Simplifying LLMs, AI Agents, RAGs and Machine Learning for you! • Co-founder @dailydoseofds_• BITS Pilani • 3 Patents • ex-AI Engineer @ LightningAI
24 subscribers
May 20 • 9 tweets • 3 min read
5 levels of Agentic AI systems, clearly explained (with visuals):
Agentic AI systems don't just generate text; they can make decisions, call functions, and even run autonomous workflows.
The visual explains 5 levels of AI agency—from simple responders to fully autonomous agents.
Let's dive to learn more about them.
May 17 • 11 tweets • 5 min read
9 MCP, LLM, and AI Agent cheat sheets for AI engineers (with visuals):
1️⃣ Model context Protocol
MCP is like a USB-C port for your AI applications.
Just as USB-C standardizes device connections; MCP standardizes AI app connections to data sources and tools.
Let's build an MCP-powered synthetic data generator (100% local):
Today, we're building an MCP server that every data scientist will love to have.
Tech stack:
- @cursor_ai as the MCP host
- @datacebo's SDV to generate realistic tabular synthetic data
Let's go! 🚀
May 15 • 14 tweets • 5 min read
Let's build a multi-agent book writer, powered Qwen3 (100% local):
Today, we are building an Agentic workflow that writes a 20k word book from a 3-5 word book title.
Tech stack:
- @firecrawl_dev for web scraping.
- @crewAIInc for orchestration.
- @ollama to serve Qwen 3 locally.
- @LightningAI for development and hosting
Let's go! 🚀
May 9 • 7 tweets • 3 min read
Traditional RAG vs. Agentic RAG, clearly explained (with visuals):
Traditional RAG has many issues:
- It retrieves once and generates once. If the context isn't enough, it cannot dynamically search for more info.
- It cannot reason through complex queries.
- The system can't modify its strategy based on the problem.
May 5 • 13 tweets • 4 min read
How LLMs work, clearly explained:
Before diving into LLMs, we must understand conditional probability.
Let's consider a population of 14 individuals:
- Some of them like Tennis 🎾
- Some like Football ⚽️
- A few like both 🎾 ⚽️
- And few like none
Here's how it looks 👇
May 4 • 7 tweets • 3 min read
5 amazing Jupyter Notebook tricks not known to many:
1️⃣ Retrieve a cell's output in Jupyter
If you often forget to assign the results of a Jupyter cell to a variable, you can use the `Out` dictionary to retrieve the output.
Apr 30 • 9 tweets • 3 min read
Let's fine-tune DeepMind's latest Gemma 3 (100% locally):
Before we begin, here's what we'll be doing.
We'll fine-tune our private and locally running Gemma 3.
To do this, we'll use:
- @UnslothAI for efficient fine-tuning.
- @ollama to run it locally.
Let's begin!
Apr 26 • 16 tweets • 5 min read
Let's build an MCP-powered multi-agent deep researcher (100% local):
Before we dive in, here's a quick demo of what we're building!
Tech stack:
- @Linkup_platform for deep web research
- @crewAIInc for multi-agent orchestration
- @Ollama to locally server DeepSeek
- @cursor_ai as MCP host
Let's go! 🚀
Apr 21 • 10 tweets • 4 min read
Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals):
Mixture of Experts (MoE) is a popular architecture that uses different "experts" to improve Transformer models.
The visual below explains how they differ from Transformers.
Let's dive in to learn more about MoE!
Apr 17 • 12 tweets • 5 min read
10 MCP, AI Agents, and RAG projects for AI Engineers (with code):
1️⃣ Real-time Voice RAG Agent
In this project you'll learn how to build a real-time Voice RAG Agent.
You will also learn how to clone your voice in just 5 seconds.
MCP vs A2A (Agent2Agent) protocol, clearly explained:
Agentic applications require both A2A and MCP.
- MCP provides agents with access to tools.
- While A2A allows agents to connect with other agents and collaborate in teams.
Today, I'll clearly explain what A2A is and how it can work with MCP.
Apr 13 • 8 tweets • 3 min read
Traditional RAG vs. Graph RAG, clearly explained (with visuals):
top-k retrieval in RAG rarely works.
Imagine you want to summarize a biography where each chapter details a specific accomplishment of an individual.
Traditional RAG struggles because it retrieves only top-k chunks while it needs the entire context.
Apr 11 • 12 tweets • 4 min read
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👇
Apr 10 • 14 tweets • 5 min read
Let's build a multi-agent brand monitoring system using DeepSeek-R1 (100% local):
Today, we're building a brand monitoring app that scraps web mentions and produces insights about a company.
Tech stack:
- Bright Data to scrape data at scale
- @crewAIInc for orchestration
- @ollama to serve DeepSeek locally
Let's go! 🚀
Apr 6 • 13 tweets • 4 min read
Let's compare Llama 4 and DeepSeek-R1 using RAG:
Today, we're building a Streamlit app to compare MetaAI's Llama 4 against DeepSeek-R1 using RAG.
Tech stack:
- @Llama_Index workflows for orchestration
- @Cometml Opik for evaluation
- @GroqInc for blazing-fast inference (FREE)
Let's go! 🚀
Mar 28 • 15 tweets • 4 min read
Let's build our own reasoning LLM using Reinforcement fine-tuning:
RFT allows you to transform any open-source LLM into a reasoning powerhouse.
No labeled data needed.
Today we'll use:
- @predibase for reinforcement fine-tuning (RFT)
- @Alibaba_Qwen-2.5:7b as the base model
Let's go! 🚀
Mar 23 • 7 tweets • 2 min read
Eigenvalues and eigenvectors, clearly explained:
The concept of eigenvalues & eigenvectors is widely known yet poorly understood!
Today, I'll clearly explain their meaning & significance.
Let's go! 🚀
Mar 21 • 4 tweets • 2 min read
Effortlessly trace and monitor multi-agent LLM applications!
With just two lines of code, Opik tracks everything happening inside your AI application, including costs. See the CrewAI example below.
100% open-source, self-hosted.
Opik offers integrations for nearly all popular frameworks.
But if you don't find what you need, don't worry!
A single decorator will handle it for you.
Check this out👇
Mar 18 • 9 tweets • 3 min read
Multithreading in Python clearly explained:
Ever felt like your Python code could run faster❓
Multithreading might be the solution you're looking for!
Today, I'll simplify it for you in this step-by-step guide.
Let's go! 🚀
Mar 13 • 11 tweets • 3 min read
Model Context Protocol (MCP), clearly explained:
MCP is like a USB-C port for your AI applications.
Just as USB-C offers a standardized way to connect devices to various accessories, MCP standardizes how your AI apps connect to different data sources and tools.
