DEV Community: Achille kabasele

Python in 2026: Why I Replaced pip with uv (Complete Guide + Benchmarks)

Achille kabasele — Tue, 24 Mar 2026 13:28:33 +0000

For years, I thought slow pip installs were just part of being a Python developer.
I was wrong.

Working daily on Django APIs, microservices, and CI/CD pipelines, dependency management was always my biggest friction point:

slow builds
broken virtual environments
dependency conflicts
duplicated packages

Then I discovered uv.

And honestly?
👉 It completely changed the way I work.

⚡ What is `uv`?

uv is a blazing-fast Python package manager written in Rust that replaces pip, venv, and partially pyenv.

Built by Astral (the team behind Ruff), it delivers:

⚡ Speed
🔒 Reliability
🧠 Simplicity

❌ Why `pip` is no longer enough

Let’s be honest.

With `pip`

slow installations
inefficient dependency resolution
duplicated packages per project
fragile environments

👉 Result: wasted time + hidden bugs

⚙️ 1. Installation (Windows, macOS, Linux)

One of the biggest advantages of uv:

👉 it can install and manage Python itself

🪟 Windows

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

🍏 macOS / 🐧 Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

⚡ Alternatives

winget install --id=astral-sh.uv -e

brew install uv

🧪 Quick test

pip install uv

⚠️ Limited (depends on your system Python)

🚀 2. Real Workflow with `uv`

Initialize a project

uv init crypto-tracker
cd crypto-tracker

Install dependencies

uv add requests rich

👉 This is where you feel the difference: it’s instant.

Example code

import requests
from rich.console import Console

console = Console()

def fetch_price():
    data = requests.get(
        "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd"
    ).json()
    price = data["bitcoin"]["usd"]
    console.print(f"[bold green]BTC:[/bold green] ${price:,}")

if __name__ == "__main__":
    fetch_price()

Run your code

uv run main.py

👉 No activation
👉 No setup
👉 Just run

🛠️ 3. Windows & Network Survival Guide

⚠️ PowerShell “scripts disabled”

Set-ExecutionPolicy RemoteSigned -Scope CurrentUser

❌ Wrong venv activation

.\.venv\Scripts\activate

🌐 Corporate Wi-Fi / SSL issues

$env:UV_NATIVE_TLS="1"

export UV_NATIVE_TLS=1

🏢 Proxy setup

export HTTP_PROXY="http://proxy:8080"
export HTTPS_PROXY="http://proxy:8080"

📦 Winget not recognized

👉 Update App Installer in Microsoft Store

🧬 4. Why `uv` is insanely fast

🧱 Global cache

packages downloaded once
shared across projects

⚙️ Rust engine

parallel downloads
fast extraction
native performance

🧠 PubGrub algorithm

fast dependency resolution
fewer conflicts
predictable builds

🧱 5. Project Architecture (`uv init`)

crypto-tracker/
├── pyproject.toml
├── uv.lock
├── .venv/
└── main.py

📄 `pyproject.toml`

[project]
name = "crypto-tracker"
version = "0.1.0"
dependencies = ["requests", "rich"]

👉 replaces requirements.txt
👉 official Python standard

🔒 `uv.lock`

👉 locks exact versions
👉 ensures reproducibility

📁 `.venv`

👉 uses hardlinks
👉 fast + lightweight

🧠 Global cache

~/.cache/uv/

🔄 Lifecycle

pyproject.toml → uv.lock → .venv → global cache

⚖️ Before vs After

pip	uv
slow	ultra fast
fragile	reliable
duplication	global cache
manual	automated

🧰 6. Essential commands

Command	Purpose
uv add	install packages
uv run	execute code
uv sync	sync environment
uv tree	debug dependencies
uv python install	install Python

🎬 7. Real Benchmark: `pip` vs `uv`

Same dependencies

requests, numpy, pandas, fastapi

🐢 pip

pip install requests numpy pandas fastapi

⏱️ 15–45 seconds

⚡ uv

uv add requests numpy pandas fastapi

⏱️ 1–3 seconds

📊 Result

Tool	Time
pip	15–45s
uv	1–3s

👉 10x–20x faster

🖥️ Example terminal output

$ uv init crypto-tracker
Initialized project 'crypto-tracker'

$ uv add requests rich
Resolved 12 packages in 120ms
Installed in 340ms

$ uv run main.py
BTC: $67,245

🧪 CI/CD Impact

Before

pip install -r requirements.txt

⏱️ 60–120s

After

uv sync

⏱️ 5–10s

🔥 Real-world impact

After switching to uv:

faster CI pipelines
fewer environment bugs
easier onboarding
better developer experience

🧭 Conclusion

uv is not just another tool.

