Forem: Saket Jha

Algolia MCP SERVER FINANCIAL ANALYST

Saket Jha — Mon, 28 Jul 2025 06:22:11 +0000

This is a submission for the Algolia MCP Server Challenge

What I Built

I built the MCP-Powered Financial Analyst, an AI assistant that lives directly inside my code editor (Cursor IDE). It acts as a sophisticated partner for financial research, capable of handling complex, multi-step tasks using natural language.
The assistant has two primary skills:
Quantitative Analysis & Plotting: You can ask it to fetch historical stock data, perform comparisons, and generate visualizations. It writes and executes Python code using yfinance and matplotlib to create charts on the fly.
Qualitative News Analysis: This is where Algolia comes in. You can ask it for the latest news, market sentiment, or reports on a specific company. It performs a semantic search on a real-time news database and uses an LLM to provide a concise, insightful summary.
The "magic" is a Router Agent I built with CrewAI. It intelligently analyzes the user's query and routes it to the correct specialized team of agents—either the "Plotting Crew" or the "News Analysis Crew"—creating a seamless and powerful user experience.

## Demo
Github:-

How I Utilized the Algolia MCP Server

Key Takeaways

[Boost]

Saket Jha — Sun, 27 Jul 2025 17:33:47 +0000

🧑‍⚕️CareSetu AI: Instant Appointments, Intelligent Health Advice, and 24/7 Support Through Voice

Saket Jha ・ Jul 26

#devchallenge #assemblyaichallenge #ai #api

🧑‍⚕️CareSetu AI: Instant Appointments, Intelligent Health Advice, and 24/7 Support Through Voice

Saket Jha — Sat, 26 Jul 2025 22:33:49 +0000

This is a submission for the AssemblyAI Voice Agents Challenge for Business Automation Voice Agent and Domain Expert Voice Agent

📝 What I Built
As a software engineer at the healthcare startup CareSetu and a 3rd-year B.Tech student in Mathematics and Computing at institute of national importance, I've seen firsthand how technology can solve critical real-world problems. This voice-based web app allows users to schedule medical appointments, get answers to health questions like 'What precautions should I take for diabetes?', and manage their healthcare needs seamlessly. It’s designed to feel like you're having a conversation with a trusted health assistant, making healthcare more accessible for everyone.
From a business perspective, this directly impacts CareSetu an other business by automating front-desk tasks, reducing operational costs, and ensuring a steady flow of scheduled appointments, which is vital for the financial health of our partner clinics.
As you can see your result(appointment scheduled) which is 100% correct that means AssemblyAI STT conversion is most reliable for your other task also.

Tech Stack Used:

Backend Tech Stack
✅Core Framework & Runtime:
✅ Python 3.11.9 - Main backend language
✅ LiveKit Agents Framework- Real-time voice/video communication platform
✅AsyncIO - Asynchronous programming for handling concurrent operations
AI & Machine Learning:
✅Gemini flash - LLM integration for conversational AI
✅Cartesia/TTS - text-to-speech services
✅AssemblyAI - STT service with business optimizations
✅ElevenLabs - Premium text-to-speech service but as fallback
✅Google Cloud Speech - Additional TTS provider as fallback
✅Transformers/HuggingFace - ML model handling

PDFMiner/PDFPlumber/PyPDF2 - PDF document processing
✅NumPy/SciPy - Scientific computing
✅Scikit-learn - Machine learning utilities

Web Framework & APIs:
✅LiveKit Agents Framework - Real-time communication platform
✅ Python HTTP Server - Simple token server for frontend integration
✅ AIOHTTP - HTTP client library (for outbound requests

Integrations:
✅Google Calendar API - Appointment scheduling
✅Google Cloud APIs - Various Google services

