Flask vs FastAPI for Python Backends

Your Django app went from 200MB to 8GB RAM usage in three weeks. Memory leaks don't crash dramatically—they creep up slowly until your servers start swapping and alerts start screaming. This guide shows you how to profile Python applications in production using memory_profiler and tracemalloc without causing downtime or performance impact. Learn to catch circular references, global variable accumulation, and resource leaks before they kill your application. #Python #DevOps #PerformanceOptimization #Django Learn More: https://lnkd.in/eWe2bRhT

🚨 Anthropic accidentally leaked their entire source code yesterday. How to Deploy Anthropic Claude Opus 4.6 For free 😄 Download Repo: https://lnkd.in/eHyiGhpU What this repo contains The project has two build paths: a Python workspace under src/ a Rust port under rust/ So “build this code” can mean either: run the Python version, or compile the Rust version. Option A: Build and run the Python workspace 1) Install Python Make sure you have Python 3 installed. Check: python3 --version 2) Go into the repo folder cd claw-code 3) Inspect the Python workspace The README says the active Python code is in src/ and tests are in tests/. You can confirm: ls ls src ls tests 4) Run the main Python entrypoint The README shows these commands: python3 -m src.main summary python3 -m src.main manifest python3 -m src.main subsystems --limit 16 These are the first commands to try because src.main is the CLI entrypoint. 5) Run tests Use the verification command from the README: python3 -m unittest discover -s tests -v That checks whether the Python workspace is functioning as expected. 6) Run additional Python inspection commands The README also lists: python3 -m src.main parity-audit python3 -m src.main commands --limit 10 python3 -m src.main tools --limit 10 Use those after the basic commands work. Option B: Build the Rust port 1) Install Rust You need Cargo and Rust. Check: rustc --version cargo --version 2) Enter the Rust folder cd claw-code/rust 3) Build in release mode The README gives the exact build command: cargo build --release That is the official Rust build step for this repo. 4) Run the built binary The README says there is a crates/claw-cli crate, so after building, the binary is likely under: ./target/release/claw-cli If that name does not exist, inspect the release folder: ls target/release The CLI crate is identified in the README as crates/claw-cli, which strongly suggests the executable will be named claw-cli. Recommended build order If you are new to the repo, do it in this order: Path 1: easiest start git clone https://lnkd.in/eHyiGhpU cd claw-code python3 -m src.main summary python3 -m unittest discover -s tests -v Path 2: compile Rust cd rust cargo build --release This order makes sense because the README says the repo is now “Python-first,” while the Rust workspace is the systems-language port. Full step-by-step from scratch # 1) Clone git clone https://lnkd.in/eHyiGhpU cd claw-code # 2) Check Python python3 --version # 3) Try the Python CLI python3 -m src.main summary python3 -m src.main manifest python3 -m src.main subsystems --limit 16 # 4) Run tests python3 -m unittest discover -s tests -v # 5) Optional: inspect parity and inventories python3 -m src.main parity-audit python3 -m src.main commands --limit 10 python3 -m src.main tools --limit 10 # 6) Build Rust version cd rust cargo build --release # 7) Inspect compiled binaries ls target/release

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

Most Python automation scripts never become products. Not because the logic is weak, but because delivery is hard. A quick UI wrapper eventually hits a ceiling. Sharing scripts over Slack is not a user experience. And a local environment does not scale across team. I wrote about an architecture pattern that solves this: → keep Python as the execution engine → wrap it behind a FastAPI layer → build a Next.js frontend against that API → bundle the backend locally as a sidecar → deploy it on the web → package the same app as a click-and-run desktop tool using Tauri Same business logic. Different delivery surfaces. No rewrite. From “Works on My Machine” to a Real Product - https://lnkd.in/gyZYzEbT

