Python's Free-Threading Era: Revolutionizing High-Performance Apps

Machine Learning Data Visualization using tSNE #machinelearning #datascience #datavisualization #opentsne Open tSNE is a modular Python library for t-SNE dimensionality reduction and embedding | Extensible, parallel implementations of t-SNE | openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations https://lnkd.in/g-G-nmhn

🚀 Python vs Other Programming Languages A Deep Technical Perspective In the evolving software ecosystem, choosing the right programming language is less about popularity and more about architecture, runtime behavior, and system constraints. 🔍 Why Python Stands Out: • High-Level Abstraction Python minimizes boilerplate using dynamic typing and automatic memory management, accelerating development cycles. • Interpreted Execution Model Unlike compiled languages (e.g., C/C++), Python executes via an interpreter, enabling rapid prototyping but introducing runtime overhead. • Dynamic Typing with Optional Static Hints Python supports runtime polymorphism while also allowing type hints (PEP 484) for better tooling and maintainability. • Garbage Collection (GC) Automatic memory management using reference counting + cyclic GC reduces developer burden compared to manual allocation in low-level languages. • Massive Ecosystem Libraries like NumPy, TensorFlow, and Pandas make Python dominant in AI/ML, Data Science, and Automation. ⚙️ Where Other Languages Excel: • Performance-Critical Systems Languages like C/C++ provide low-level memory control and near-hardware execution speed. • Static Typing & Compile-Time Safety Java, Rust, and Go enforce strict type systems, reducing runtime errors in large-scale systems. • Concurrency & Parallelism Languages like Go (goroutines) and Rust (ownership model) outperform Python’s GIL limitations. 💡 Key Insight: Python is not a replacement for all languages it is a productivity multiplier. For high-performance systems, it often works alongside lower-level languages rather than replacing them. 📊 Conclusion: > Python dominates where development speed, flexibility, and ecosystem matter. Other languages dominate where performance, control, and scalability guarantees are critical. #Python #Programming #SoftwareEngineering #AI #MachineLearning #DataScience #Coding #TechInsights

Why is Python the second-best language for everything? Because it excels at specific roles. Not as a replacement for native code, but as a flexible layer between application logic and core processing. Embedding a Python interpreter into your vision application creates version dependencies, limits ecosystem access, and complicates debugging. The alternative is to attach your application to the Python interpreter already installed on the system. This architectural shift solves multiple problems: Users choose their Python version, libraries install normally, IDEs work as expected, and core algorithms stay protected in native code, whilst scripting handles the flexible parts that need field adjustments. Andreas Rittinger explains this approach in the latest inVISION News, with Common Vision Blox's PyScript engine as the working example. The article includes a 1-minute video demonstration. Read the article here: https://lnkd.in/dJ88udJv #MachineVision #CVB #EmbeddedVision #IndustrialAutomation #MachineLearning

Tutorial Python and production Python look nothing alike. And that's why most junior engineers struggle to ship real systems. Here's the difference: ❌ Tutorial code: "Make it work" ✅ Production code: "Make it readable, maintainable, reliable" I used to write clever one-liners. Then I opened a file I wrote 6 months ago and thought: "What is this?" That's when I learned the Three Pillars of Production Python: 📖 Readability → Can someone understand this without asking? 🔧 Maintainability → Can someone safely change this? 🛡️ Reliability → Does this handle edge cases gracefully? Example: Instead of this: # ❌ Tutorial style def process(d):     r = []     for x in d:         if x[2]: r.append(x[0] * x[1])     return r # ✅ Production style def calculate_line_totals(items: list[dict]) -> list[float]:     """Return total price for each active line item."""     return [         item["price"] * item["quantity"]         for item in items         if item.get("is_active")     ] Same logic. Different outcome: one survives a team handoff. The other doesn't. Why this matters for AI engineering: 1. Model endpoints need type hints for clear contracts 2. Data pipelines need maintainable transformations 3. Every AI product is Python + infrastructure + impact Master production patterns. Ship systems that last. 👇 What's a "boring" pattern you rely on in production? #Python #BackendEngineering #CleanCode #AIEngineering #LearnInPublic #CyberSecurity

🚀 Day 15 – Learning Python API Integration & REST APIs Today I explored how Python interacts with REST APIs and how web services communicate over the internet. 🔹 What I learned: • Understanding REST architecture and how client-server communication works • Common HTTP methods: GET, POST, PUT, PATCH, DELETE • API endpoints and how they represent resources • Importance of status codes (200, 201, 404, 500, etc.) • How to consume APIs using Python (requests library) • Basics of building REST APIs and designing endpoints 🔹 Key takeaway: APIs are the backbone of modern applications — they allow systems to retrieve, update, and exchange data seamlessly. Working with APIs helped me understand how real-world applications connect and communicate with external services. 📚 Reference: https://lnkd.in/e9UX244q Continuing to build strong foundations in Python, Data Engineering, and AI step by step. 💻 #Python #DataEngineering #API #RESTAPI #LearningJourney #SelfLearning #AI #NewCareer

