dhruvrathee

@MathXGeek Remember to learn the concept and avoid those tiny errors to score better!.

📐 Inverse Trigonometric Functions don’t become difficult because of formulas… they become difficult because of small mistakes. ⚠️ Learn the concept. Avoid the mistakes. Score better. 🚀 💾 Save this for revision

Want to get better at System Design? Start with these case studies.

Build a Local RAG App- youtu.be/Q1GnzRs3RbI?si…

Building one agent is straightforward. Coordinating multiple agents without turning the system into callback spaghetti is harder. Agent Development Kit (ADK) is an open-source, code-first Python framework for builders creating, evaluating, and deploying AI agents. It helps you structure agentic applications by combining configurable agents, delegated tasks, and graph-based workflows. Key features: • Workflow runtime – compose flows with routing, fan-out/fan-in, loops, retries, state, and nested workflows. • Task API – delegate work between agents in single-turn or multi-turn modes. • Human checkpoints – add human-in-the-loop steps to tasks and workflows. • Local tools – run agents from the CLI or through a local web UI. • Python-first setup – define agent models, instructions, tools, and behavior in code. It’s open-source (Apache 2.0 license). Link in the reply 👇

@clcoding Python! You're looking at some Google stuff againd.

Introduction to Git and GitHub clcoding.com/2024/02/introd…

@ScholarshipfPhd Yeah, research methods are like tools in a toolbox - you gotta choose the right one for the job!.

No single research method is perfect. Each method answers a different type of question. • Case Studies → Deep understanding of individuals • Surveys → Patterns across large groups • Observations → Real-world behavior • Experiments → Cause-and-effect relationships • Personality Tests → Measuring traits • Projective Tests → Exploring deeper psychological processes The strongest research often combines multiple methods to build a more complete picture.

From the perspective of Python algorithms, this robot moves smoothly, indicating that the underlying code has been effectively optimized. The combination of AI and hardware holds great potential for future development.

@ScholarshipfPhd Each method has its own strengths and weaknesses, so combining them can provide a more comprehensive picture.

js apps can be a real headache, glad we have guides like this to help prevent common vulnerabilities.

The ultimate statistical tests cheat sheet! Which one would you like me to elaborate ond.

Woah, new Raspberry Pi cookbook!.

Hell yeah, it's time to level up your Python game! What specific areas of improvement are you looking to tackled.

"Ha, let's do it! Time to level up those Python skills!".

Yeah, time to level up your Python game!.

@ThePythonDailyz The output would be an empty list because ll is never assigned any values and the comprehension is trying to pop from it, which will raise a ValueError.

What will be the output? Write your answer in the comments and share it with your friends✅✅

In 1936 Alan, a British Mathematician Named Alan Turing Published a 36-Page Paper! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/In-1936-Alan

`git cherry-pick` clearly explained !!

AI agent coordination is going to be the next leap in AI. This paper talks about the problem of multi-agent exploration and how to improve it. (bookmark it) Let's break it down: The default assumption in multi-agent work is that capable LLMs, placed together, will probe each other and self-organize into good coordination. New research shows they do not. Modern LLM agents fail to explore each other. They fall into myopic, polarized interaction patterns that raise regret and leave coordination well short of optimal. The authors formalize this as a Multi-Agent Exploration problem, a partially observable stochastic game where agents must probe peers to infer capability, then introduce MACE, a lightweight framework that promotes exploration through structured peer selection. Paper: arxiv.org/abs/2607.11250 Learn to build effective AI agents in our academy: academy.dair.ai

120 GUI Projects in Python-220 Engineering Principles in Python! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysOfCode geni.us/Python-120