👉 It’s a shift in how Python projects are built and managed

In 2026:

👉 sticking to pip means staying behind
👉 adopting uv means moving faster, cleaner, smarter

🚀 TL;DR

replaces pip + venv
written in Rust → blazing fast
global cache → no duplication
built-in Python management
developer experience 🔥

👉 Try it now

uv init

💡 Final thought

The first time I saw uv install a project in under 2 seconds…
I thought something was broken.

Chatbots, AI Agents, and Agentic AI: Understanding the Evolution of Intelligent Systems

Achille kabasele — Mon, 16 Mar 2026 02:02:55 +0000

Chatbots, AI Agents, and Agentic AI: Understanding the Evolution of Intelligent Systems

From simple conversational bots to autonomous AI systems.

Artificial Intelligence is rapidly transforming how software interacts with humans and performs tasks. Over the last few years, three important concepts have emerged in modern AI development: Chatbots, AI Agents, and Agentic AI Systems.

While these terms are often used interchangeably, they represent very different levels of intelligence and autonomy. Understanding the distinction between them helps developers, entrepreneurs, and organizations design more powerful digital systems.

In this article, we explore how these technologies differ and how they represent the evolution of intelligent software systems.

The Evolution of AI Systems

Artificial intelligence systems have evolved significantly over time. Early AI-powered tools were designed primarily to respond to user inputs, but modern systems are increasingly capable of making decisions and executing complex tasks autonomously.

This evolution can be summarized in three stages:

Chatbots — systems focused on conversation
AI Agents — systems capable of performing tasks
Agentic AI Systems — systems capable of autonomous planning and collaboration

Figure 1 — Comparison between Chatbots, AI Agents, and Agentic AI systems.

This progression reflects a broader shift in software development: from reactive interfaces to autonomous intelligent systems capable of achieving goals.

What Is a Chatbot?

A chatbot is a software application designed to simulate conversation with users. Chatbots typically rely on Natural Language Processing (NLP) and machine learning models to understand questions and generate responses.

The fundamental behavior of a chatbot is reactive: it waits for user input and then produces an appropriate response.

Typical chatbot workflow

The user sends a message
The chatbot processes the message
The system generates a response
The chatbot returns the answer

Chatbots are commonly used in:

Customer support systems
FAQ assistants
Banking chat interfaces
E-commerce customer service
Website help assistants

For example, a user might ask:

“What are your opening hours?”

The chatbot analyzes the question and replies:

“Our offices are open from 9 AM to 5 PM.”

Chatbots are effective for communication, but they typically do not execute complex actions or workflows.

Figure 2 — Basic workflow of a chatbot interaction system.

What Is an AI Agent?

An AI agent goes beyond conversation. It is a system capable of perceiving its environment, making decisions, and performing actions in order to achieve a specific goal.

Unlike chatbots, AI agents can interact with external systems, tools, or software services.

AI agents usually follow a cycle known as the Perception–Decision–Action loop.

AI Agent Cycle

Perception — gathers information from APIs, databases, sensors, or user inputs
Reasoning — analyzes information and decides what to do
Action — executes tasks using tools or software
Memory — stores context and previous results

This cycle allows AI agents to operate with a higher degree of autonomy.

For example, if a user requests:

“Schedule a meeting with my team tomorrow.”

An AI agent may:

Check the calendar
Identify available time slots
Send invitations to participants
Confirm the meeting

In this case, the system is not just answering a question — it is executing tasks across multiple systems.

Figure 3 — Architecture of an AI Agent showing perception, reasoning, memory, and action components.

What Is an Agentic AI System?

An Agentic AI System represents the next stage of intelligent automation.