Frontend Tech Stack
Core Framework:
✅React 19.1.0 - Modern React with latest features
✅Vite 7.0.4 - Fast build tool and dev server
✅TypeScript - Type-safe JavaScript development
✅UI & Styling:
✅Tailwind CSS 4.1.11- Utility-first CSS framework
✅PostCSS- CSS processing
✅Real-time Communication:
✅LiveKit Client - WebRTC client for voice/video
✅@livekit/components-react - Pre-built React components for LiveKit
Testing:
✅Vitest - Fast unit testing framework
✅Testing Library - React component testing utilities
✅JSdom- DOM simulation for testing
Development Tools:
✅ESLint - Code linting
✅ Terser - JavaScript minification

🔍 STEP-BY-STEP DETAILED BREAKDOWN

User Voice → Microphone → Web Audio API → LiveKit Stream
Audio Stream → AssemblyAI → Text Transcript

3.Text Query → Query Processing → Knowledge Search → Context Building

4.Enhanced Context → Google Gemini → AI Response

5.Appointment Intent → Google Calendar API → Booking Result

6.AI Response → Cartesia/ElevenLabs/Google → Audio Stream

7.Audio Stream → Web Audio API → Speaker Output

8.Complete Interaction → Analysis → Knowledge Update

9.User continues → Loop to Step 1 | Timeout/Disconnect → End session

10.STT Error → Show error → Retry → Text input fallback
LLM Error → Show error → RAG-only response → Retry

TTS Error → Try next service → Text response fallback
Calendar Error → Show error → Manual booking → Retry

Note: This model currently supports the Appointment Intent and Query Intent (such as providing information based on FAQs, the Privacy Policy of CareSetu, health insurance details, various departments of CareSetu, and general modern scientific tips along with homemade remedies related to healthcare).

💻Demo

Explanation Video

👉About Myself, Working Project and Repository explanation

Note:- As you can see at timestamp 7:07 model tell me my name this mean it remembered my name during conversation.

👉Pure Backend Explanation

👉Pure Frontend Explanation

The application is live at:
👉Live Link

1) click on connect to agent
2) then click on start conversastion

👉Backend is hosted on an AWS EC2 instance with Nginx as a reverse proxy.
👉Frontend is hosted on Vercel.

📁 GitHub Repository

✅Frontend Code

✅Backend Code

Proof of Code Snippet and its result

Source:- caresetuAgent_3.0(Backend)

Code Snippet for Building a Voice Agent with AssemblyAI and LiveKit

Code Snippet for RAG Integration

Calendar Integration

🧘🏻‍♂️Conclusion

AssemblyAI played a crucial role in helping me successfully complete this challenge. From the start of the CareSetu agent project, the AssemblyAI team provided responsive support and guidance, answering my questions about technical requirements, deployment options, and permissible ways to share my project publicly. Whether it was clarifying best practices for publishing my work, assisting with integration details, or offering encouragement during each milestone, their team was always available whenever I needed help, as evidenced by the direct conversations with team members like Lee Vaughn , Dan Ince , Amanda DiNoto and Ryan Seams. Their willingness to address any issues and interest in seeing my progress not only boosted my confidence but also ensured technical obstacles never became roadblocks. This support allowed me to focus fully on building an impactful, reliable voice agent for healthcare automation and customer support—demonstrating AssemblyAI’s genuine commitment to the success of developers using their platform.

Comment your thoughts, and follow me!

✅Twitter Post Of This Blog 5k+ Impression

✅Published This Story in Artificial Intelliegence having 27k+ followers + email subscriber

🔗Connect with Me
Medium:- Profile Link
Twitter/X: Profile Link
LinkedIn: Profile Link

“Why I Migrated from Next.js to React + Vite + Redux”

Saket Jha — Fri, 25 Jul 2025 13:50:20 +0000

When I inherited a project with a JavaScript backend that I couldn’t modify, I faced a critical decision: how to optimize the frontend for better performance and developer experience. Here’s why I chose to migrate from Next.js to React with Vite and Redux Toolkit Query.

