Django vs FastAPI: 2025 Survey Data & Expert Insights

Build Your First AI Agent Without LangChain: the Minimal Tool-Calling Loop in Python There's a moment in agent development where everything snaps into focus—and it almost never happens inside a framework. Most beginners reach for LangChain before they've seen what a tool call actually looks like in raw Python. So they absorb abstractions without building intuition. When something breaks, they're lost. The core mechanic isn't mysterious. You send messages to the model. You describe the tools it can call. It returns a structured request—"run this function with these arguments." You parse that, execute the function, pass the result back. Repeat. That loop *is* the agent. But here's where tutorials skip the honest part: the messiness. The model doesn't always return clean JSON. Arguments need validation. Errors need to feed back gracefully. What looks tidy in a demo gets rough edges fast in practice. Getting a handle on this before reaching for any framework means you know what the framework is actually doing for you—and when it's quietly doing the wrong thing. I've watched teams debug production issues for hours because nobody on the team had ever seen the raw request/response cycle. That's an expensive gap. Write the loop yourself once. Even if you never use it in production, it changes how you read everything else.

🚀 Python GIL vs No-GIL — Real FastAPI Benchmarks (Python 3.13) Free-threaded Python is no longer just an experiment — it’s starting to show real impact. I came across a benchmark comparing Python 3.12 (with GIL) vs Python 3.13t (No-GIL) using FastAPI, and the results are pretty interesting 👇 💡 Key Takeaways: 🔹 Massive CPU Boost (~8x) CPU-bound endpoints jumped from ~4 RPS to ~32 RPS — with ZERO code changes. This is what true parallelism across cores looks like. 🔹 Threading inside requests ≠ better performance Even without GIL, spawning threads inside a single request didn’t help. Why? Under load, request-level parallelism already saturates the CPU. Extra threads just add overhead. 🔹 I/O performance unchanged No surprise here — GIL was never the bottleneck for I/O-bound workloads. Async + I/O still behaves the same. 📊 What this means in practice: ✅ Use No-GIL Python when: - You have CPU-heavy APIs (ML inference, image processing, data pipelines) - High concurrency + CPU contention exists - You previously relied on multiprocessing to bypass GIL ❌ Don’t expect gains if: - Your app is mostly I/O (DB calls, HTTP requests) - You’re already using async effectively ⚠️ Things to keep in mind: - Free-threading is still evolving - Thread safety is now YOUR responsibility - Some C extensions may not be ready yet 🔥 The most exciting part? Same code. Same FastAPI app. Just a different Python runtime → 8x improvement. This could seriously change how we design backend systems in Python. Curious — would you switch to No-GIL Python for your APIs? #Python #FastAPI #BackendEngineering #Performance #Concurrency #AI #SoftwareEngineering

In a world where the machine writes most of the code, Python lacks solid type enforcement, Rust is overly strict with complex lifetimes, while Go strikes the right balance by catching critical issues without hindering development velocity. The article argues in favor of Go over Python and Rust for AI-generated code due to Go's efficiency, simplicity, clear error-handling, and easy deployment capabilities. https://lnkd.in/eEgmd4qW --- Like what you see? Subscribe 👉 https://faun.dev/join

Stop Googling the same Python functions over and over. 🔴 Bookmark this instead. 👇 Putting together a cheat sheet of every built-in Python function beginners actually need - organized by category so you can find what you want in seconds: 📊 Numbers → abs(), round(), min(), max(), sum(), pow() Do math without reinventing the wheel. 🔤 Strings → len(), upper(), lower(), split(), join(), replace() Text wrangling made painless. 📋 Lists → append(), extend(), insert(), pop(), remove(), sort() You'll use these every single day. 🔗 Tuples & Sets → count(), index(), add(), update(), remove(), clear() Immutable data + unique elements = fewer bugs. 🔄 Control Flow → print(), input(), type(), range(), enumerate() The backbone of every loop and script you'll write. 🎲 Random & Type Conversion → random(), randint(), choice(), int(), float(), str() Simulations, transformations, and quick conversions. ⚙️ Functions → def, lambda, return, map() Write it once. Use it everywhere. ⚠️ Error Handling → try, except, raise, assert, finally Because "it works on my machine" isn't a strategy. Here's the thing most tutorials won't tell you: Memorizing syntax doesn't make you a developer. Building things does. Pick one category above. Open a blank .py file. Break something. Fix it. That's the loop. 🚀 These fundamentals are the difference between someone who "knows Python" and someone who builds with Python. Drop your most-used Python function in the comments. 👇 #Python #Programming #DataScience #SoftwareDevelopment #Coding

OrJSON looks like a small optimization. Until you realize how much time your API spends just serializing JSON. In many Python APIs, the bottleneck isn’t only the database or the LLM. Sometimes it’s the most invisible step: turning Python objects into JSON. What is OrJSON? A high-performance JSON library for Python, written in Rust. It replaces the default json module and focuses on one thing: speed. It: → serializes faster → deserializes faster → supports dataclass, datetime, numpy, UUID out of the box → returns bytes instead of str So what’s happening under the hood? The idea is simple: optimize the hottest path in your API. → less overhead per operation → less work per payload → faster UTF-8 writing And it shows. In its own benchmarks: → dumps() can be ~10x faster than json → loads() can be ~2x faster Where this actually matters: → large payloads → APIs returning a lot of JSON → RAG metadata, events, telemetry → long lists Now the part most people ignore: Trade-offs. → orjson.dumps() returns bytes, not str → no built-in file read/write helpers → not always a perfect drop-in replacement → holds the GIL during serialization So when should you use it? → large responses → heavy metadata → serialization shows up in profiling And when won’t it help? → DB is your bottleneck → LLM latency dominates → responses are small → network / I/O dominates OrJSON won’t magically make your API fast. But if serialization is on your hot path, it’s one of the highest ROI optimizations you can make.

