Python 3.14 removes gzip friction, enables zstd compression

JavaScript inside Python? I thought it was a joke… until I saw the benchmarks. Last week, I tried PythonMonkey, a project that embeds Mozilla’s SpiderMonkey JS engine directly into the Python runtime. No APIs. No subprocesses. Just raw cross-language execution in one VM. Within minutes, I was running NPM packages from a Python REPL like it was native code. My mind was blown. Zero-copy data sharing - Python lists instantly behave like JS arrays with map, filter, and shared memory under the hood. Bidirectional execution - JS functions become Python callables, and Promises await like coroutines. Node.js interoperability - use require() in Python to load .js, .py, and .json modules. No glue code, no IPC latency. Production-level reliability - Distributive runs it to power NPM workloads in their cloud compute network, stable at v1.1 with Python 3.13 support. This kills the “Python for backend, Node for frontend” divide. The need for microservice bridges between the two might vanish sooner than most realize. If you’re still manually serializing data between Python and Node apps, you’re already behind. The landscape is shifting fast. Follow me to stay ahead of the dev workflow revolution. #Python #JavaScript #DevTools #OpenSource #AIInfrastructure #BackendEngineering #Developers #TechInnovation

🆕 My latest blog is out which covers AWS Lambda Powertools for Python. It's the library I add to every Lambda function before writing any business logic, and I wanted to put everything I've learned from using it across real projects into one comprehensive guide. The post covers the core observability stack: Logger, Tracer, and Metrics - along with utilities like idempotency, batch processing, event routing, and parameters. I included Terraform and SAM examples from two of my own projects to keep things practical. If you're building with Lambda and spending more time on observability boilerplate than business logic, this might save you some effort. Three decorators can replace a surprising amount of code. Check it out! https://lnkd.in/evB8U4np

やばい A developer used OpenAI Codex to rewrite the leaked Claude Code source code in Python, so that storing the code would not violate copyright. https://lnkd.in/gWkVdK6v

Python Reimplementation of Claude Code Agent Now Available for Local Models 📌 Claw Code Agent lets devs run Claude-style coding agents locally-no cloud subscription needed. Built in pure Python, it strips away enterprise bloat for lean, open-source control over tool calls, file edits, and shell commands. Perfect for Python engineers wanting to experiment with agentic workflows using local models like Qwen3-Coder. 🔗 Read more: https://lnkd.in/d7SNVJ4f #Python #Claudecodeagent #Clawcodeagent #Localmodels #Opensource

🔥 Hi Guys, Do you know what is the Importance of List Slicing in Python? Here it is.. We can get the role names from Aws Iam Section. Here is the a sample python script the gets you role names from aws 👇 aws iam list-roles | jq ".Roles[].Arn" -r > roles.txt role_list = [] with open('roles.txt', 'r') as f:     for line in f.readlines():         role_name = line.strip().split('/')[-1]         role_list.append(role_name) print(role_list) f.readlines(): reads the entire file and returns a list of lines in the file. line.strip(): removes any leading and trailing whitespace characters from the line. Input: ARN = 'arn:aws:iam::051826708837:user/AdminUser' Output: AdminUser Do you know any better version of this?. Comment below 👇 #PythonScript #DevOps

⚡ FastStream: Building Event-Driven Apps in Python Made Simple 🐍 As systems become more distributed, event-driven architectures are becoming the standard. That’s where FastStream comes in, a modern Python framework designed to simplify working with message brokers like Kafka, RabbitMQ, or Redis. 🔹 What is FastStream? FastStream brings a FastAPI-like developer experience to streaming and messaging systems, making it easier to build scalable, asynchronous applications. ✅ Why Use FastStream? → Simple & Familiar Syntax : Inspired by FastAPI, easy to learn and use. → Async First : Built for high-performance, event-driven workloads. → Multi-Broker Support : Works with Kafka, RabbitMQ, Redis, and more. → Type Safety : Uses Pydantic models for message validation. → Microservices Ready : Perfect for decoupled, scalable architectures. 💡 Use Cases → Event-driven microservices 🔄 → Real-time data pipelines 📊 → Messaging systems and queues 📬 → Streaming applications ⚡ FastStream helps bridge the gap between traditional APIs and event-driven systems, making it easier for Python developers to build modern, scalable backends. #Python #FastStream #EventDriven #Microservices #Async

