Large Context Window: The agent leverages Google’s LLM for summarisation, which excels at handling large volumes of data while preserving context and coherence.
Modular Design: Built with modularity in mind, the agent can easily integrate additional tools or APIs for enhanced functionality.

Envisioned Use Cases:

Automating summarisation for lengthy reports, research papers, or articles.
Streamlining inter-agent communication by condensing verbose outputs.
Enhancing knowledge management systems with rapid digest generation.

Demo

Link to agent: https://agent.ai/agent/summarizer-pro

Scenario: Summarizing my previous submission here

Link to the run: https://agent.ai/agent/summarizer-pro?rid=e358682a055744c093e3ef32a80b4ee7

Agent.ai Experience

Delightful Moments:

Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.

Challenges:

Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.

From Curiosity to Clarity—Research, Refine, Deliver.

Aman Yadav — Sun, 26 Jan 2025 08:33:28 +0000

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built

I developed an Autonomous Research AI agent that automates end-to-end research, analysis, and insight generation. Frustrated by the time-consuming process of manually scouring the web, synthesizing data, and refining results, I built this agent to act as a "smart research assistant" who thinks while it works. Users submit a topic and areas of interest, and the agent:

Researches the topic across trusted web sources,
Generates a draft summary,
Critically reflects on its own output to identify gaps or biases,
Iteratively improve the summary into a polished, actionable answer.

Agent Flow [1]

Technical Details:

Control Flows: The agent uses advanced control flows for orchestration, ensuring seamless transitions between research, summarization, and reflection phases.
Multi model: uses different models for different tasks
Claude for prompt writing: uses Claude excellent prompt writing capability to prompt perplexity
Google LLM with Large Context Window: For summarization, the agent leverages Google’s LLM, which excels at handling large volumes of data while preserving context and coherence.
GPT-4o for Reflection: GPT-4o powers the agent’s self-reflection phase, identifying knowledge gaps, biases, and areas for improvement to refine the summary iteratively.
Perplexity for web search: Fetches facts and data form web using perplexity
Modular Design: The agent is built with modularity in mind, allowing easy integration of additional tools or APIs for enhanced functionality.

Why?
Traditional search tools overwhelm users with raw data. This agent tackles that by delivering refined, context-aware insights—perfect for time-constrained professionals, researchers, or even other AI agents needing preprocessed data.

Envisioned Use Cases:

Accelerating due diligence for startups.
Generating unbiased summaries for topics of interest
Powering real-time market trend reports.

Demo

Link to agent: https://agent.ai/profile/researcher-pro

Scenario: A user asks, “How deepseek trained their r1 model?”

Link to run: https://agent.ai/agent/researcher-pro?rid=361127f4c4774eabb37f5cfbbe220fa0

Agent.ai Experience

Delightful Moments:

Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.

Challenges:

Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.

References:
[1] https://github.com/langchain-ai/ollama-deep-researcher

VoiceLoom: Turn Fuzzy Thoughts into Structured Knowledge

Aman Yadav — Sun, 24 Nov 2024 18:19:21 +0000

This is a submission for the AssemblyAI Challenge : Sophisticated Speech-to-Text & No More Monkey Business.

What I Built

VoiceLoom is an AI-powered voice notes application that transforms stream-of-consciousness recordings into structured, actionable content. Powered by AssemblyAI's LeMUR model, it tackles the universal challenge of converting unstructured thoughts into organised, valuable information.

Key Features

Intelligent Speech-to-Text: Crystal-clear transcription using AssemblyAI's Transcription API
Smart Summarization: Concise summaries via the summarisation parameter in Transcription API
Structured Output: Transformation of fuzzy thoughts into organised content using LeMUR
Dynamic Title Generation: Automatic creation of contextual titles using LeMUR
Action Item Detection: Smart extraction of action points from voice recordings

Demo

Experience VoiceLoom: Launch App

Note: Bring your own AssemblyAI API key, configurable in settings after login.