These systems are designed to plan strategies, coordinate multiple tasks, and adapt to changing conditions with minimal human supervision.

Instead of performing a single task, an agentic system can break complex goals into multiple steps and execute them autonomously.

For example, consider the request:

“Prepare a market analysis report about electric vehicles.”

An agentic AI system could:

Search multiple data sources
Collect relevant statistics
Analyze market trends
Generate charts and visualizations
Write a structured report
Export the final document

This entire workflow may occur without step-by-step human guidance.

Figure 4 — Architecture of an Agentic AI system coordinating multiple agents and tools.

Multi-Agent Collaboration

One of the defining features of agentic AI systems is multi-agent collaboration.

Instead of relying on a single AI component, these systems often involve multiple specialized agents working together.

Together, these agents behave like a team of digital collaborators, each responsible for a specific part of the workflow.

Figure 5 — Multi-agent collaboration in an agentic AI system.

Key Differences Between the Three Systems

In simple terms:

Chatbots talk
AI agents act
Agentic AI systems plan and coordinate complex tasks

Why Agentic AI Matters

The emergence of agentic AI systems signals an important shift in how software operates.

Instead of designing systems that simply respond to commands, developers are beginning to build systems capable of:

Managing processes
Coordinating workflows
Achieving goals independently

Industries already exploring agentic AI include:

Software development
Cybersecurity
Finance
Healthcare
Logistics
Digital services
Enterprise automation

These systems are often described as digital workers, capable of assisting humans in complex tasks.

Conclusion

The progression from chatbots to AI agents and agentic AI systems reflects the rapid evolution of artificial intelligence.

Chatbots improved communication between humans and machines
AI agents introduced the ability for software to perform tasks
Agentic AI systems now enable autonomous workflows and multi-agent collaboration

As these technologies continue to evolve, the boundary between traditional software and intelligent digital collaborators will become increasingly blurred.

The future of AI-powered systems will likely involve autonomous agents working together to solve complex problems and augment human capabilities.

Scaling Django APIs: How I Reduced Database Load by 80% with Redis and Optimized Queries

Achille kabasele — Tue, 10 Mar 2026 16:28:15 +0000

Introduction

In modern software development, building an API that “just works” is only the first step. The real challenge begins when your user base grows. As a Software Engineer, I’ve seen many projects struggle with latency issues and database bottlenecks as they attempt to scale.

In this article, I will show you how to transform a standard Django REST API into a high-performance system using PostgreSQL, Redis, and Docker. We will focus on real-world optimizations that ensure your backend remains responsive, regardless of the load.

The Hidden Performance Killer: The N+1 Query Problem Most performance issues in Django start with the "N+1 query problem". This happens when your code makes one query to fetch a list of objects, and then N additional queries to fetch related data for each object.

The Slow Way (Avoid this):

# views.py
def get_queryset(self):
    # This triggers 1 query for articles + 50 queries for 50 authors!
    return Article.objects.all()

The Solution: SQL Optimization with select_related To fix this, we use SQL Joins. Django provides select_related for ForeignKey relationships. It allows us to fetch all the data in one single database hit.

The Optimized Way:

api/views.py

from rest_setframework import viewsets
from .models import Article
from .serializers import ArticleSerializer

class ArticleViewSet(viewsets.ReadOnlyModelViewSet):
    """
    Optimized ViewSet: Uses select_related to join Author table at the SQL level.
    This reduces database overhead significantly.
    """
    serializer_class = ArticleSerializer

    def get_queryset(self):
        # Fetching articles and their authors in one single query
        return Article.objects.select_related('author').all()

Adding a Speed Layer with Redis Caching Even with optimized SQL, hitting the database for every request is expensive. For data that doesn't change every second (like articles), we should use Caching.

By using Redis, we store the API response in memory. The next time a user requests the same data, it’s served in milliseconds without even touching PostgreSQL.

# api/views.py 
from django.utils.decorators import method_decorator
from django.views.decorators.cache import cache_page

class ArticleViewSet(viewsets.ReadOnlyModelViewSet):
    # ... existing code ...

