Simplifying LLMs, AI Agents, RAGs and Machine Learning for you! • Co-founder @dailydoseofds_• BITS Pilani • 3 Patents • ex-AI Engineer @ LightningAI
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Aug 8 • 16 tweets • 5 min read
Let's compare GPT-5 and Claude Opus-4.1 for code generation:
Today, we're building a CodeArena, where you can compare any two code-gen models side-by-side.
Tech stack:
- @LiteLLM for orchestration
- @Cometml's Opik to build the eval pipeline
- @OpenRouterAI to access cutting-edge models
- @LightningAI for hosting CodeArena
Let's go!🚀
Aug 6 • 14 tweets • 5 min read
Let's compare OpenAI gpt-oss and Qwen-3 on maths & reasoning:
Before we dive in, here's a quick demo of what we're building!
Tech stack:
- @LiteLLM for orchestration
- @Cometml's Opik to build the eval pipeline (open-source)
- @OpenRouterAI to access the models
You'll also learn about G-Eval & building custom eval metrics.
Let's go! 🚀
Aug 5 • 13 tweets • 5 min read
Tech giants use Multimodal RAG every day in production!
- Spotify uses it to answer music queries
- YouTube uses it to turn prompts into tracks
- Amazon Music uses it to create playlist from prompt
Let's learn how to build a Multimodal Agentic RAG (with code):
Today, we'll build a multimodal Agentic RAG that can query documents and audio files using the user's speech.
Tech stack:
- @AssemblyAI for transcription.
- @milvusio as the vector DB.
- @beam_cloud for deployment.
- @crewAIInc Flows for orchestration.
Let's build it!
Aug 4 • 13 tweets • 4 min read
Sub-agents in Claude Code, clearly explained:
Claude Code subagents solved two of AI’s biggest problems:
- Large Context management
- Right tool selection
Making it the best AI coding assistant!
Let's understand how to build and use Sub-agents in Claude code:
Aug 3 • 10 tweets • 4 min read
uv in Python, clearly explained (with code):
uv is incredibly fast.
- Creating virtual envs. using uv is ~80x faster than python -m venv.
- Package installation is 4–12x faster without caching, and ~100x with caching
Today, let's understand how to use uv for Python package management.
Let's dive in!
Aug 1 • 14 tweets • 5 min read
Let's build a (Text2SQL + RAG), hybrid agentic workflow:
Before we dive in, here's a quick demo of what we're building!
Tech stack:
- @Llama_Index for orchestration
- @Milvusio to self-host a vectorDB
- @CleanlabAI to validate the response
- @OpenRouterAI to access the latest Qwen3
Let's go! 🚀
Jul 31 • 17 tweets • 6 min read
"Attention is all you need" implemented from scratch using PyTorch:
This is the paper that revolutionized AI!
I have been fine-tuning LLMs for more that 2 years now!
Here are the top 5 LLM fine-tuning techniques, explained with visuals:
Traditional fine‑tuning is impractical for LLMs (billions of params; 100s GB).
Since this kind of computing isn't accessible to everyone, parameter-efficient finetuning (PEFT) came into existence.
Today, we’ll cover the top 5 PEFT techniques, step by step.
Jul 25 • 10 tweets • 3 min read
How LLMs train LLMs, clearly explained (with visuals):
LLMs learn not only from raw text but also from other models.
Google’s Gemma 2 and 3, for example, were distilled from the larger Gemini model.
Today we cover, the three most common knowledge‑distillation methods.
Let's dive in! 🚀
Jul 24 • 13 tweets • 4 min read
Let's build a "Chat with your Code" RAG app using Qwen3-Coder:
Before we begin, take a look at what we're about to create!
Tech stack:
- @Llama_Index for orchestration
- @Milvusio to self-host a vectorDB
- @CleanlabAI codex to validate the response
- @OpenRouterAI to access @Alibaba_Qwen 3 Coder.
Let's go! 🚀
Jul 23 • 15 tweets • 5 min read
I just built the ultimate MCP server for Multimodal AI.
It lets you do RAG over audio, video, images and text!
100% open-source, here's the full breakdown...👇
Before we dive in, here's a quick demo of what we're building!
Tech stack:
- @pixeltablehq to build the multi-modal AI infrastructure
- @crewAIInc to orchestrate the agentic workflow
Quickly check the thread, then return here for a detailed overview. 🚀
Jul 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!
Jul 20 • 13 tweets • 4 min read
MCP security is completely broken!
Let's understand tool poisoning attacks and how to defend against them:
MCP allows AI agents to connect with external tools and data sources through a plugin-like architecture.
It's rapidly taking over the AI agent landscape with millions of requests processed daily.
But there's a serious problem... 👇
Jul 17 • 13 tweets • 7 min read
10 GitHub repos that will set you up for a career in AI engineering (100% free):
1️⃣ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using real-world datasets using Scikit-learn.
Includes quizzes, R/Python lessons, and hands-on projects. Some of the lessons are available as short-form videos.
Check this👇
Jul 16 • 14 tweets • 5 min read
Let's build a multi-agent content creation system (100% local):
Before we dive in, here's a quick demo of what we're building!
Tech stack:
- @motiadev as the unified backend framework
- @firecrawl_dev to scrape web content
- @ollama to locally serve Deepseek-R1 LLM
The only AI framework you'll ever need to learn! 🚀
Jul 14 • 8 tweets • 3 min read
ML researchers just built a new ensemble technique.
It even outperforms XGBoost, CatBoost, and LightGBM.
Here's a complete breakdown (explained visually):
For years, gradient boosting has been the go-to for tabular learning.
TabM is a parameter-efficient ensemble that provides:
- The speed of an MLP.
- The accuracy of GBDT.
The visual below explains how it works.
Let's dive in!
Jul 12 • 5 tweets • 2 min read
A Crash Course on Building AI Agents!
Here's what it covers:
- What is an AI agent
- Connecting agents to tools
- Overview of MCP
- Replacing tools with MCP servers
- Setting up observability and tracing
All with 100% open-source tools!
This course builds agents based on the following definition:
An AI agent uses an LLM as its brain, has memory to retain context, and can take real-world actions through tools, like browsing web, running code, etc.
In short, it thinks, remembers, and acts.
Jul 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👇
Jul 10 • 15 tweets • 5 min read
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! 🚀
Jul 7 • 9 tweets • 3 min read
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👇
Jul 3 • 10 tweets • 4 min read
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.