🐍 Python Roadmap Want to master Python? Here’s a simple roadmap: 🔹 Basics – Syntax, variables, conditionals, functions, data structures 🔹 Advanced – List comprehensions, generators, decorators, regex 🔹 DSA – Arrays, stacks, queues, trees, recursion, sorting 🔹 OOP – Classes, inheritance, methods 🔹 Data Science – NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch 🔹 Web Dev – Django, Flask, FastAPI 🔹 Automation – Scripting, web scraping, GUI automation 🔹 Testing – Unit testing, integration testing, TDD 🚀 Learn → Practice → Build projects = Python mastery. #Python #Programming #Coding #Developer #DataScience #100DaysOfCode

🐍 Python Roadmap Want to master Python? Here’s a simple roadmap: 🔹 Basics – Syntax, variables, conditionals, functions, data structures 🔹 Advanced – List comprehensions, generators, decorators, regex 🔹 DSA – Arrays, stacks, queues, trees, recursion, sorting 🔹 OOP – Classes, inheritance, methods 🔹 Data Science – NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch 🔹 Web Dev – Django, Flask, FastAPI 🔹 Automation – Scripting, web scraping, GUI automation 🔹 Testing – Unit testing, integration testing, TDD 🚀 Learn → Practice → Build projects = Python mastery. #Python #Programming #Coding #Developer #DataScience #100DaysOfCode

Just how fast is Pyrefly? How does it compare to other type checkers? 📝 In our latest type checker comparison blog we cover the speed and memory benchmarks we run regularly across 53 popular open source Python packages, comparing Pyrefly, Ty, Pyright, and Mypy. The results: Rust-based checkers are roughly an order of magnitude faster, with Pyrefly checking pandas in 1.9 seconds vs. Pyright's 144. https://lnkd.in/eYhNkbfm shoutout to Aaron Pollack for authoring this piece 👏

Python codebases that break under pressure all share one thing in common. The developer skipped the fundamentals. Not the syntax. Not the frameworks. Not the libraries. The fundamentals. Arrays. Sets. Hash Maps. Trees. Queues. The building blocks that every great Python developer has locked in. Here's the pattern I keep seeing 👇 --- Developers who skipped DSA fundamentals: ❌ Use a list when a set would be 100x faster ❌ Write nested loops when one pass is enough ❌ Reach for a new library when the right structure solves it ❌ Hit performance walls they can't explain — let alone fix ❌ Spend days debugging what should take minutes to trace Developers who know their DSA fundamentals: ✅ Look at a problem and immediately know the right tool ✅ Write code that scales from 100 to 10,000,000 records ✅ Debug faster because they understand what's happening underneath ✅ Ship cleaner, leaner solutions — less code, more impact ✅ Never fear a technical interview because they think in structures --- The irony? Everyone wants to learn the latest Python framework. FastAPI. LangChain. PyTorch. But the developers who master those tools fastest — are the ones who understood the fundamentals first. Because frameworks change every year. Fundamentals don't. A list in Python is still a dynamic array. A dict is still a hash map. A set still gives you O(1) lookup. These truths were built into the language in 1991. They'll still be true in 2035. --- If you're learning Python right now: Don't rush to the shiny stuff. Spend one week deeply understanding Arrays and Lists. Spend one week on Hash Maps and Sets. Spend one week on Trees and Graphs. That one month will compound into years of better code. Fundamentals aren't the starting point. They're the competitive advantage. --- 💬 What's the one DSA concept that changed how you write Python? I read every comment — drop it below. 👇 ♻️ Repost this for every developer in your network still chasing frameworks. They need to see this first. 👉 Follow for practical Python + DSA content — built for developers who want to go deep. #Python #DSA #DataStructures #PythonProgramming #SoftwareEngineering #CodingTips #LearnToCode #TechCareer #BuildInPublic #100DaysOfCode

I ran `kill -9` on a Python worker processing three tasks. They vanished — no error, no retry, no record. This is the default behavior of most task frameworks: a worker dies mid-execution, and the work disappears. So I built automatic crash recovery into pynenc, an open-source distributed task orchestration framework for Python. Here's what it does: • Every runner emits periodic heartbeats • When heartbeats stop, the recovery service detects the dead runner • Orphaned tasks are automatically re-queued • A healthy runner picks them up and finishes the job No external monitoring. No manual re-queueing scripts. No lost work. I wrote up the full scenario — including a runnable demo you can try locally with zero dependencies (no Docker, no Redis): https://lnkd.in/ehWVK-3p The demo takes about 90 seconds and shows recovery happening end-to-end. How does your team handle crashed workers today? #python #distributedsystems #opensource #backend #reliability