Best of Python — July 2025

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Article
DigitalOcean Community·45w
LangChain Explained: The Ultimate Framework for Building LLM Applications
LangChain is an open-source Python framework that simplifies building LLM applications by providing standard interfaces for chat models, embeddings, and vector stores. It offers key components like chains for sequential operations, agents for autonomous decision-making, memory for conversation context, tools for external integrations, and vector stores for retrieval-augmented generation. The framework abstracts away complexity when connecting LLMs to external data sources and APIs, making it easier to build chatbots, question-answering systems, and other AI applications without reinventing common functionality.
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Article
Awesome Go·43w
Go vs Python vs Rust: Which One Should You Learn in 2025? Benchmarks, Jobs & Trade‑offs
Compares Go, Python, and Rust across performance benchmarks, memory efficiency, developer productivity, ecosystem maturity, and 2025 salary trends. Rust leads in raw speed (2x faster than Go, 60x faster than Python), Go excels in cloud-native development with balanced performance and simplicity, while Python dominates AI/ML with fastest development cycles. Salary ranges show Rust ($150K-$210K), Go ($140K-$200K), and Python ($130K-$180K). Recommends Python for AI/ML and prototyping, Go for microservices and DevOps, Rust for performance-critical systems, with hybrid approaches becoming common in production environments.
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Article
TestDriven.io·43w
Developing a Real-time Dashboard with FastAPI, Postgres, and WebSockets
A comprehensive tutorial demonstrating how to build a real-time inventory tracking dashboard using FastAPI, PostgreSQL, and WebSockets. The guide covers setting up database triggers with LISTEN/NOTIFY for automatic change detection, implementing WebSocket connections for live data streaming, creating RESTful API endpoints for inventory management, and building a responsive frontend that updates instantly when data changes occur.
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Article
Javarevisited·43w
10 AI Frameworks and Libraries Every Developer Should Learn in 2025
A comprehensive guide covering 10 essential AI frameworks and libraries for developers in 2025, including LangChain for building LLM applications, vector databases like Pinecone and Weaviate for semantic search, multi-agent systems with CrewAI, fine-tuning techniques like LoRA, and automation tools like N8N. Each framework includes practical use cases and recommended learning resources to help developers build production-ready AI applications.
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Article
freeCodeCamp·43w
How In-Memory Caching Works in Redis
Redis is an in-memory data store that dramatically improves application performance by caching frequently accessed data in RAM. The guide covers Redis installation, core data types (strings, lists, hashes, sets, sorted sets), and Python integration. Key features include automatic key expiration, persistence options, and scalability through replication and clustering. Common use cases include API response caching, session management, and real-time data storage, making Redis essential for building fast, scalable web applications.
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Article
Claudette·45w
Python For Everything
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Article
Planet Python·42w
Python Roadmap with Free Courses/Certifcates to High-Paying Jobs
Python leads to six-figure salaries when applied in specialized fields like AI, data science, cybersecurity, and automation. Five free certifications are recommended: Cisco's Programming Essentials for foundational skills, IBM Data Science Professional Certificate for data scientist roles, freeCodeCamp's Machine Learning with Python for ML engineering, Information Security certification for cybersecurity programming, and Jovian's Pandas course for data analysis mastery. Success requires specializing Python skills within high-demand domains rather than learning the language in isolation.
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Article
Medium·43w
How to get Kimi-K2 Free API?
Moonshot AI released Kimi K2, a 1 trillion parameter open source model that outperforms Claude 4 Sonnet, GPT 4.1, and DeepSeek V3. While the model requires significant GPU resources to run locally, developers can access it for free through OpenRouter's unified API platform. The guide provides step-by-step instructions to obtain a free API key and includes sample Python code for making requests to the Kimi K2 model through OpenRouter's endpoint.
