When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python

🚀 Turn any Python CLI script into a modern GUI – with zero extra dependencies. I just open‑sourced PyScript-to-GUI, a tool that instantly wraps your command‑line scripts into a clean, functional graphical interface. ⚡ No more boring terminals. Your users get a real window with dark mode, real‑time output, and interactive input dialogs – without writing a single line of GUI code. ✨ Key features: ✅ Zero external dependencies – uses only tkinter (built into Python) ✅ Smart input() handling – automatically converts prompts into pop‑up dialogs ✅ Live logging – all print() output appears in a scrollable terminal‑style area ✅ Multi‑threaded – the GUI never freezes, even during heavy tasks ✅ Hacker aesthetic – dark grey + lime green theme, ready to impress 🔧 Perfect for: Sharing your scripts with non‑technical colleagues Building quick internal tools with a professional look Teaching Python without scaring beginners with the terminal 🔗 GitHub repo: https://lnkd.in/dDpXCYSk 👨💻 Built by NULL200OK – because every script deserves a beautiful face. #Python #GUI #Tkinter #OpenSource #DeveloperTools #CLItoGUI #PyScriptToGUI #Coding

Your build server is running out of space. You run df -h. 94% full. Great. So you du -sh your way through directories like it's 2005, mentally adding up numbers, until you finally find the 6 .venv folders nobody cleaned up. There's a better way — and building it yourself is half the point. In my latest article, I walk through building pydusk: a terminal disk usage analyzer in Python, inspired by ncdu. Keyboard-driven, non-blocking, with a delete confirmation flow and a clean TUI — all in a single file with two dependencies. Stack: Textual · Typer · os.scandir Things worth stealing from this project even if you never run it: → Why os.scandir beats os.walk for disk traversal → Textual's @work(thread=True) pattern for background tasks → ModalScreen[T] + dismiss() for confirmation dialogs https://lnkd.in/dmKvDSgH #Python #Textual #CLI #OpenSource #Developer

I was debugging a Django service last week and hit a classic problem memory growing silently across requests, no obvious culprit. The usual suspects (tracemalloc, memory_profiler, objgraph) are great tools. But I wanted something I could drop on any function in 30 seconds and get a readable answer from. Also, honestly I wanted to understand what's happening at the GC and tracemalloc abstraction layer in Python. The best way I know to understand something is to build on top of it. So I built MemGuard over a weekend. What it does: Drop @memguard() on any function and after every call you get: Net memory retained (the actual leak signal) Peak vs net ratio — catches memory churn even when net looks clean Per-type gc object count delta tells you what is accumulating, not just how much Cross-call trend detection if net grows every call, it flags it Allocation hotspots via tracemalloc exact file and line Zero dependencies. Pure stdlib gc, tracemalloc, threading. @memguard() def process_batch(records): That's it. It also works as a context manager if you want to profile a block rather than a function. Biggest thing I learned building this: Python's gc and tracemalloc expose far more than most people use day to day. The object-reference graph alone tells a story that byte counts miss entirely. Repo: https://lnkd.in/gdjkHvfb Would love feedback from anyone who's dealt with Python memory issues in production. #Python #Django #SoftwareEngineering #OpenSource #BackendDevelopment #MemoryManagement

UV is one of the best Python tools I've used recently. But it taught me something the hard way. A few weeks ago, I created a virtual environment with uv venv, assumed it was active, and started installing packages. I thought everything was fine. Only later did I realize: packages had been installed globally, not in the project environment. Here's what I was missing: UV doesn't auto-activate virtual environments the way Poetry or Conda might. When you run uv run python script.py, UV uses the project environment internally—it looks active, but your shell session isn't actually modified. There's no prompt change. No visual indicator. This is intentional design, not a bug. UV is explicit, not implicit. It separates environment creation from environment activation. But that separation can catch you off-guard. The correct workflows: -Option 1 (recommended): Use uv run uv run python script.py No need to manually activate anything. UV handles it internally. -Option 2: Manually activate the venv source .venv/bin/activate Then install or run as normal. This gives you shell-level control. -Option 3: Use uv pip install uv pip install package_name This ensures installation happens in the uv-managed environment, not globally. The lesson: understand the tool's philosophy. UV prioritizes explicitness over convenience. That's actually a strength—it makes behavior predictable once you understand the pattern. But it requires intentional workflows. Big credit to Astral for building a fast, thoughtful package manager. This wasn't a flaw—it was me not reading the documentation carefully enough. If you use UV, make this part of your muscle memory. Small habit, saves debugging time. #Python #UV #PackageManagement #DeveloperExperience #SoftwareEngineering #Astral #DevTools #BestPractices #Python3 #VirtualEnvironments