Why SSR Wasn’t Helping
The biggest realization was that if your API responses are slow, SSR will only make your pages load slower. Since I was stuck with an existing backend that had performance bottlenecks, Next.js’s server-side rendering became a liability rather than an asset. The pages were taking longer to load because the server had to wait for slow API responses before rendering.

Performance Issues with the Existing Codebase
The previous developer had written components that triggered unnecessary re-renders, making the application sluggish. Additionally, there was no implementation of debouncing or throttling for API requests. The app was making API calls on every keystroke, which significantly impacted rendering performance and user experience.

While this wasn’t the primary reason for changing the frontend stack, it highlighted a crucial point: when your frontend frustrates users with slow loading times, optimization techniques like debouncing become essential.

Why React + Vite Was the Perfect Solution
Lightning-Fast Development Experience. Vite offers instant hot module replacement (HMR) and near-instant startup times. Unlike traditional bundlers, Vite serves source files over native ES modules, eliminating the need to bundle the entire app during development.

This means:
Faster development server startup — almost instantaneous compared to Next.js
Instant updates when making changes to React components
Better developer productivity with minimal configuration required

Redux Toolkit Query: Centralizing Data Management
To address the data management chaos, I implemented RTK Query (Redux Toolkit Query)which provided several key advantages:
Centralized State Management

Single source of truth for all application data
Automatic caching and invalidation of API responses
Background synchronization to keep data fresh

`import { configureStore } from '@reduxjs/toolkit'
import authReducer from './slices/authSlice'
import uiReducer from './slices/uiSlice'
import doctorReducer from './slices/doctorSlice'
import hospitalReducer from './slices/hospitalSlice'

export const store = configureStore({
  reducer: {
    auth: authReducer,
    ui: uiReducer,
    doctor: doctorReducer,
    hospital: hospitalReducer,
  },
  middleware: (getDefaultMiddleware) =>
    getDefaultMiddleware({
      serializableCheck: {
        ignoredActions: ['persist/PERSIST'],
      },
    }),
})

export type RootState = ReturnType<typeof store.getState>
export type AppDispatch = typeof store.dispatch
`

Zoom image will be displayed

Performance Optimizations

Reduced boilerplate code compared to traditional Redux patterns
Automatic request deduplication to prevent unnecessary API calls
Built-in support for optimistic updates and real-time data streaming Developer Experience
Auto-generated hooks for each API endpoint -Redux DevTools integration for debugging data flow -Consistent approach to data fetching across the entire application Additional Benefits of the New Stack Reduced Server Load By moving to client-side rendering, I eliminated the server-side processing overhead that was slowing down the application with the existing slow backend APIs. Easier Maintenance The zero-configuration setup of Vite and the structured approach of RTK Query made the codebase more maintainable and easier for new developers to understand.

Conclusion
Migrating from Next.js to React + Vite + Redux Toolkit Query was the right decision for this specific scenario. When you’re constrained by backend performance and don’t need SSR benefits, this stack offers superior development experience, better performance, and more flexibility.

The migration not only solved the immediate performance issues but also provided a solid foundation for future development with faster builds, better state management, and improved developer productivity.

Main motive to right this blog is don’t forget about right in the hype of others.

            ‘Thanks for reading. Stay connected!’.

10 Essential Questions Every Beginner Should Know About Generative AI

Saket Jha — Sun, 20 Jul 2025 07:14:58 +0000

If you’ve heard people talking about Generative AI or other AI tools and you’re curious about what they are, you’re in the right place. Generative AI isn’t just a trendy term — it’s actually changing the way we work, create things, and use technology every day.

I’ve put together the 10 most important questions that will give you a solid foundation in Generative AI.

What’s the Difference Between Generative AI and Discriminative AI?** Imagine you’re at an art gallery.

