Python

04/10/2026

04/10/2026

Shout out to my newest followers! Excited to have you onboard! Antonio Rivas, Jerome Hunter, Mark Catenza Pangilinan, Jonesbabu Selvam, Alex Najoli, Karthik Ashokan, Akalu Alaro, Ionel Razvan Ghiorghe, Kevin Lefeve, Pradeep Selvaraj, Vikesh Hasowa, Abdulqadr Mohammadyasin, Amr D Hamdi, Muhammad Sajid, Δημητρης Πατητης, Joel Lin, Pranjal Yadu, Jezel, Tharindu Dilshan, Yokesh Babu Sundaresan, Stear Sea, Deenee Yakubu, Darius Wilder, Sigimoo Koo, Kimi Autto, Abdellatif Elias Brixi Nigassa, Bert Chan, Faqir Yar, Franck Lokossué, Esther Mortimer, Donald Balfour, Joseph Mahukpéhou Zounkimba, Yali Galindo, Ahmed Ashraf, Itokiana Raz, Marc Jerry, Sar E Gam, Thoi Shotoi, Wartha Happyi, Ali Khalaf, Given Norbert, Blero Hgoing, Low SO, Emry Pablo, Al Ameen Aminu, Jelly Funds, Lance Dumagcao, Phoenix Reborrn, Abdulrhman Ahmed, Godwin Yashe

02/10/2026

Can you fix it without ai ??

02/10/2026

𝗟𝗟𝗠 𝘃𝘀 𝗥𝗔𝗚 𝘃𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝘃𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 🤖
These terms are often used interchangeably, but they describe very different levels of capability.
𝗟𝗟𝗠
An LLM takes a prompt + provided context and generates a response based on its learned parameters.
At its core:
Prompt → LLM → Response
It predicts tokens sequentially, but by itself it doesn't automatically retrieve external knowledge, execute actions, or manage a workflow.
𝗥𝗔𝗚
RAG adds external knowledge retrieval before generation.
Query → Retriever → Relevant Context → LLM → Grounded Response
Instead of relying only on what the model learned during training, the LLM receives relevant information from sources such as documents, databases, or vector stores.
This makes responses more grounded, but RAG still doesn't guarantee correctness.
𝗔𝗜 𝗔𝗴𝗲𝗻𝘁
An AI agent goes beyond generating an answer.
It works toward a goal.
The agent can:
→ Understand the goal
→ Decide what to do next
→ Call tools or APIs
→ Observe the result
→ Update its task state
→ Repeat until the task is completed
A simplified loop looks like:
Goal → Decide → Act → Observe → Repeat
This feedback loop is what makes an agent capable of taking actions rather than simply returning text.
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
Agentic AI takes this further by combining autonomous decision-making, tools, memory/state, workflows, and orchestration to accomplish a broader objective.
It may involve a single agent or multiple specialized agents working together.
For example:
Objective → Orchestrator → Agents/Workflows → Tools → Evaluate → Replan → Outcome
The biggest difference is the progression:
LLM → Generates
RAG → Retrieves + Generates
AI Agent → Decides + Acts
Agentic AI → Orchestrates + Adapts toward an objective
As AI systems evolve, the shift is moving from models that simply answer questions to systems that can reason about tasks, interact with tools, and execute workflows.

02/10/2026

Want to learn Python but don’t know where to start? Follow a clear path and build your skills step by step.

🐣 Start with Python basics, logic, loops, and functions.
🧩 Learn OOP and core data structures.
📝 Practice arrays, strings, linked lists, stacks, and queues.
🔍 Move into sorting, searching, hashing, recursion, and dynamic programming.
🌳 Explore trees, graphs, greedy algorithms, and advanced problem-solving.
🚀 Apply your skills through automation, web development, Python packages, and coding challenges.

•Learn one concept, practice it with code, then build something small with it. Consistency matters more than speed.

01/10/2026

Claude gets far more useful when you build the right environment around it.

I would not think of this as “24 things to install.”

I’d think of it as 3 layers that make Claude better at real work.

First: Plugins

These can help with debugging, planning, code review, refactoring, security checks, commit workflows, and development setup.

Then: Skills

This is where Claude becomes more capable across repeatable tasks.

And then comes the part I find most useful: MCP Servers.

This is what connects Claude to the tools and context your workflow already depends on.

If anyone needs all these links..

comment " need all these links... " I'll send it over..

Share it to help others..