Visual Journey 📸

Welcome to VoiceLoom

Your gateway to organized thoughts

Voice Capture Interface

Simple, intuitive recording experience

Structured Notes View

Your thoughts beautifully organized

Source code

Journey

After exploring AssemblyAI's comprehensive API documentation, VoiceLoom emerged as the perfect showcase for the platform's capabilities. The project addresses two key challenge prompts:

Sophisticated Speech-to-Text
- Advanced transcription with precise punctuation
- Intelligent summary generation
No More Monkey Business
- LeMUR-powered title generation
- Structured note formatting
- Smart action item extraction

Universal-2 Implementation

Generates professional transcriptions with proper punctuation
Creates informative summaries from voice content

LeMUR Integration

Dynamic title generation based on content analysis
Conversion of unstructured thoughts into formatted notes
Intelligent action item identification

Future Roadmap 🛣️

Interactive Note Analysis
- Chat interface for note exploration
- LeMUR-powered Q&A using transcript IDs
Enhanced Insights
- Automated note highlights
- Intelligent pattern recognition
- Personalised content analysis

Local AI Knowledge Base with Next.js, Ollama, and PostgreSQL

Aman Yadav — Sun, 10 Nov 2024 19:19:31 +0000

This is a submission for the Open Source AI Challenge with pgai and Ollama

What I Built

I developed a fully local, AI-powered Knowledge Base Management System that enables users to upload documents and interact with them through RAG (Retrieval-Augmented Generation). This system harnesses the capabilities of PostgreSQL’s AI extensions alongside Ollama's local models, creating a privacy-centered solution that operates entirely on your own infrastructure.

Key Features:

Fully Local Operation
- All AI processing occurs directly on your machine, powered by Llama 3.1
- Ensures that no data leaves your infrastructure, providing complete privacy and control.
Intelligent chat interface with RAG capabilities
- Uses Retrieval-Augmented Generation to deliver accurate responses.
- Vector similarity search is driven by pgvector, with efficient querying through pgvectorscale.
Function Calling
- Supports extensible operations for expanded interaction capabilities.
Modern Web Interface
- Features a clean, intuitive UI designed with shadcn/ui.
- Enables real-time chat interactions with integrated file management.

Demo

In this demo, users can select multiple PDFs and receive answers based on the content. Here, the hackathon announcement is used as a source. Additionally, users have the option to manage files within the app.

User also has option to manage files in the app

Below is the chat interface, where users interact with the documents.

GitHub Repository:

yadav-aman / ai-chatbot

Next.js AI Knowledge-base chatbot

An fully local Open-Source AI knowledge made with Next.js and the AI SDK Powered by Ollama and Postgres

Features · Model Providers · Deploy Your Own · Running locally

Features

Next.js App Router
- Advanced routing for seamless navigation and performance
- React Server Components (RSCs) and Server Actions for server-side rendering and increased performance
AI SDK
- Unified API for generating text, structured objects, and tool calls with LLMs
- Hooks for building dynamic chat and generative user interfaces
shadcn/ui
- Styling with Tailwind CSS
- Component primitives from Radix UI for accessibility and flexibility
Data Persistence
- Postgres for saving chat history and user data
- Min.io for efficient file storage
NextAuth.js
- Simple and secure authentication
Ollama for AI model management
- Easily switch between different AI models and providers
pgvector for vector similarity search
- Efficiently store embeddings for similarity search
pgvectorscale for scaling vector similarity search
- Query embeddings for RAG

Model Providers

…

View on GitHub

Tools Used

The project leverages several powerful open-source tools:

AI and Database:

PostgreSQL as the primary database
pgvector for storing vectors
pgvectorscale for similarity search
Ollama running
- Llama 3.1 for chat
- mxbai-embed-large for embeddings

Frontend and Backend:

Next.js 14 with App Router
Vercel AI SDK for chat interfaces
shadcn/ui for component design
Tailwind CSS for styling
NextAuth.js for authentication
Min.io for file storage
Drizzle as ORM

Final Thoughts

Building this project for the Open Source AI Challenge was a fulfilling journey. The most challenging yet rewarding aspect was enabling it to run entirely locally, offering a significant data privacy advantage.

Using pgvector and pgvectorscale made the RAG implementation seamless.
The pgai Vectorizer currently supports only OpenAI embeddings. While I initially aimed to integrate it with Ollama, the absence of a compatible tokenization endpoint made this integration unfeasible. As an alternative, I implemented the embedding logic directly.

Prize categories:

Main Category
Open-source Models from Ollama

Future Improvements

Fine-tune prompts for better tool interaction
Add support for more document formats
Implement multi-model support
Optimize vector search performance
Add batch processing capabilities for large document sets