🚀 Built a simple Python script to clean up my messy Downloads folder! We all download files daily, and things get cluttered fast. So I wrote a quick automation script using Python to organize files into folders like Images, Documents, Archives, etc. 💡 Here’s the code: ```python from pathlib import Path import shutil # Folder to organize source = Path("C:/Users/YourName/Downloads") # File type mapping folders = { ".jpg": "Images", ".png": "Images", ".pdf": "Documents", ".zip": "Archives", ".exe": "Installers" } for file in source.iterdir(): if file.is_file(): folder_name = folders.get(file.suffix.lower()) if folder_name: destination = source / folder_name destination.mkdir(exist_ok=True) shutil.move(str(file), destination / file.name) ``` ⚡ What it does: * Scans your Downloads folder * Detects file types * Creates folders automatically * Moves files to the right place Sometimes, small automations like this can save a lot of time and keep your system organized. #Python #Automation #Coding #Developers #Productivity #Backend

The "Shadow" Fix: Python Version Compatibility **Hook:** Building for the "Latest & Greatest" is easy. Building for the "Real World" is where the engineering gets messy. **Body:** While finalizing my Enterprise RAG pipeline, I hit a silent production-breaker: A `TypeError` buried deep in a third-party dependency. The culprit? The `llama-parse` library uses Python 3.10+ type union syntax (`|`), but the production environment was locked to Python 3.9. Result: Immediate crash on boot. Instead of demanding a system-wide upgrade—which isn’t always possible in locked-down enterprise environments—I implemented a **Graceful Fallback Logic**: ✅ **Dynamic Imports**: Wrapped the cloud-parser initialization in a guarded `try-except` block. ✅ **Smart Routing**: If the Python environment is incompatible, the system automatically redirects to a local, high-fidelity `PyMuPDF` parser. ✅ **System Resilience**: The app stays online, the UI remains responsive, and 99% of RAG functionality remains available without a single user noticing a failure. Real Engineering isn't just about using the best tools—it’s about writing code that doesn't break when the environment isn't perfect. #Python #SoftwareEngineering #RAG #AIEngineering #SystemDesign #Resilience

I used to think my Python UI code was "fine"—until I tried to write a unit test for it. 🤯 If you've ever felt trapped between Choosing #Streamlit or #Gradio, or if you've struggled with code that’s impossible to test without launching a browser, this article is for you. I've been working on #AgnosticPyUI to prove that we can apply the same clean architecture patterns we use in our backends to our frontend Python code. Decoupling your UI library from your logic isn't just about flexibility; it's about peace of mind. Read more about it in my latest Medium post: https://lnkd.in/eZpjQxqA I'd love to hear your thoughts on this! Which one do you prefer using? #PythonDevelopers #DataScience #Testing #SoftwareEngineering #OpenSource

One thing that significantly improved my Python code quality: Static analysis is not optional at scale. For a long time, I relied on code reviews to catch issues. Eventually, I realized something: 👉 Humans are bad at consistently spotting patterns. 👉 Tools are not. That’s where static analysis changed everything. Without running the code, these tools analyze your source and detect: bugs code smells complexity issues type inconsistencies All before production The combination that worked best for me: Ruff → fast linting and code quality Replaces multiple tools (flake8, isort, etc.) and runs extremely fast Mypy → type checking Uses type hints to catch bugs before runtime, bringing discipline to Python’s dynamic nature Radon → complexity analysis Measures cyclomatic complexity and highlights functions that are hard to maintain. #Python #StaticAnalysis #BackendEngineering #Django #CleanCode #SoftwareEngineering #DevOps

A 600-run benchmark by Yusuke Endoh tested #ClaudeCode across 13 #ProgrammingLanguages by implementing a simplified Git. Key takeaways: ⇨ Ruby, Python & JavaScript were the fastest, cheapest & most stable - $0.36–$0.39 per run ⇨ Statically typed languages were 1.4–2.6x slower and more expensive ⇨ Adding type checkers to dynamic languages caused 1.6–3.2x slowdowns More details on #InfoQ ⇨ https://bit.ly/4trOIph #SoftwareDevelopment #AI #Coding