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More from @akshay_pachaar

Akshay 🚀

@akshay_pachaar

Aug 22

Let's build an MCP server (100% local):

Before diving in, here's what we'll be doing today:

- Understand MCP with a simple analogy.
- Build a 100% local and secure MCP client using @mcpuse
- Integrate the client with @Stagehanddev MCP sever
- Use this setup for control and automate browser

Let's go! 🚀

First, let's understand MCP using a translation analogy.

Imagine you only know English. To get info from a person who only knows:

- French, you must learn French.
- German, you must learn German.
- and so on.

Learning even 5 languages will be a nightmare for you!

Read 14 tweets

Akshay 🚀

@akshay_pachaar

Aug 21

A simple technique makes RAG up to 40x faster & 32x memory efficient!

- Perplexity uses it in its search index
- Google uses it in Vertex RAG engine
- Azure uses it in its search pipeline

Let's understand how to use it in a RAG system (with code):

Today, we're building a multi-agent legal assistant that can query 50M+ vectors in <30ms using Binary Quantization (BQ).

Tech stack:

- @milvusio to self-host vectorDB with BQ
- @firecrawl_dev for web search
- @crewAIInc for orchestration
- @ollama to serve GPT-OSS

Let's go! 🚀

First things first: What exactly is binary quantization❓

In this video, I answer this question and provide a really nice analogy to explain why BQ works and how it makes your setup fast and memory efficient.

Check this out👇

Read 15 tweets

Akshay 🚀

@akshay_pachaar

Aug 19

JSON prompting for LLMs, clearly explained:

I used to think prompt engineering is dead!

Then I discovered JSON prompting and everything changed.

Today, I'll show you exactly what JSON prompting is and how it can drastically improve your AI outputs!

Let's dive in! 🚀

What is Json anyway?

JSON stands for JavaScript Object Notation.

Don’t let the name scare you; it’s just a way to organize info with clear labels.

You can think of it like a pizza order ticket with clear labels so the kitchen gets it right:

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Aug 18

MCP & A2A (Agent2Agent) protocol, clearly explained (with visuals):

Agentic applications require both A2A and MCP.

- MCP provides agents with access to tools.
- A2A allows agents to connect with other agents and collaborate in teams.

Today, let's clearly understand what A2A is and how it can work with MCP.

What is A2A?

A2A (Agent2Agent) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.

Instead, they communicate by exchanging context, task updates, instructions, and data.

Read 8 tweets

Akshay 🚀

@akshay_pachaar

Aug 17

This simple technique can scale training from 1-1000+ GPUs.

- OpenAI uses it to train GPT models
- Google uses it in their TPUs to train Gemini
- Meta uses it to train Llamas on massive GPU clusters

Let's learn how to sync GPUs in multi-GPU training (with visuals):

One major run-time bottleneck in multi-GPU training happens during GPU synchronization.

For instance, in multi-GPU training via data parallelism:

- The same model is distributed to different GPUs.
- Each GPU processes a different subset of the whole dataset.

Check this 👇

This leads to different gradients across different devices.

So, before updating the model parameters on each GPU device, we must communicate the gradients to all other devices to sync them.

Let’s understand 2 common strategies next!

Read 14 tweets

Akshay 🚀

@akshay_pachaar

Aug 15

Google just dropped a new LLM!

You can run it locally on just 0.5 GB RAM.

Let's fine-tune this on our own data (100% locally):

Google released Gemma 3 270M, a new model for hyper-efficient local AI!

We'll fine-tune this model and make it very smart at playing chess and predict the next move.

Tech stack:
- @UnslothAI for efficient fine-tuning.
- @huggingface transformers to run it locally.

Let's go! 🚀