I often wondered it was possible to auto-optimize agents. Say you didn't want to touch the prompt, Python scripts, tools, and agent configuration. You give an agent some task and a benchmark then let it iterate and improve on it autonomously. All you need to edit manually is program.md (instructions for the meta agent + directive) and the eval tasks. This is what AutoAgent does. It is the first open-source library for self-optimizing agents: https://lnkd.in/gJxUrMXm

🚀 Understanding the Proxy Pattern (Python) The Proxy pattern provides a surrogate or placeholder for another object to control access to it. A proxy acts as an intermediary, controlling access to the real object. This pattern is useful when you want to add security, lazy initialization, or remote access to an object. The proxy can perform additional operations before or after forwarding the request to the real object. This promotes loose coupling and provides a flexible way to control object access. #Python #PythonDev #DataScience #WebDev #professional #career #development

📂 Moving beyond the basics: Automating file system analysis with Python. I recently tackled a challenge to build a directory analyzer that goes deeper than a simple ls command. Using the os module, I developed a script that provides a comprehensive audit of any given path. What it does: 1) Recursively traverses directories using os.walk(). 2) Aggregates total file counts and folder structures. 3) Calculates total storage footprint with formatted sizing. 4) Identifies the "heavy hitters" (largest files). 5) Uses Python dictionaries to map and group files by extension. This project was a great exercise in handling file metadata and organizing unstructured data into a clean, readable summary. Check out the screen recording below to see it in action! 👇 #Python #Coding #Automation #SoftwareDevelopment #Programming

Some Python ropes have a plastic encapsulated core — PlastGuard®. The solution presents several advantages over non-encapsulated #wirerope — all of which result in extended service life and reduced downtime ⤵️ https://lnkd.in/eyHu6icY

⚠️ Medium Risk Vulnerability Alert! The Claude SDK for Python has a memory tool path validation race condition that allows sandbox escape. This issue is related to API security and can lead to unrestricted resource consumption. It's been patched in version 0.87.0. Stay safe and keep your systems updated! #ClaudeSDK #Python #APIsecurity #OWASP #CVE2026-34452 https://lnkd.in/g26q6ZAn

C++26 Reflection & Python Bindings Writing bindings manually is tedious: * Extra code you need to read, maintain(,and write). * Extra dependencies in the project. * Extra bugs. I’ve been exploring C++26 reflection and built a small prototype: automatic Python bindings without writing bindings. Here’s how it works 👇 Post: https://lnkd.in/gwJYhnnF Code: https://lnkd.in/gbQqPVNr #cpp #reflection #c++26

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns

🚀 Python 3.13+ is a game-changer: Free-threading (no-GIL mode) and experimental JIT boost multithreaded code by 2-5x! Speed gains are real for CPU-heavy tasks. Tested a simple parallel sum script—3x faster than 3.12. Python 3.15 stabilizes JIT fully. Here’s the snippet: # Run with: python3.13 -X free-threading import threading def compute(n): return sum(i*i for i in range(n)) threads = [threading.Thread(target=compute, args=(10**7,)) for _ in range(4)] for t in threads: t.start() for t in threads: t.join() print("Done!") Who’s upgraded? Share your benchmarks below! 👇 #Python #Python313 #Programming

UUID Generator: Creating Unique Tracking IDs for Orders What you will build today: A production-grade, thread-safe order ID system that prevents double-fills, survives API failures, and gives you forensic traceability during live incidents — all in under 300 lines of Python. https://lnkd.in/dpP8P4ei

Dashboard with DASH. Using a series of synthetic data, I created this dashboard using the DASH library in Python, but I must admit that it's easier to work with the Shainy library in R. Which do you prefer? The advantages of using these two libraries I just mentioned are that they are completely free and that you can also share and interact with the image in a professional meeting without having to pay a single cent. This is much more expensive with other solutions. #DataVisualization #PythonProgramming #BusinessIntelligence