Markus Eisele’s Post

How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution In this tutorial, we build and operate a fully local, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, set up authenticated model access through environment variables, and define a secure execution environment using the built-in exec tool. We then create a structured custom skill that the OpenClaw agent can discover and invoke deterministically. Instead of manually running Python scripts, we allow OpenClaw to orchestrate model reasoning, skill selection, and controlled tool execution through its agent runtime....

🚀 Python Concurrency Explained | Multithreading vs Multiprocessing Many times we hear “make it faster using threads or processes”… but what actually happens behind the scenes? Here’s a simple breakdown 👇 🧵 Multithreading (Same Process, Shared Memory) Multiple threads run inside a single process They share the same memory space Useful for I/O-bound tasks (API calls, file handling, DB queries) Faster context switching ⚠️ Limitation: Python uses GIL (Global Interpreter Lock), so only one thread executes Python bytecode at a time 👉 Result: Good for waiting tasks, not ideal for heavy CPU work ⚙️ Multiprocessing (Separate Processes, Separate Memory) Each process runs independently Own memory space (no sharing by default) Utilizes multiple CPU cores 👉 Best for: CPU-bound tasks (data processing, heavy computations, ML workloads) ⚠️ Trade-off: Higher memory usage and slower communication between processes 🧠 Behind the Scenes OS scheduler decides which thread/process runs Threads share memory → faster but risk of race conditions Processes isolate memory → safer but need IPC (Inter-Process Communication) True parallelism happens with multiprocessing 💡 Simple Rule I Follow: ✔️ I/O-bound → Multithreading ✔️ CPU-bound → Multiprocessing 📌 Still exploring deeper concepts like: Async programming (asyncio) Thread pools & process pools Deadlocks & synchronization Consistency matters more than speed in learning. #Python #BackendDevelopment #Multithreading #Multiprocessing #SystemDesign #Concurrency #SoftwareEngineering #Coding #Developers #TechLearning #100DaysOfCode

Built Pyre Agents: An Elixir-Orchestrated Runtime for Python Agents Pyre lets you build agents in Python without relying on Python to orchestrate them. Elixir/BEAM handles concurrency and fault tolerance, which makes large-scale, reliable agent systems much more practical. Read here: https://lnkd.in/e6cw_mT6

🐍 Python Term of the Day: Lovable (AI Coding Tools) An AI-powered full-stack platform that generates and deploys web applications from natural language descriptions. https://lnkd.in/gW--_n-T

🚀 Java vs Python in AI Development — Which One Wins? When it comes to building AI solutions, Python often steals the spotlight — but does that mean Java is out of the game? Not quite. Let’s break it down 👇 🐍 Python: The AI Favorite - Rich ecosystem: TensorFlow, PyTorch, scikit-learn - Simpler syntax → faster prototyping - Huge community support - Ideal for research, experimentation, and rapid development ☕ Java: The Enterprise Powerhouse - Strong performance & scalability - Better suited for large-scale production systems - Robust multithreading capabilities - Preferred in enterprise environments with existing Java infrastructure ⚖️ So, which should you choose? - 👉 Go with Python if you're focusing on AI model development, data science, or quick iteration. - 👉 Choose Java if you're deploying AI at scale within enterprise systems or need performance-critical applications. 💡 Reality check: Many modern AI systems use both — Python for building models, Java for integrating them into production environments. 🔍 Bottom line: It’s not about Java vs Python — it’s about using the right tool at the right stage. What’s your go-to language for AI projects? 👇 Let’s discuss! #AI #MachineLearning #Python #Java #Tech #SoftwareDevelopment #DataScience

Why learn it? Python is considered the "lingua franca" of the artificial intelligence industry and a fundamental requirement for most AI-related job roles. While other languages like C++ are used for high-performance backends, Python serves as the primary interface for developing, testing, and deploying models. Take this online course to hone your AI skills or upskill. #C++

Build your first AI agent with Python and Claude Learn to build an AI agent with Python and Claude that uses tools, makes decisions, and executes multi-step tasks autonomously. Read the full post 👇 https://lnkd.in/ghMddf5c #GenerativeAI #AI #WebDevelopment #PHP #Python #Developer #LLM

Why engineers are rewriting Python automation engines in Golang — and getting 10x throughput with the same logic. Here’s the technical breakdown 🧵 Python is great for automation. But when task volume scales — scraping, parsing, hitting APIs, pushing data — these cracks start to show: ❌ High memory usage under concurrent load ❌ The GIL blocking true parallelism ❌ Slow cold starts in containerized deployments ❌ Thread management becoming increasingly complex This is where Golang steps in. Same business logic. Same endpoints. Completely different performance profile. Here’s what changes: ✅ Goroutines vs ThreadPoolExecutor Spawning 10,000 goroutines costs almost nothing in Go. Python threads can’t compete at that scale. ✅ True concurrency, no GIL Golang utilizes all CPU cores natively. Python’s GIL prevents that by design. ✅ Memory footprint drops ~60% Go’s compiled binary vs Python’s interpreter overhead — the difference is significant at scale. ✅ Near-zero cold start time Critical when running bots on serverless or containerized infrastructure. ✅ Built-in race condition detection go build -race surfaces concurrency bugs that would silently corrupt data in production. Python is not the wrong choice. It remains the best tool for ML, Computer Vision pipelines, and rapid prototyping. But for high-throughput automation engines that need to scale — Golang is the stronger choice. The rule is simple: pick your language based on the problem, not habit. What’s your take — have you made a similar switch for performance reasons? 👇 #Golang #Python #Automation #SoftwareEngineering #RPA #BackendDevelopment #Performance #ComputerVision #MachineLearning

🚀 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