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Article
freeCodeCamp·44w
How to Transform JSON Data to Match Any Schema
Learn how to transform JSON data to match specific schemas using two approaches: pure Python and pandas. The tutorial covers loading JSON files, defining target schemas, cleaning and renaming fields, and validating the output. It demonstrates transforming customer records by removing unwanted fields and renaming others, while comparing performance between pure Python (faster for simple tasks) and pandas (better for complex datasets with built-in data cleaning methods).
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Article
SingleStore·44w
How to Build a RAG Knowledge Base in Python for Customer Support
A comprehensive guide to building a Retrieval-Augmented Generation (RAG) system for customer support using Python, LangChain, OpenAI, and SingleStore. The tutorial covers setting up a vector database, converting documents into embeddings, implementing semantic search, and generating contextual answers. Real-world case studies show 28.6% reduction in issue resolution time. The step-by-step implementation includes environment setup, database configuration, embedding creation, and API endpoint development for instant, accurate support responses.
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Article
Towards Data Science·45w
Run Your Python Code up to 80x Faster Using the Cython Library
Cython is a superset of Python that converts Python code to C for significant performance improvements. The article demonstrates a four-step process: load Cython extension, use %%cython magic command, type function parameters, and declare variables with cdef. Three examples show dramatic speedups: nested loops (80x faster), Monte Carlo simulation (10x faster), and image processing (25x faster). The guide covers both Jupyter notebook usage and standalone Python script compilation using setup.py files.
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Article
Towards Data Science·43w
I Analysed 25,000 Hotel Names and Found Four Surprising Truths
A data scientist analyzed 25,000 hotel names worldwide using the Hotel Data API to uncover why hotels are named after cities they're not located in. The study revealed that Paris is the most borrowed city name (1,100+ hotels), followed by Vienna and Rome. Three main reasons emerged: proximity for search visibility, branding to evoke luxury and sophistication, and historical tradition dating back to 18th-century aristocratic travel patterns. The analysis used Python, pandas, and geographic distance calculations to map naming patterns across countries.
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Article
Tigris·45w
Standardizing Python Environments with Development Containers
Development containers solve Python environment management complexity by providing consistent, containerized development environments. Using devcontainer.json files, teams can standardize Python versions, dependency management tools like uv, and editor configurations. This approach eliminates common Python setup issues, enables instant project onboarding, and works across different machines and GitHub Codespaces.
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Video
YouTube·44w
Complete Guide to Build and Deploy an AI Agent with Docker Containers and Python
A comprehensive guide covering Docker fundamentals and building AI agents with Python. Starts with Docker basics including container creation, image building, and Docker Compose usage. Progresses through setting up FastAPI web applications, integrating databases, and ultimately implementing AI agents using Langchain and Langraph. Covers both local development with Docker containers and deployment strategies using services like Railway and Digital Ocean. Demonstrates how to use both managed LLM services and open-source AI models available through DockerHub.
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Article
Armin Ronacher·42w
From Async/Await to Virtual Threads
Virtual threads could provide a simpler alternative to async/await in Python by combining the ergonomics of blocking APIs with the performance benefits of concurrent execution. The approach would use structured concurrency through thread groups, eliminating the need for colored functions while moving complexity into the runtime. This model would allow developers to write sequential-looking code that runs concurrently, with automatic thread scheduling and cancellation handling built into the interpreter.
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Article
Nordic APIs·42w
What is Code Refactoring?
Code refactoring involves restructuring existing code without changing its functionality to improve readability, maintainability, and performance. The practice is particularly crucial for APIs where performance and reliability are paramount. Refactoring can reduce execution time by 15%, decrease complexity by 30%, and speed up developer onboarding by 62%. Key best practices include following the DRY principle, implementing comprehensive testing before refactoring, favoring composition over inheritance, and making incremental changes rather than major overhauls. The article demonstrates these concepts through Flask API examples, showing how poorly structured code can be transformed into clean, efficient, and maintainable systems.