Discriminative AI is like an art expert. It looks at a painting and says, “This is by Van Gogh” or “This is a Picasso.” It’s good at recognizing and telling the difference between things.
Discriminative AI: “Is this a cat or a dog?” → It’s all about choosing or classifying.
Generative AI is like the artist. It doesn’t just look at art — it creates it! It can make new paintings, write stories, compose music, or even write code.
Generative AI: “Draw a cat wearing a superhero cape.” → It’s all about creating something new.

How Are Large Language Models Trained, and What Challenges Do They Face?

Note:-An LLM is a type of artificial intelligence (AI) that is trained to understand and generate human language. It’s like a super smart chatbot that can read, write, summarize, translate, answer questions, and more.

🧠 The Training Process
Step 1: Collecting Data
The model reads tons of text — books, websites, articles — billions of pages!
Imagine every book in every library, all read by a super-fast learner.

Step 2: Learning Patterns
The model studies how words go together.
For example, it learns that:

“The cat sat on the…” is often followed by “mat” or “chair.”

It figures out grammar, common phrases, and how people usually talk or write.

Step 3: Getting Better (Fine-Tuning)
After the first learning, humans help the model improve.
Like a tutor giving feedback so it answers more clearly, politely, and correctly.

⚠️ The Big Challenges
💻 Lots of Computing Power
It takes super-powerful computers and a lot of electricity — sometimes enough to power a small city!

📚 Good vs Bad Data
If the model reads bad or biased info, it can learn the wrong things. So, data quality really matters.

📏 Size Matters (But Costs More)
Bigger models are often smarter, but they take way more time, money, and energy to build and use.

3.What are some popular generative AI models used for natural language generation, and what are their key features?

Note:-NLP stands for Natural Language Processing.
It is a field of Artificial Intelligence (AI) that focuses on how computers can understand, interpret, and generate human language (like English, Hindi, etc.).

Meet the Stars of the Generative AI World
GPT Series (by OpenAI)
What it does: Writes and talks like a human. Can help with writing, chatting, coding, and more.
BERT (by Google)
What it does: Understands the meaning of words by reading the full sentence.
T5 (by Google)
What it does: Turns all language tasks into a “text-in, text-out” job.
Claude (by Anthropic)
What it does: Aims to be helpful, safe, and honest.

4.Explain the concept of transfer learning as applied to large language models.

Transfer learning is like learning tennis after you already know how to play badminton. You don’t have to start from zero — you use the skills you already have.
How It Works in AI:
First, a model learns from a big, general dataset (like learning basic language skills).
Next, that model is adjusted (fine-tuned) to do a specific job — like understanding medical words.
Result? The model does the new job much better than if you trained it from scratch.
Why It’s a Game-Changer:
Saves Time: Training can take hours or days, not months.
More Access: Smaller teams can create smart AI without huge resources.
Better Results: The model starts with good knowledge, so it performs better.

Think of It Like This:
You don’t teach every doctor how to talk from birth. You teach them language first, then medical terms. That’s much faster and smarter

Discuss the concept of perplexity in the context of evaluating language models, and its significance in generative AI.

Perplexity sounds complicated, but it’s actually a simple concept. Think of it as a measure of how “surprised” an AI model is by what comes next in a sentence.
Understanding Perplexity
Low Perplexity: The model is confident and unsurprised
Example: After “The sun rises in the…” the model confidently predicts “east”
High Perplexity: The model is confused and uncertain
Example: After “The quantum flux capacitor…” the model has no idea what comes next
Why It Matters
Model Evaluation: Lower perplexity generally means better performance
Comparison Tool: Helps researchers compare different models objectively
Quality Indicator: Shows how well a model understands language patterns
It’s like grading a student’s reading comprehension — the less confused they are by the text, the better they understand it.

6.How do generative AI models like GPT-3 and BERT utilize attention mechanisms in their architecture?
Attention mechanisms are one of the most important breakthroughs in AI. They help models figure out what to focus on, just like how you focus on specific words when reading.

