Abhishek Das @PythonMeetDev

yo @sama, can u build an AI agent that just wipes out everyone makin noise at 4am while I’m tryna crash after cookin up my company's next-gen AI? like fr bro, let me sleep 😩

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🚀 LLM Calls, Workflows, and Agents — Made Simple! In the world of AI, buzzwords are flying everywhere — LLM calls, workflows, agents... and it's easy to feel overwhelmed. When I first started diving deep into AI development, I often wondered: ➡️ How are these three connected? ➡️ When should I use a simple LLM call vs build a full agent? ➡️ What exactly does an AI workflow look like behind the scenes? ❓ If you've ever had these questions too — you're not alone! Today, I finally put all my learnings together into an article: ✨ "LLM Calls vs Workflows vs Agents (Made Simple) — The AI Trinity Explained" 🔹 I break down each concept in plain English 🔹 I show how they actually work together in real-world AI systems 🔹 I share tips on when to use what (plus common mistakes to avoid) Whether you're a beginner trying to understand the basics, or an AI builder looking for clarity — I wrote this for you. Would love to hear your thoughts if you give it a read! 🙌 👉 medium.com/p/llm-calls-vs… #AI #MachineLearning #LLM #GenerativeAI #DataScience #AIAgents #AIWorkflows #LLMCalls

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Abhishek Das @PythonMeetDev

a year ago

🚀 Instant Data Insights with One Line of Code! Performing Exploratory Data Analysis (EDA) used to be tedious — especially when working with large datasets or developing LLM-based applications. I found myself juggling between Pandas and PySpark just to get basic attribute-level and table-level insights. That’s when I discovered ydata-profiling, a powerful profiling library that changed the game! 🔍 Instantly generate rich data reports ✅ Compare multiple dataset versions ✅ Handle time-series and large-scale data ✅ Respect data sensitivity and PII ✅ Export insights as JSON for programmatic use ✅ Fully customize and embed the report in your workflow ✅ Even profile data from relational/cloud databases With just a single line of code, you get an interactive, visual, and insightful snapshot of your dataset — perfect for ML model development, governance, and data quality checks. 📖 Read the full article here: 👉 medium.com/@abhishekdas69… 📢 If you're working in data engineering, machine learning, or building GenAI apps, I highly recommend checking this out! 🙌 If you find it helpful, give it a like and feel free to connect or follow me for more data engineering content! #DataEngineering #LLM #EDA #Python #YDataProfiling #DataQuality #MachineLearning #PySpark #DataScience #BigData #DataProfiling #OpenSourceAI

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Abhishek Das @PythonMeetDev

a year ago

Python Meets SQLGlot: Seamlessly Parse and Convert SQL Queries Ever struggled with SQL syntax differences between MySQL, PostgreSQL, SparkSQL, or Hive? Or needed to dynamically parse, transpile, or even execute SQL directly within Python? Meet SQLGlot — an insanely powerful Python library that lets you: ✅ Parse SQL into AST ✅ Transpile across dialects ✅ Optimize and clean up queries ✅ Even execute them in memory! In this article, I break down how SQLGlot works and show how you can: 🔄 Convert SQL dialects 🧠 Optimize logic using AST 🧪 Run queries with zero database setup #Python #SQL #SQLGlot #DataEngineering #OpenSource #SQLParsing #Transpiler #DevTools medium.com/p/python-meets…

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Abhishek Das @PythonMeetDev

2 years ago

Data Reconciliation in Python: Exploring Effective Techniques for Dataset Comparison 🚀 Encountered hurdles in your analysis due to data disparities or omissions? 🤔 I've unveiled a fresh manual on robust data reconciliation methods in Python. If ensuring your dataset's precision is a recurring concern, this resource is tailored for you! Keen to delve deeper? Dive into the complete article here: shorturl.at/ju1Y1 What specific data reconciliation obstacles do you face? Share your thoughts in the comments below! hashtag#Python hashtag#DataAnalysis hashtag#DataScience hashtag#MachineLearning hashtag#Analytics hashtag#DataQuality