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Akshay 🚀 Profile picture
@akshay_pachaar
Jul 11 • 12 tweets • 4 min read • Read on X
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MCP is on fire.

AI agents can now talk to real world tools, apps and actually get stuff done.

This changes everything.

Here are 10 amazing examples:
1️⃣ WhatsApp MCP

Exchange images, videos, and voice notes on WhatsApp!

Pair it with the ElevenLabs MCP server for AI-powered transcription & audio messages with 3,000+ voices.

Check this out👇
2️⃣ MCP-powered Agentic RAG

I created this server for Cursor and lets it perform deep web searches, as well as RAG over a specified directory.

Everything from the comforts of your IDE:
3️⃣ Ableton MCP

Create music with just prompts:
4️⃣ Figma MCP

Talk directly with Figma to craft sleek, modern login screens for mobile devices.
5️⃣ GroundX MCP Server

I created this server to process any complex real-world documents and make the knowledge accessible in Cursor IDE.

Powered by @eyelevelai's GroundX, it can understand text, images, diagrams and flowcharts.
6️⃣ ElevenLabs MCP server.

Give Claude and Cursor access to the entire ElevenLabs AI audio platform via simple text prompts.

You can even spin up voice agents to perform outbound calls for you — like ordering pizza.
7️⃣ Effortless website cloning

@Firecrawl_dev's MCP server can make things like website cloning effortless in your editor of choice 🔥

Just drop any site into Cursor and watch Claude visit the site and clone it for you.
8️⃣ Supabase MCP

Supabase MCP Server allows you to read and write to your database, create new projects, and more, right from your favorite AI tools!

Check this out👇
9️⃣ @BrowserbaseHQ's MCP Server

This server lets agents mimic human-like browsing using headless browsers.

You can order food, book hotels or flights, and do anything else you can imagine.

Check this out👇
github.com/browserbase/mc…Image
🔟 FastAPI MCP server

A zero-config tool for automatically convert FastAPI endpoints as MCP tools and use them with Clause, Cursor or any MCP client

100% open-source.
If you found it insightful, reshare with your network.

Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Check my illustrated MCP explainer:

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More from @akshay_pachaar

Akshay 🚀 Profile picture
Jul 10
90% of Python programmers don't know these 11 ways to declare type hints:
Type hints are incredibly valuable for improving code quality and maintainability.

Today, I'll walk you through 11 must-know principles to declare type hints in just two minutes.

Let's begin! 🚀 Image
1️⃣ Type hints for standard Python objects:

The most basic (and must-know) way to declare type hints for standard Python objects is as follows👇 Image
Read 15 tweets
Akshay 🚀 Profile picture
Jul 7
Temperature in LLMs, clearly explained (with code):
Let's prompt OpenAI GPT-3.5 with a low temperature value twice.

It produces identical responses from the LLM.

Check the response below👇 Image
Now, let's prompt it with a high temperature value.

This time, it produces a gibberish output. Check the output below👇

What is going on here? Let's dive in! Image
Read 9 tweets
Akshay 🚀 Profile picture
Jul 3
7 MCP projects for AI Engineers (with video tutorials):
1️⃣ MCP meets Ollama

An MCP client is a component in an AI app (like Cursor) that establishes connections to external tools.

Learn how to build it 100% locally.

Full walkthrough:
2️⃣ MCP-powered shared memory for Claude Desktop and Cursor

Devs use Claude Desktop and Cursor independently.

Learn how to add a knowledge graph based common memory layer to cross-operate without losing context.

Full walkthrough:
Read 10 tweets
Akshay 🚀 Profile picture
Jun 28
I have tested 100+ MCP servers in the last 3 months!

Here are 6 must-use MCP servers for all developers (open-source):
1️⃣ Graphiti MCP server

Agents forget everything after each task.

Graphiti MCP server lets Agents build and query temporally-aware knowledge graphs, which act as an Agent's memory!

Check this👇
2️⃣ Opik MCP server

This enables traceability into AI Agents and lets you monitor your LLM applications, by @Cometml.

Tools include:
- Creating projects
- Enable tracing
- Getting tracing stats

Check this demo👇
Read 9 tweets
Akshay 🚀 Profile picture
Jun 25
Let's generate our own LLM fine-tuning dataset (100% local):
Before we begin, here's what we're doing today!

We'll cover:
- What is instruction fine-tuning?
- Why is it important for LLMs?

Finally, we'll create our own instruction fine-tuning dataset.

Let's dive in!
Once an LLM has been pre-trained, it simply continues the sentence as if it is one long text in a book or an article.

For instance, check this to understand how a pre-trained LLM behaves when prompted 👇 Image
Read 12 tweets
Akshay 🚀 Profile picture
Jun 22
Let's build a real-time Voice RAG Agent, step-by-step:
Before we begin, here's a quick demo of what we're building

Tech stack:

- @Cartesia_AI for SOTA text-to-speech
- @AssemblyAI for speech-to-text
- @LlamaIndex to power RAG
- @livekit for orchestration

Let's go! 🚀
Here's an overview of what the app does:

1. Listens to real-time audio
2. Transcribes it via AssemblyAI
3. Uses your docs (via LlamaIndex) to craft an answer
4. Speaks that answer back with Cartesia

Now let's jump into code!
Read 11 tweets

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