Python developers in 2026 are sitting on a goldmine and not using it. You already know FastAPI. You already know Django. Your CRUD is clean. Your endpoints are solid. Your logic is tight. But here's the thing That's the baseline now. Not the advantage. Every developer ships CRUD. Not every developer ships a product that thinks. And the good news? If you're already in Python you're one integration away. Python is the only language where the gap between "CRUD app" and "AI-powered product" is measured in hours, not months. Here's what that gap looks like in practice: → Add openai or anthropic SDK — your app now understands user input, not just stores it → Plug in LangChain — your endpoints start making decisions, not just returning rows → Use scikit-learn or Prophet — your FastAPI routes now predict, not just fetch → Connect Celery + an AI model — your background tasks now act intelligently on patterns → Drop in pgvector with PostgreSQL — your database now does semantic search, not just SQL filters This is not a rewrite. This is an upgrade. What CRUD alone gives your users in 2026: ❌ The same experience on day 1 and day 500 ❌ Manual decisions they have to make themselves ❌ A product that stores their data but never understands it ❌ A reason to switch the moment something smarter appears What Python + AI gives your users in 2026: ✅ An app that learns their behavior and adapts ✅ Recommendations, predictions and alerts automatically ✅ A product that gets more valuable the more they use it ✅ A reason to stay and a reason to tell others The architecture stays familiar. FastAPI route → AI layer → response. You're not rebuilding anything. You're making what you already built actually intelligent. Python developers have transformers, LangChain, OpenAI SDK, Hugging Face all production-ready, all pip-installable, and all designed to sit right next to your existing FastAPI or Django project. No other ecosystem makes this this accessible. CRUD was the foundation. AI is the product. And if you're already writing Python you're already holding the tools. The only move left is using them. Which Python AI library are you integrating into your stack this year? 👇 #Python #FastAPI #Django #AIIntegration #SoftwareDevelopment #LangChain #MachineLearning #BackendDevelopment #TechIn2026 #BuildInPublic

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

Every Python dev on AWS runs into this at some point: Three Lambda functions, all sharing the same Pydantic models. You copy-paste once. Then twice. Then you spend a Tuesday figuring out why one function is missing a field. Google it, and every article from 2019 gives the same answer: "Just use Lambda Layers!" But Lambda Layers are not a package manager. Yan Cui said it well back in 2021: "Lambda Layer is a poor substitute for existing package managers." No IDE autocomplete, no proper versioning, and if someone deletes a layer version, your deploys break until you fix the ARN. There is a cleaner way: uv workspaces with local packages, bundled into self-contained ZIPs by CDK. Each function gets only what it needs. Normal Python packaging, no AWS-specific workarounds. Full blog post with the CDK construct (Demo GitHub Repository Link included): 👉 https://lnkd.in/d2A8AqAv PS: One thing I want to mention regarding my blog posts in general: I don't want to write another "Hello World with AWS Lambda" tutorial. There are enough of those. What I find interesting are the edge cases - things that are barely documented but matter a lot once you run real workloads in production. #AWS #Python #Serverless #CDK

When people think Python, they think simplicity. In 2026, they should also think production maturity, AI readiness, and backend flexibility. Python is still one of the most practical languages for building scalable, intelligent applications - and the ecosystem keeps evolving. Python 3.14 is now the current major series, Django has moved into the 6.0 line, Flask 3.1.x is current, and FastAPI remains a go-to option for high-performance API development. Why it scales: ✔️ Mature backend frameworks like Django and Flask ✔️ Strong fit for APIs, services, and modular architectures ✔️ Deep advantage in AI, ML, and data-heavy products ✔️ Modern API development options like FastAPI for performance-focused builds It’s a strong choice for: - SaaS platforms - AI-powered applications - Internal tools and data products - Backend services connected to modern frontend stacks 💡 2026 tip: Pair Python backends with React or Next.js on the frontend to combine fast product delivery with serious long-term flexibility. Python is not just beginner-friendly. It is one of the most durable languages in the modern stack. Is Python part of your stack? Why or why not? #Python #ScalableApps #AIEngineering #MachineLearning #WebDevelopment #TechStack

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)