Akshay 🚀 Profile picture
Aug 3 10 tweets 4 min read Read on X
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! Image
uv is a Rust-based Python package manager built to be fast and reliable.

It replaces not just pip but also pip-tools, virtualenv, pipx, poetry, and pyenv, all with a single standalone binary.

Here's a uv cheatsheet for Python devs👇

Let's look at the code next!
1️⃣ Create a new project

To set up a new Python project, run: uv init project-name.

This creates a directory structure, a TOML file, a sample script, and a README.

Check this 👇 Image
2️⃣ Initialize an env.

Although uv automatically initializes a virtual env. in a project, you can explicitly create a virtual env. with `uv venv` command.

Activate it as follows:
- MacOS/Linux: source .venv/bin/activate
- Windows: .venv\Scripts\activate

Check this 👇 Image
3️⃣ Install packages

Next, you can add dependencies using `uv add <library-name>` command.

When you add packages, uv updates the toml file and resolves the full dependency tree, generating a lockfile.

Check this 👇 Image
4️⃣ Execute a script

To run a script, use `uv run script[.]py` command.

If a package is not available in your environment but it is used in the script, uv will install it when you run the script, provided the dependency is specified in the toml file.

Check this 👇 Image
5️⃣ Reproduce an env.

Finally, uv gives 100% reproducible installs.

Say you cloned a project that used uv. You can run `uv sync` to precisely match the project.

This works across OS, and even if the project you cloned used a different Python version.

Check this 👇 Image
And that is how you can start using uv.

Note: When you push your project to GitHub, DO NOT add the uv[.]lock file to your gitignore file. This helps uv reproduce the environment when others use your project.

Here is the cheatsheet again for your reference 👇
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!

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More from @akshay_pachaar

Aug 4
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:
What are subagents?

Subagents are like focused teammates for your IDE

Each one:

- Has a specific purpose
- Uses a separate context window
- Can be limited to selected tools
- Follows a custom system prompt

It works independently and returns focused results.
Read 13 tweets
Aug 1
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! 🚀
Here's how our app works:

- LLM processes the query to select a tool
- Converts the query into right format (text/SQL)
- Executes the tool and fetch the output
- Generates a response with enriched context
- Validates the response using Cleanlab's Codex

Now, let's see the code!
Read 14 tweets
Jul 31
"Attention is all you need" implemented from scratch using PyTorch:
This is the paper that revolutionized AI!

Today, we'll implement:

- The complete Transformer architecture
- Multi-Head Attention mechanism
- Encoder-Decoder structure
- Positional Encoding

Everything in clean, educational Python code!

Let's go! 🚀 Image
Here's the full Transformer model that we'll build piece by piece!

Notice the key components:

- Encoder & Decoder stacks
- Multi-head attention layers
- Position-wise feed-forward networks
- Positional encoding

Now let's break it down! 👇 Image
Read 17 tweets
Jul 27
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. Image
Some background!

LLM weights are matrices of numbers adjusted during finetuning.

Most PEFT techniques involve finding a lower-rank adaptation of these matrices—a smaller-dimensional matrix that can still represent the information stored in the original. Image
Read 11 tweets
Jul 25
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! 🚀
1️⃣ Soft-label Distillation

Generate token-level softmax probabilities over the entire corpus using:

- A frozen, pre-trained Teacher LLM
- An untrained Student LLM

Train the Student LLM to match the Teacher's probabilities.

Check this out👇
Read 10 tweets
Jul 24
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! 🚀
The architecture diagram presented below illustrates some of the key components & how they interact with each other!

It will be followed by detailed descriptions & code for each component:
Read 13 tweets

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