Anthropic Leaks Source Code, Deploy Claude Opus 4.6 for Free

An expert comparison of Flask and FastAPI for Python backends. Learn architectural trade-offs, deployment patterns with Docker and Kubernetes, performance tuning, and business impact for New Zealand projects.

Shipping Python code shouldn’t feel like rolling dice in production. Modern tooling has quietly changed the game — not by adding complexity, but by removing entire classes of bugs before they ever exist In my latest Towards Data Science article I break down how a lightweight but powerful toolchain can turn your dev pipeline into a safety net: black → zero-effort format consistency ruff → lightning-fast linting pytest → confidence through real, maintainable tests mypy → catching type-related bugs before runtime py-spy → understanding performance without touching code pre-commit → enforcing all of the above automatically The real takeaway isn’t the tools themselves — it’s how combining them creates a feedback loop that catches issues early, standardizes quality, and speeds up development instead of slowing it down. If your pipeline still relies on “we’ll catch it in review” or “we’ll fix it later”… this is worth your time. Read the full breakdown and setup guide: https://lnkd.in/ewuXn6NF

We recently received requests from clients around Python, Machine Learning, and Flask. Before diving into the more complex topics, we decided to start with a solid foundation: building a simple CRUD REST API using Flask and SQLite. No ORMs. No extra dependencies. Just Flask, Python's built-in sqlite3, and Pytest. The goal is straightforward: understand how a REST API actually works from the ground up, before adding layers of abstraction on top of it. It is the kind of foundation that makes everything else easier to learn and easier to debug. In this tutorial, we cover: → Structuring a Flask project with the application factory pattern → Managing database connections per request using Flask's g object → Building five CRUD endpoints with proper validation and error handling → Writing a complete Pytest suite with isolated test databases This is the first article in what will become a series. JWT authentication is coming up next. Read the full tutorial here: https://lnkd.in/g_DwkMDk

Ansible_5 Indentation and Whitespace Like Python, YAML uses space indentation to reduce the number of interpunction characters. We use two spaces as a standard. For readability, we prefer to add whitespace between each task in a playbook, and between sections in files. Strings In general, you don’t need to quote YAML strings. Even if there are spaces, you don’t need to quote them. For example, this is a string in YAML: this is a lovely sentence The JSON equivalent is as follows: "this is a lovely sentence" Use single quotes for literal values that should not be evaluated, like version numbers and floating point numbers, or strings with reserved characters like colons, brackets, or braces. Booleans YAML has a native Boolean type and provides you with a variety of values that evaluate to true or false. These are all Boolean true values in YAML: true, True, TRUE, yes, Yes, YES, on, On, ON JSON only uses: true These are all Boolean false values in YAML: false, False, FALSE, no, No, NO, off, Off, OFF JSON only uses:false Lists YAML lists are like arrays in JSON and Ruby, or lists in Python.The YAML specification calls these sequences, but we call them lists here to be consistent with the official Ansible documentation. Indent list items and delimit them with hyphens. shows: - My Fair Lady - Oklahoma - The Pirates of Penzance YAML also supports an inline format for lists, with comma-separated values in square brackets: shows: [ My Fair Lady , Oklahoma , The Pirates of Penzance ] Dictionaries YAML dictionaries are like objects in JSON, dictionaries in Python, hashes in Ruby, or associative arrays in PHP The YAML specification calls them mappings, but we call them dictionaries here to be consistent with the Ansible documentation. address: street: Main Street appt: 742 city: Logan state: Ohio YAML also supports an inline format for dictionaries, with comma separated tuples in braces: address: { street: Main Street, appt: '742', city: Logan, state:Ohio} Multiline Strings You can format multiline strings with YAML by combining a block style indicator (| or >) If you want one string across multiple lines: Using | message: | This is line one This is line two This is line three Using > message: > This is line one This is line two This is line three output will be This is line one This is line two This is line three

