Python Automation: Extracting Google Distances with Selenium

Manually scanning logs during incidents can be slow and inefficient — especially when trying to quickly identify errors. To understand how SREs handle this, I built a Log Analyzer in Python. 🔍 What it does: • Parses log files and classifies logs (INFO, WARNING, ERROR) • Counts occurrences of each log level • Identifies the most frequent error messages • Generates structured output for easier debugging 💡 Why this matters: In real-world systems, logs grow rapidly and manual analysis doesn't scale. This project helped me understand the basics of log analysis used in observability and incident response. 🚀 GitHub Repo: https://lnkd.in/dEZyK7qH 🔧 Next step: Planning to enhance this with CLI support, pattern detection, and real-time monitoring. Would love to hear your feedback! #SRE #Python #DevOps #Observability #LogAnalysis #LearningInPublic #GitHub

A Model Context Protocol (MCP) server that provides Python code intelligence to AI coding agents. It exposes documentation lookup, environment introspection, code analysis, and diagnostics as structured MCP tools — enabling agents like Claude Code, Cursor, and others to generate more accurate, version-compatible Python code with fewer tokens. https://lnkd.in/g5sBvisP

Day 9 of #100DaysOfCode – Mastering Lists in Python 🐍 Today’s focus was completely on one powerful concept: 👉 Lists – the backbone of data handling in Python Instead of jumping between topics, I went deep into list operations and logic building 💻🧠 ✨ What I practiced today (Programs 101–115): 🔹 Core list operations ✔️ Sum, product, count of elements ✔️ Finding largest & smallest (without built-ins) ✔️ Second largest & second smallest 🔹 Logical problem solving ✔️ Count even & odd numbers ✔️ Separate positive & negative values ✔️ Find indices of elements 🔹 Real-world list handling ✔️ Remove duplicates (without set) ✔️ Reverse list using loop ✔️ Copy list manually ✔️ Rotate list 💡 Key Learning: Lists are not just collections… They are the foundation for solving real-world problems Today helped me understand: 👉 How to think without built-in shortcuts 👉 How logic works behind the scenes 🔥 The more I practice, the more confident I feel in problem solving 🙏 Special thanks to Global Quest Technologies (GQT) for continuous support and guidance throughout this journey 💬 One step closer to becoming a better developer every day Global Quest Technologies ✨ #100DaysOfCode #Day9 #Python #PythonProgramming #CodingJourney #LearnPython #DataStructures #ListsInPython #ProblemSolving #DeveloperMindset #TechSkills #SoftwareDevelopment #Consistency #GlobalQuestTechnologies #GQT

Day 2/30 – Building with Python Recently, I worked on a Vehicle Feedback System using Python The idea behind this project was to create a simple system where users can: 📝 Submit feedback about vehicles 📊 Store and manage responses efficiently Through this project, I learned: ✨ How to handle user input and data ✨ Basic logic building and structuring a program ✨ The importance of user-friendly systems This is just the beginning — I’m planning to improve it further by adding: OTP-based authentication for better security Database integration for scalability Possibly a simple UI for better user experience Building projects like this is helping me understand how real-world systems evolve step by step Would love your suggestions or ideas to improve this further! #Day2 #PythonProject #LearningInPublic #StudentDeveloper #BuildInPublic #TechJourney

Machine Learning Financial Data using bt #machinelearning #datascience #financialdata #bt bt is a flexible backtesting framework for Python used to test quantitative trading strategies. Backtesting is the process of testing a strategy over a given data set. This framework allows you to easily create strategies that mix and match different Algos. https://lnkd.in/gEMPWX-M

I’m checking this out. As we need to select the “best” platform for our genAI applications, the permutations become truly daunting. Adrian provides a framework and code to do so - scoring and promoting the highest rated candidates. Fascinating!

𝙂𝙞𝙩 𝙘𝙤𝙢𝙢𝙖𝙣𝙙𝙨 𝙖𝙧𝙚 𝙚𝙖𝙨𝙮. 𝙂𝙞𝙩 𝙥𝙧𝙤𝙗𝙡𝙚𝙢𝙨 𝙖𝙧𝙚 𝙣𝙤𝙩. Everything works fine… until it breaks. And that’s where most developers get stuck. You can clone, commit, and push. But real challenges look like this: ➥ How do you pull without losing your work? ➥ How do you commit only what matters? ➥ How do you undo mistakes safely? ➥ How do you resolve conflicts cleanly? ➥ What do you do when your push gets rejected? This guide focuses on real Git problems you face daily and shows exactly what to do in each situation. Git isn’t about memorizing commands. It’s about knowing what to do when things go wrong. Doc Credit - Respective Owner ♻️ Repost if you found this useful 🤝 Follow Sattari Sateesh Kumar for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning #dataengineering #sqllearning #dataengineeringinterview #azuredataengineer #bigdata #spark #datalearning #datacareer #azuredataengineering #dataengineeringjobs #linkedinlearning #dataengineeringlearning

You write for loops every day. Do you know what actually runs underneath them? Day 03 of 30 -- Generators and Iterators Deep Dive Advanced Python + Real Projects Series Python calls iter() to get the iterator, then next() repeatedly until StopIteration is raised. That is every for loop you have ever written. And yield pauses the function, hands the value out, and resumes from the exact same line next time. Today's topic covers: The lazy vs eager evaluation problem -- why loading 10GB into a list crashes servers The full iterator protocol -- what powers every for loop 3 types -- generator function, expression, async generator Annotated syntax -- basic, yield from, and the send() two-way pattern Real fintech pipeline -- 52GB log file, 4.2MB memory used 5 production mistakes including exhausting a generator twice Generator pipeline architecture -- identical to Unix pipes Key insight: Don't store what you can stream. #Python #PythonProgramming #DataEngineering #BackendDevelopment #LearnPython #100DaysOfCode #PythonDeveloper #SoftwareEngineering #TechContent #BuildInPublic #TechIndia #CleanCode #CodingTips #CodeNewbie #LinkedInCreator #PythonTutorial

📣 SynapseKit v0.6.8 is live. Your agents can now search PubMed, GitHub, and YouTube. Send emails. Query your own vector store. All with zero new dependencies for most of it. That last one matters more than it sounds- every tool you add to an agent is a potential point of failure. We built these to be stdlib-first wherever possible. Also in this release: WebSocket streaming for graph workflows and structured execution tracing with timestamps. So when something breaks in production, you know exactly where and how long each node took. What SynapseKit looks like today: ⚡ 743 tests 🔌 15 LLM providers 🛠️ 29 built-in tools 🔍 18 retrieval strategies 🧠 8 memory backends 📄 14 document loaders 💾 4 cache backends 🔗 2 hard dependencies Async-native from day one. Not retrofitted. No hidden chains. No magic. Just Python you can actually read. pip install synapsekit 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MachineLearning #Agents #SynapseKit

From a simple log parser to simulating real SRE scenarios I extended my Log Analyzer project to make it more aligned with real-world production systems and incident handling. 🔧 What’s new: • Regex-based log parsing to extract timestamp, log level, and message • Top N error analysis using Python’s Counter • Error spike detection based on a time window (simulating incident conditions) 📊 Example insight: The tool can now detect abnormal error spikes within a short duration — something SREs rely on during production incidents. 💡 What I learned: Log analysis isn’t just about counting errors — it’s about identifying patterns, trends, and anomalies over time. 🔗 Project: https://lnkd.in/dEZyK7qH Next step: exploring real-time log monitoring and alerting integrations. Would love your feedback! #SRE #DevOps #Python #Observability #SiteReliabilityEngineering #LearningInPublic #GitHub