    # Cache the result for 15 minutes (900 seconds)
    @method_decorator(cache_page(60 * 15))
    def dispatch(self, *args, **kwargs):
        return super().dispatch(*args, **kwargs)

Containerizing for Consistency: Docker & Redis To ensure this works in production, we use Docker. This eliminates the "it works on my machine" excuse and makes deployment to any cloud provider seamless.

The docker-compose.yml architecture:

services:
  db:
    image: postgres:15 # Relational data persistence
  redis:
    image: redis:alpine # High-speed in-memory cache
  web:
    build: .
    depends_on:
      - db
      - redis
    # Automating migrations and starting the server
    command: >
      sh -c "python manage.py migrate && python manage.py runserver 0.0.0.0:8000"

Proof of Quality: Unit Testing As engineers, we don't guess; we verify. Adding tests ensures that our performance optimizations don't break the logic.

# api/tests.py
from django.urls import reverse
from rest_framework.test import APITestCase
from django.core.cache import cache

class PerformanceTest(APITestCase):
    def test_cache_is_populated(self):
        url = reverse('article-list')
        self.client.get(url) # First hit
        # Verify that the cache key exists in Redis
        self.assertTrue(cache.has_key(f"views.decorators.cache.cache_page..{url}"))

Conclusion
Building scalable systems is about making conscious decisions on how data flows through your architecture. By combining Django’s ORM optimization, Redis caching, and Docker orchestration, we can build robust backends ready for the real world.

Are you looking to scale your next digital product?
I specialize in building high-performance APIs and scalable architectures designed for global reach.

🚀 Let’s connect and build something great together:

LinkedIn: Achille Kabasele

GitHub: @Kabasele754

Full Project Source Code: django-redis-high-perf-api

Python #Django #Redis #Docker #SoftwareEngineering #BackendOptimization #WebScalability #FullStackn

DEV Community: Achille kabasele

Python in 2026: Why I Replaced pip with uv (Complete Guide + Benchmarks)

⚡ What is uv?

❌ Why pip is no longer enough

With pip

⚙️ 1. Installation (Windows, macOS, Linux)

🪟 Windows

🍏 macOS / 🐧 Linux

⚡ Alternatives

🧪 Quick test

🚀 2. Real Workflow with uv

Initialize a project

Install dependencies

Example code

Run your code

🛠️ 3. Windows & Network Survival Guide

⚠️ PowerShell “scripts disabled”

❌ Wrong venv activation

🌐 Corporate Wi-Fi / SSL issues

🏢 Proxy setup

📦 Winget not recognized

🧬 4. Why uv is insanely fast

🧱 Global cache

⚙️ Rust engine

🧠 PubGrub algorithm

🧱 5. Project Architecture (uv init)

📄 pyproject.toml

🔒 uv.lock

📁 .venv

🧠 Global cache

🔄 Lifecycle

⚖️ Before vs After

🧰 6. Essential commands

🎬 7. Real Benchmark: pip vs uv

Same dependencies

🐢 pip

⚡ uv

📊 Result

🖥️ Example terminal output

🧪 CI/CD Impact

Before

After

🔥 Real-world impact

🧭 Conclusion

🚀 TL;DR

👉 Try it now

💡 Final thought

Chatbots, AI Agents, and Agentic AI: Understanding the Evolution of Intelligent Systems

Chatbots, AI Agents, and Agentic AI: Understanding the Evolution of Intelligent Systems

The Evolution of AI Systems

What Is a Chatbot?

Typical chatbot workflow

What Is an AI Agent?

AI Agent Cycle

What Is an Agentic AI System?

Multi-Agent Collaboration

Key Differences Between the Three Systems

Why Agentic AI Matters

Conclusion

Scaling Django APIs: How I Reduced Database Load by 80% with Redis and Optimized Queries

api/views.py

Python #Django #Redis #Docker #SoftwareEngineering #BackendOptimization #WebScalability #FullStackn

⚡ What is `uv`?

❌ Why `pip` is no longer enough

With `pip`

🚀 2. Real Workflow with `uv`

🧬 4. Why `uv` is insanely fast

🧱 5. Project Architecture (`uv init`)

📄 `pyproject.toml`

🔒 `uv.lock`

📁 `.venv`

🎬 7. Real Benchmark: `pip` vs `uv`