I was going through the Python 3.15 release notes recently, and it’s interesting how this version focuses less on hype and more on fixing real-world developer pain points. Full details here: https://lnkd.in/gSvcuvWg Here’s what stood out to me, with practical examples: --- Explicit lazy imports (PEP 810) Problem: Your app takes forever to start because it imports everything upfront. Example: A CLI tool importing pandas, numpy, etc. even when not needed. With lazy imports: import pandas as pd # only loaded when actually used Result: Faster startup time, especially for large apps and microservices. --- "frozendict" (immutable dictionary) Problem: Configs get accidentally modified somewhere deep in your code. Example: from collections import frozendict config = frozendict({"env": "prod"}) config["env"] = "dev" # error Result: Safer configs, better caching keys, fewer “who changed this?” moments. --- High-frequency sampling profiler (PEP 799) Problem: Profiling slows your app so much that results feel unreliable. Example: You’re debugging a slow API in production. Result: You can profile real workloads without significantly impacting performance. --- Typing improvements Problem: Type hints get messy in large codebases. Example: from typing import TypedDict class User(TypedDict): id: int name: str Result: Cleaner type definitions, better maintainability, stronger IDE support. --- Unpacking in comprehensions Problem: Transforming nested data gets verbose. Example: data = [{"a": 1}, {"b": 2}] merged = {k: v for d in data for k, v in d.items()} Result: More concise and readable transformations. --- UTF-8 as default encoding (PEP 686) Problem: Code behaves differently across environments. Result: More predictable behavior across systems, fewer encoding-related bugs. --- Performance improvements Real world impact: Faster APIs, quicker scripts, and better resource utilization. --- Big takeaway: Python 3.15 is all about practical improvements: - Faster startup - Safer data handling - Better debugging - More predictable behavior Still in alpha, so not production-ready. But it clearly shows where Python is heading. #Python #Backend #SoftwareEngineering #Developers #DataEngineering

Typed-FFMpeg 4.0 Release I built typed-ffmpeg, a Python package that lets you build FFmpeg filter graphs with full type safety, autocomplete, and validation. It’s inspired by ffmpeg-python, but addresses long-standing issues like lack of IDE support and fragile CLI strings. What’s New in v4.0: TypeScript support — Full TypeScript bindings with the same API as Python. Works in Node.js and the browser. Code-generated from FFmpeg source, so every filter and option is typed. parse() — reverse-engineer any FFmpeg command — Paste an FFmpeg CLI string and get back a typed filter graph object, in both Python and TypeScript. Useful for learning, migrating legacy scripts, or building tools on top of FFmpeg. Per-version packages — Instead of one 10 MB bundle with all FFmpeg versions, you now install only what you need: pip install typed-ffmpeg-v7 (~300kb). Packages exist for FFmpeg 5 through 8. FFmpeg 8.0 support — Full compatibility with the latest FFmpeg release. GitHub: https://lnkd.in/gHZAV7QG I’d love feedback, bug reports, or ideas. Thanks! — David (maintainer)

GoScrapy is a high-performance web scraping framework for Go, designed with the familiar architecture of Python's Scrapy. It provides a robust, developer-centric experience for building sophisticated data extraction systems, purposefully crafted for those making the leap from Python to the Go ecosystem. Why GoScrapy? While low-level scraping libraries are powerful, many teams require the high-level architectural framework established by Scrapy. GoScrapy brings this architectural discipline natively to Go, organizing your request callbacks, middlewares, and pipelines into a structured, manageable workflow. Instead of manually orchestrating retries, cookie isolation, or database handoffs, GoScrapy provides the engine that powers your spiders. You focus purely on the extraction logic; the framework manages the high-throughput lifecycle and concurrency in the background.