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Article
Data Engineer Things·45w
The Ultimate Roadmap to Become a Data Engineer in 2025 (With Free Resources)
A comprehensive guide for becoming a data engineer in 2025, covering essential skills like SQL, Python, and data modeling, along with big data technologies like Apache Spark and cloud platforms. The roadmap emphasizes free learning resources and practical experience, highlighting that mastering core principles enables quick adaptation to new tools in the rapidly evolving data engineering landscape.
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Article
The Palindrome·43w
What Elden Ring Has Taught Me About Hard Things
Using the challenging video game Elden Ring as a metaphor, this piece explores how to tackle difficult problems in programming and life. The key insights include choosing your battles wisely, preparing thoroughly before attempting challenges, using all available tools without shame, and focusing on results rather than arbitrary constraints. The author emphasizes that success comes from strategic thinking and leveraging resources effectively, not from brute force or artificial limitations.
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Video
Tech With Tim·43w
How I Mastered Data Structures and Algorithms
A practical guide to mastering data structures and algorithms efficiently for coding interviews. The approach emphasizes choosing an easy language like Python, learning theory quickly without over-studying, practicing 75-100 quality problems instead of hundreds, and simulating real interview conditions with mock interviews and whiteboard practice. The key is quality over quantity, proper preparation methodology, and building confidence through thorough practice.
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Article
Medium·43w
SmolLM3 : The best small LLM for everything
SmolLM3 is a 3-billion parameter language model from Hugging Face that outperforms larger models through extensive training on 11.2 trillion tokens. Key features include extended thinking mode for step-by-step reasoning, native 64k token context length (extendable to 128k), multilingual support for six languages, and built-in tool calling capabilities. The model excels in benchmarks for math, reasoning, and programming tasks while being deployable on edge devices and single-GPU setups through various frameworks like transformers, vLLM, and llama.cpp.
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Article
Planet Python·43w
Another Python TUI Package
Asciimatics is a Python library for creating text-based user interfaces (TUIs) with a retro aesthetic. The tutorial covers installation via pip, creating a colorful Hello World application that displays random text across the terminal, and building forms with widgets like text inputs and buttons using layouts and frames. While not as modern as Textual, Asciimatics offers a charming old-school approach to terminal application development with its Screen, Scene, and Effects hierarchy.
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Article
MotherDuck·43w
Summer Data Engineering Roadmap
A comprehensive 3-week structured learning roadmap for aspiring data engineers covering foundational skills (SQL, Git, Linux), core engineering concepts (Python, cloud platforms, data modeling), and advanced topics (streaming, data quality, DevOps). The guide provides curated resources and a progressive learning path from beginner to intermediate level, emphasizing practical skills needed for full-stack data engineering roles.
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Article
InfoWorld·45w
What you absolutely cannot vibe code right now
Large language models excel at generating repetitive, well-understood code like CRUD applications and web development, but struggle significantly with algorithmic problems and novel implementations. Through practical experience porting a patch system from Python to TypeScript, the author demonstrates that LLMs fail at medium to hard difficulty problems where they cannot rely on well-known templates. While LLMs are valuable tools for routine development tasks, they require human oversight and cannot autonomously handle complex algorithmic design or domains with limited training examples.
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Video
Tech With Tim·42w
Python Advanced MCP Server Tutorial (Authentication, Databases & More)
A comprehensive tutorial covering advanced MCP (Model Context Protocol) server development in Python using FastMCP. The guide demonstrates implementing OAuth authentication with Stitch, setting up HTTP transport, creating custom tools and resources, and building a React frontend for user authentication. Key topics include MCP architecture, client-server communication, bearer token authentication, database integration with SQLAlchemy, and production-ready features beyond basic MCP server setup.
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Article
Learn Python·44w
I built a Learn Python desktop Application with python to learn python 😂
A developer shares their journey of creating an offline Python learning desktop application using PyQt. Starting as a personal solution to avoid browser tabs and unreliable internet during learning, the project evolved into a cross-platform desktop app with integrated code editor and terminal. The post covers challenges with PyInstaller packaging, debugging cross-platform issues, and the satisfaction of building something useful for other learners.
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