Enhanced Log Analyzer with CLI & Top N Error Detection

I built the fastest Python logging framework. 446K ops/sec. 2.7x faster than stdlib. 20% faster than Microsoft's picologging, which is written in C. It's a one-line migration:  import logging → from logxide import logging Same getLogger(). Same format strings. Flask, Django, FastAPI all work. Sentry and OTLP are built in. Zero config. Wrote up the production guide with copy-paste examples. ⬇️ See comment #Python #Rust #OpenSource

one important part of controlling kubernetes clusters is a monitoring tool. However, taking a look at that tool sometimes and very often we cannot understand what is going on and what further steps to solve it are. Therefore, I have created a diagnostic web tool in order to make suggestions and recommendations with issues on clusters. It has a report system as well. Here is the GitHub repo where you can either run as a python script or use docker compose file. Please try to diagnose your cluster. Thanks https://lnkd.in/efEsRDXW

Excited to share that my personal website now has a blog! 😀 It's a space where I'll be sharing findings, learnings, and recommendations from my day-to-day work as an engineer. For the first post, I wrote about something that came up naturally while load testing a high-throughput event pipeline with Locust: - Python parallelism, the GIL, and how Locust actually works under the hood. It covers: - Why Python threads don't give you true parallelism (the GIL) - How Locust uses gevent greenlets for I/O concurrency - Why worker pods are the key to scaling RPS on Kubernetes - When C extension libraries like NumPy and PyTorch can bypass the GIL A short read, but one I wish I had found when I first started digging into this. https://lnkd.in/e66t7Fhk Would love to hear your thoughts. 😊 #Python #SoftwareEngineering #LoadTesting #Kubernetes #BackendEngineering

I still can't believe that there isn't a bulk archive space feature in Confluence. 💡 Solution Python script that takes your spaces report (Confluence Administration -> Data management -> Space reports). From this report you will see Last Viewed column. This script is taking a look if that column is empty or not. If it's empty it will mark the space for archiving. You can get the script here: https://lnkd.in/dKNDrRfT Set the dry run to False to actually do the archiving. #AtlassianChampion #Confluence #Atlassian #Python

You subclass a data processor, override one method, tests pass… then you swap it in and something upstream breaks ⚠️ That’s usually not “bad luck,” it’s a Liskov Substitution Principle violation 🧠 The subclass technically works, but it quietly changed the contract: stricter preconditions, different return guarantees, or new exceptions the parent never had 💥 If callers need to check which subclass they’re dealing with, the abstraction already failed. I broke this down with concrete Python examples 🐍 and when inheritance is just the wrong tool entirely 👉 https://lnkd.in/ewG9ugCc What’s the sneakiest contract violation you’ve shipped without noticing? 💬 #Python #SoftwareEngineering #SOLID #CleanCode #DataScience

𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀, 𝘂𝗻𝗳𝗶𝗹𝘁𝗲𝗿𝗲𝗱 I added logging to my Python app and thought I was done. Two lessons later — I wasn't even started. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴 𝗳𝗶𝗹𝗲 𝗶𝘀 𝗮 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝗯𝗼𝗺𝗯 No size limit. No rotation. No cleanup. It just grows. Forever. Dev - fine. Production - it silently eats your disk until your app is down at 2AM. One swap in Python's standard library fixes this. Max size. Backup count. Auto-rotates. Auto-deletes oldest. That's it. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝘀 𝗮𝗿𝗲 𝘂𝗻𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 🔍 Human-readable text is perfect for dev. In production with thousands of requests ⁉️ Useless. Switch to JSON logs. Every line becomes a structured event : timestamp, level, request_id, user_id. Want all errors from one specific request? One query. Done. No regex. No grepping through walls of text. The difference between logging and logging for production is bigger than I thought. That gap - that's what this series is about #Python #Backend #LearnInPublic #SoftwareEngineering

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

VelocityFL The uv of federated learning. VelocityFL pairs a Rust core for aggregation, attack sim, and round orchestration with a Python API for HuggingFace, PEFT, and PyTorch workflows. → PyO3 on the hot path (~92× faster aggregation vs pure Python at 1M params) → Prefect for round-level observability → Typer CLI for everything else https://lnkd.in/ekGGBR35

There's been an explosion of open-source Python type checkers and language servers recently. Now you're not locked into Pylance! I wrote about the state of the LSP world today, and why you should still care about them even if AI writes all your code: https://lnkd.in/gusUZybX

Experimented with OpenClaw using local Ollama but performance was horrible (each response taking up 1.5+ minutes), so ended up writing a Python script for the task I had in mind for it instead. Uses Google OAuth to get access to Gmail, goes through recent messages and marks anything that doesn't look important as read. https://lnkd.in/gAHcxcdv #MinorInconveniencePatches : #3