Day 10: Python Code Tools — When Language Fails, Logic Wins 🐍 Welcome to Day 10 of the CXAS 30-Day Challenge! 🚀 We’ve connected our agents to external APIs (Day 9), but what happens when you need to perform complex calculations or multi-step logic that doesn't require a database call? The Problem: The "Calculator" Hallucination LLMs are incredible at understanding context, but they are not calculators. They are probabilistic next-token predictors. If you ask an LLM to calculate a 15% discount on a $123.45 cart total with a weight-based shipping surcharge, it might give you an answer that looks right but is mathematically wrong. In an enterprise environment, "close enough" isn't good enough for billing. The Solution: Python Code Tools In CX Agent Studio, you can empower your agent with deterministic logic by writing custom Python functions directly in the console. How it works: You define a function in a secure, server-side sandbox. The LLM's Role: The model shifts from calculator to orchestrator. It extracts the variables from the conversation (e.g., weight, location, loyalty tier), calls your Python tool, and receives an exact, guaranteed result. Safety First: The code runs in a secure, isolated sandbox, ensuring enterprise-grade security while giving your agent "mathematical superpowers." 🚀 The Day 10 Challenge: The EcoShop Shipping Calculator EcoShop needs a reliable way to quote shipping fees. The rules are too complex for a prompt: Base fee: $5.00 Weight surcharge: +$2.00 per lb for every pound above 5 lbs. International: Flat +$15.00 surcharge. Loyalty: Gold (20% off), Silver (10% off). Your Task: Write the Python function for this logic. Focus on handling the weight surcharge correctly (including fractions of a pound) and applying the loyalty discount to the final total. Stop asking your LLM to do math. Give it a tool instead. 🔗 Day 10 Resources 📖 Full Day 10 Lesson: https://lnkd.in/gGtfY2Au ✅ Day 9 Milestone Solution (OpenAPI): https://lnkd.in/g6hZbtGX 📩 Day 10 Challenge Deep Dive (Substack): https://lnkd.in/g6BM8ESp Coming up tomorrow: We wrap up the week by looking at Advanced Tool Orchestration—how to manage multiple tools without confusing the model. See you on Day 10! #AI #AgenticAI #GenerativeAI #GoogleCloud #Python #LLM #SoftwareEngineering #30DayChallenge #AIArchitect #DataScience #CXAS

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

This is bigger than it looks. First, Understand the Problem. You buy a powerful server with 10 CPU cores. You build a Python API. You deploy it. Python uses 1 core. The other 9 sit there. Idle. Doing nothing. You just paid for 10, got 1. This wasn't a bug. It was a design decision from the 1990s called the GIL — Global Interpreter Lock. A rule that said: only ONE thread runs at a time, no matter how many cores you have. Why did it exist? It made Python safer and simpler to build back then. Memory management was easier when only one thing ran at a time. It was a smart tradeoff — for 1991. For 2025? Not so much. Since Python couldn't use multiple cores in one process, the solution was: → Run 10 separate Python processes instead of 10 threads → Each process gets its own RAM, its own startup time, its own everything → 10 processes × 500MB RAM = 5GB just to use the machine you already paid for It worked. But it was expensive, wasteful, and messy. Teams switched to Go or Node.js specifically because of this. What Actually Changed ? 🔹 Python 3.13 (October 2024) → Free-threaded build introduced. Experimental. 🔹 Python 3.14 (2025) → Free-threaded officially supported. No longer experimental. Still optional. Note: The GIL hasn't been deleted forever. It's been made OPTIONAL. You choose to disable it. This was a deliberate, careful decision — the Python team didn't want to break the entire ecosystem overnight. FastAPI 0.136.0 now officially supports running on this free-threaded Python. So What Does This Actually Mean? Remember that 10-core machine? With free-threaded Python, FastAPI can now actually use those 10 cores — inside a single process — running threads in true parallel. Real benchmark numbers: → 5 threads on standard Python (with GIL): same speed as 1 thread. No improvement. → 5 threads on free-threaded Python (no GIL): 4.8x faster. In practical terms for your API: → Same traffic, fewer servers needed → Fewer servers = less RAM, less cost, less complexity → Response times improve under heavy load → Scaling becomes a choice, not a survival requirement ━━━ Who Should Pay Attention? ━━━ If you're building: 🔹 ML inference APIs — running a model on every request 🔹 Data processing endpoints — transforming, aggregating, scoring 🔹 Real-time pipelines — processing events as they come 🔹 Document parsing — PDFs, contracts, files at volume 🔹 Any API that actually computes something, not just fetches from a DB The GIL was also acting as an invisible safety net — it prevented two threads from touching the same data at the same time accidentally. Without it, if two threads modify the same variable simultaneously — you can get corrupted data or crashes. These bugs are hard to reproduce and painful to debug. The gains are real. But they require intentional adoption. If you're building Python APIs, this release deserves more than a scroll. Read the changelog. Test it. The ceiling just got raised. Thank you FastAPI