How Attention Works
Imagine you’re reading the sentence: “The cat that lived in the house with the red door was very friendly.”
When processing “was very friendly,” the attention mechanism helps the model focus on “cat” rather than getting confused by “house” or “door.”

In GPT and BERT

GPT (Generative Pre-trained Transformer):
Uses “self-attention” to understand relationships between words.
Looks at previous words to predict the next one.
Like reading a story and using context to guess what happens next.

BERT (Bidirectional Encoder Representations from Transformers):
Uses attention to look at words from both directions.
Considers the full context before making decisions.
Like reading a sentence completely before understanding its meaning.

Why It’s Revolutionary
Better Context Understanding: Models can handle longer, more complex text
Improved Performance: Attention mechanisms dramatically improved AI capabilities
Interpretability: We can sometimes see what the model is “paying attention to”

7.Explain the challenges of bias and fairness in generative AI and large language models, and strategies to mitigate them.

Bias in AI is one of the biggest challenges we face today. Let’s break it down:
Types of Bias in AI

a) Training Data Bias
If the data used to teach the AI mostly includes certain groups, the AI will learn those patterns.
📌 Example: If most CEOs in the data are men, the AI might think only men can be leaders.
b) Algorithmic Bias
Sometimes the way the AI is built can lead to unfair decisions.
📌 Example: A hiring AI might accidentally treat some people unfairly based on age, gender, or background.
c) Confirmation Bias
The AI might repeat and strengthen stereotypes it has learned.
📌 Example: Linking certain jobs to only one gender or race.

How to Reduce Bias in AI

Use Diverse Data
Include examples from all kinds of people
Make sure underrepresented groups are included

Test for Bias
Regularly check if the AI is making fair decisions
Get help from diverse teams to spot problems

Build Fair Algorithms
Add rules that help keep the system fair
Use tools that check the fairness of decisions
Keep Monitoring
Watch how the AI behaves after it’s launched
Be ready to fix any issues that show up

Be Transparent
Make it clear how the AI makes decisions
Let people step in when needed
The goal:
We may not remove all bias, but we can reduce the harmful effects and make AI as fair as possible for everyone.

8.What are some limitations or drawbacks of current generative AI and large language models, and potential areas for improvement?

🔴 Limitations of Generative AI & LLMs
Hallucinations — AI can generate false or misleading information.
Bias in Outputs — May reinforce harmful stereotypes from training data.
Lack of True Understanding — Models don’t actually “understand” content like humans.

🟢 Areas for Improvement
Better Fact-Checking — Connect models with reliable, real-time data sources.
Fairness & Safety Controls — Reduce bias through diverse data and testing.
Improved Reasoning — Teach models to follow logical steps, not just patterns.

And the last 2 questions are for you to explore:

Latent Space and Diffusion Models — take some time to learn what they mean and how they work.

If you’d like a quick example and an easy way to build an AI agent, don’t forget to comment! I’ll be sharing a simple, step-by-step guide with a foolproof plan.

Follow me please for such more intersecting things.

Forem: Saket Jha

Algolia MCP SERVER FINANCIAL ANALYST

What I Built

How I Utilized the Algolia MCP Server

Key Takeaways

[Boost]

🧑‍⚕️CareSetu AI: Instant Appointments, Intelligent Health Advice, and 24/7 Support Through Voice

Saket Jha ・ Jul 26

🧑‍⚕️CareSetu AI: Instant Appointments, Intelligent Health Advice, and 24/7 Support Through Voice

💻Demo

👉About Myself, Working Project and Repository explanation

👉Pure Backend Explanation

👉Pure Frontend Explanation

📁 GitHub Repository

Proof of Code Snippet and its result

Code Snippet for RAG Integration

*Calendar Integration *

🧘🏻‍♂️Conclusion

“Why I Migrated from Next.js to React + Vite + Redux”

10 Essential Questions Every Beginner Should Know About Generative AI

Calendar Integration