Let's demystify Python for backend in 60 seconds—with code that matters. ❌ Myth: "Python is too slow for production." ✅ Truth: Python is the glue that holds scalable systems together. Here's the pattern I use for every backend feature: # 1. The Universal Data Pattern: List of Dictionaries users = [   {"id": 1, "name": "Alice", "email": "a@x.com", "active": True},   {"id": 2, "name": "Bob", "email": "b@x.com", "active": False}, ] # 2. Filter + Transform with for + if (backend's heartbeat) active_users = [   user["email"]    for user in users    if user["active"] ] Why this pattern scales: ✅ Readable by humans (onboarding, debugging, audits) ✅ Testable in isolation (unit tests, CI/CD) ✅ Extendable without breaking (open/closed principle) ✅ Graceful under failure (error handling, logging) Why this matters for AI engineering: Model endpoints = functions with clear contracts Feature pipelines = list-of-dicts transformations Evaluation systems = filter + aggregate patterns MLOps = Python + infrastructure + observability Master the pattern. Scale the impact. 🔧 What's your go-to pattern for processing backend data? List comprehensions? Pandas? Something custom? 👇 #Python #BackendDevelopment #SoftwareEngineering #AIInfrastructure #CleanCode

A while ago I was asked what the coolest thing I had built in Python was and I realised I did not have a good answer. So I started exploring something I have been curious about: how efficiently Python can process very large datasets or files. I decided to build a streaming pipeline that reads logs line by line instead of loading everything into memory, then see how far performance could be pushed. The current version processes a synthetic dataset of roughly 33 million log lines (~2GB) in about 4.3 seconds on a single machine. That works out to roughly 7.6 million lines per second while keeping memory usage constant regardless of file size. Along the way I ended up digging into lower level Python concepts like: - streaming vs loading entire files into memory (killing my VSCode in the process 😅 ) - identifying CPU vs I/O bottlenecks - experimenting with Cython to speed up parsing One key takeaway for me was learning where offloading processing to Cython would yield meaningful performance improvements and where any Python overhead was marginal.  For example, my read_lines function uses f.read() which is a syscall waiting on disk. No amount of compiled C code speeds up waiting, so keeping it as a plain Python generator is the right call. The Python overhead of the iterator protocol, yield, and UTF-8 decode is negligible next to I/O latency. Even the .split() and .decode() calls are already C under the hood via Python's built-in implementations.                                                                      My aggregate_lines processing function is the opposite story. It involves integer arithmetic, string splitting, dict updates repeated millions of times per file. This is exactly where Python's dynamic dispatch and object boxing hurt: every += 1 on an untyped variable is a Python object operation. Dropping in cdef int / cdef long eliminates that overhead entirely, letting the loop run at C speed. I learned that if something is slow because it is "waiting", Cython won't help, but if slowness is a result of repetitive CPU work with Python objects moving the logic into Cython could result in meaningful performance improvements! Project Repo: https://lnkd.in/epvf_Gcf

Multiprocessing in Python by Harvard Register for the Quantitative Finance Cohort to learn in-depth Quantitative Finance, enquire now:- https://lnkd.in/g9f3cm8N Python’s standard interpreter, CPython, has a well known limitation called the Global Interpreter Lock (GIL). The GIL ensures that only one thread executes Python bytecode at a time within a single process. While this simplifies memory management, it limits the effectiveness of multithreading for CPU bound workloads. This is where multiprocessing becomes important. The Python multiprocessing module allows programs to create multiple independent processes, each with its own Python interpreter and memory space. Because each process has its own GIL, true parallel execution across multiple CPU cores becomes possible. In practice, multiprocessing is useful when dealing with CPU intensive tasks such as numerical simulations, Monte Carlo methods, data processing pipelines, or large scale backtesting frameworks. By distributing work across multiple cores, overall execution time can be significantly reduced. A common abstraction in multiprocessing is the process pool. Using objects such as Pool, a developer can distribute a function across many input values and let the operating system schedule execution across available cores. This makes parallelization relatively straightforward without manually managing each process. However, multiprocessing introduces trade-offs. Since processes do not share memory by default, communication must happen through mechanisms such as queues, pipes, or shared memory objects. This can introduce overhead, particularly when transferring large datasets between processes. Another practical consideration is process start-up cost. Creating processes is heavier than creating threads, so multiprocessing tends to work best for large tasks with meaningful computation time, rather than very small tasks. Register for the Quantitative Finance Cohort to learn in-depth Quantitative Finance, enquire now:- https://lnkd.in/g9f3cm8N

5 Useful Python Scripts for Synthetic Data Generation Image by Editor   # Introduction  Synthetic data, as the name suggests, is created artificially rather than being collected from real-world sources. It looks like real data but avoids privacy issues and high data collection costs. This allows you to easily test software and models while running experiments to simulate performance after release. While libraries like Faker, SDV, and…...

Multipart (Python library), ReDoS, CVE-2026-28356 (High) The `parse_options_header()` function in `multipart.py` versions up to `1.3.0` contains a regular expression with an ambiguous alternation . When parsing a maliciously crafted `Content-Type` header (specifically the options section), the regex engine attempts to validate the string against multiple paths . Due to the nested repetition and alternation in the pattern, providing a carefully crafted input with many backslashes and quotes causes exponential backtracking ....