Shipping something useful for everyday Python workflows 🐍 My Python Developer Toolkit - 5 Script Pack is live on Codester, featuring tools for log analysis, file organisation, port scanning, .env validation, and mock REST APIs. Built to be simple, practical, and dependency-light with the standard library only. 🔗 Check it out here: https://lnkd.in/ggAzvx3e #Python #PythonDeveloper #SoftwareEngineer #Programming #Coding #Developer #Developers #TechCommunity #Automation #Scripting #API #DeveloperTools #DevTools #Productivity #Code #ProgrammerLife #BuildInPublic #IndieDev #PythonScripts #TechInnovation #Codester #CodeMarketplace #DigitalProducts #ScriptPack #PythonTools #IndieMaker

Imagine being able to write C code using Python? That just became a reality with the PythoC library. PythoC is a Domain-Specific Language (DSL) compiler that allows developers to write C-like programs and create .exe's using standard Python syntax. You can call the compiled library directly from Python like this, # test1.py from pythoc import compile, i32 @compile def add(x: i32, y: i32) -> i32: return x + y # Compile to native code @compile def main() -> i32: return add(10, 20) result = main() print(result) Then run it like this, python test1.py # prints 30 Or you can create a stand-alone .exe to run. My Towards Data Science article takes you through the details. Read it for free below. https://lnkd.in/eWAku8Wg

🚀 Why Modern Python APIs Needed FastAPI (Not Just Another Framework) For years, Python web apps were built on the WSGI model (Flask / Django): 👉 1 request = 1 thread = blocking I/O This worked well when systems were monolithic and mostly compute-heavy. But modern backend architecture changed. Today APIs spend most of their time: - Calling other microservices - Waiting on databases - Talking to external APIs So servers became network-bound rather than CPU-bound. ⚡ The Architectural Shift FastAPI is built on ASGI (async architecture) which allows: - One worker to handle many concurrent requests - Non-blocking network calls using an event loop - Better CPU utilisation and improved cost efficiency Instead of threads sitting idle while waiting on I/O, the server can continue serving other requests. 🧠 What Real Problem It Solves - Thread overhead at high concurrency - Limited throughput in I/O-heavy microservice systems - Difficulty building real-time APIs (WebSockets / streaming) - Inefficient scaling in cloud-native environments In simple terms: FastAPI helps Python services handle modern distributed system latency patterns more efficiently. Why Teams Adopt FastAPI in Practice (DX Advantages) Beyond async scalability, FastAPI improves developer productivity: ✅ Automatic interactive API docs via OpenAPI (/docs) ✅ Built-in request validation using Pydantic models ✅ Type-hint driven development improving correctness and tooling This reduces boilerplate, improves API reliability, and speeds up development cycles. 🔥 Key Takeaway FastAPI is not just “faster Flask”. It represents a shift to async, event-driven backend architecture combined with strong developer ergonomics required for modern distributed systems. #FastAPI #EventDriven #DistributedSystems #APIDesign

Most teams do not reach for #PyFlink because Python feels nicer. They reach for it after paying the production cost of splitting one ML system across two ecosystems: Python for training, Java for prediction, and months lost to subtle feature mismatches, latency, and debugging. That is the real adoption driver. New on ML-Affairs: "If the real source of friction in your system is that your training, feature logic, and model-adjacent code live naturally in Python, then "just use Java #Flink" is not a neutral suggestion. It is an architectural trade, and often an expensive one."