π AI in Multiple GPUs: ZeRO & FSDP
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-05 | β±οΈ Read time: 9 min read
Learn how Zero Redundancy Optimizer works, how to implement it from scratch, and how toβ¦
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-05 | β±οΈ Read time: 9 min read
Learn how Zero Redundancy Optimizer works, how to implement it from scratch, and how toβ¦
#DataScience #AI #Python
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
https://t.me/DataScienceMβ
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.ο»Ώ
What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.
The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.
In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
https://t.me/DataScienceM
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β€7π1π₯1
π The Data Teamβs Survival Guide for the Next Era of Data
π Category: DATA SCIENCE
π Date: 2026-03-06 | β±οΈ Read time: 16 min read
6 pillars to declutter your stack, escape the service trap, and build the missing foundationsβ¦
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-06 | β±οΈ Read time: 16 min read
6 pillars to declutter your stack, escape the service trap, and build the missing foundationsβ¦
#DataScience #AI #Python
β€3
π The Black Box Problem: Why AI-Generated Code Stops Being Maintainable
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-06 | β±οΈ Read time: 9 min read
Same notification system, two architectures. Unstructured generation couples everything into a single module. Structured generationβ¦
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-06 | β±οΈ Read time: 9 min read
Same notification system, two architectures. Unstructured generation couples everything into a single module. Structured generationβ¦
#DataScience #AI #Python
β€3
π How to Create Production-Ready Code with Claude Code
π Category: LLM APPLICATIONS
π Date: 2026-03-06 | β±οΈ Read time: 8 min read
Learn how to write robust code with coding agents.
#DataScience #AI #Python
π Category: LLM APPLICATIONS
π Date: 2026-03-06 | β±οΈ Read time: 8 min read
Learn how to write robust code with coding agents.
#DataScience #AI #Python
β€1
π What Makes Quantum Machine Learning βQuantumβ?
π Category: QUANTUM COMPUTING
π Date: 2026-03-06 | β±οΈ Read time: 8 min read
And where is it today?
#DataScience #AI #Python
π Category: QUANTUM COMPUTING
π Date: 2026-03-06 | β±οΈ Read time: 8 min read
And where is it today?
#DataScience #AI #Python
β€1
π Understanding Context and Contextual Retrieval in RAG
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-07 | β±οΈ Read time: 10 min read
Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy
#DataScience #AI #Python
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-07 | β±οΈ Read time: 10 min read
Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy
#DataScience #AI #Python
π The AI Bubble Has a Data Science Escape Hatch
π Category: DATA SCIENCE
π Date: 2026-03-07 | β±οΈ Read time: 12 min read
Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmapβ¦
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-07 | β±οΈ Read time: 12 min read
Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmapβ¦
#DataScience #AI #Python
π LatentVLA: Latent Reasoning Models for Autonomous Driving
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-08 | β±οΈ Read time: 8 min read
What if natural language is not the best abstraction for driving?
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-03-08 | β±οΈ Read time: 8 min read
What if natural language is not the best abstraction for driving?
#DataScience #AI #Python
β€1
π Write C Code Without Learning C: The Magic of PythoC
π Category: PROGRAMMING
π Date: 2026-03-08 | β±οΈ Read time: 9 min read
Compile native, standalone applications using the Python syntax you already know.
#DataScience #AI #Python
π Category: PROGRAMMING
π Date: 2026-03-08 | β±οΈ Read time: 9 min read
Compile native, standalone applications using the Python syntax you already know.
#DataScience #AI #Python
π Machine Learning at Scale: Managing More Than One Model in Production
π Category: MACHINE LEARNING
π Date: 2026-03-09 | β±οΈ Read time: 7 min read
From one model to managing a massive portfolio: What 10 years in the industry taughtβ¦
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-03-09 | β±οΈ Read time: 7 min read
From one model to managing a massive portfolio: What 10 years in the industry taughtβ¦
#DataScience #AI #Python
β€3
π Three OpenClaw Mistakes to Avoid and How to Fix Them
π Category: AGENTIC AI
π Date: 2026-03-09 | β±οΈ Read time: 7 min read
Learn how to set up OpenClaw effectively
#DataScience #AI #Python
π Category: AGENTIC AI
π Date: 2026-03-09 | β±οΈ Read time: 7 min read
Learn how to set up OpenClaw effectively
#DataScience #AI #Python
Forwarded from Machine Learning with Python
π§ Python libraries for AI agents - complexity of learning π₯
π’ Easy
β’ LangChain
β’ tool calling
β’ agent memory
β’ simple agents
β’ CrewAI
β’ agents with roles
β’ collaboration of several agents
β’ SmolAgents
β’ lightweight agents
β’ quick experiments
π‘ Medium
β’ LangGraph
β’ stateful workflow
β’ agent orchestration
β’ LlamaIndex
β’ RAG pipelines
β’ data indexing
β’ knowledge agents
β’ OpenAI Agents SDK
β’ tool integrations
β’ agent workflows
β’ Strands
β’ agent orchestration
β’ task coordination
β’ Semantic Kernel
β’ skills / plugins
β’ AI process orchestration
β’ PydanticAI
β’ typed LLM applications
β’ structured agent workflows
β’ Langroid
β’ message exchange between agents
β’ interaction with tools
π΄ Difficult
β’ AutoGen
β’ multi-agent dialogues
β’ autonomous agent cooperation
β’ DSPy
β’ programmable prompting
β’ optimization of LLM pipelines
β’ A2A
β’ agent-to-agent protocol
β’ distributed agent systems
https://t.me/CodeProgrammerβ
π’ Easy
β’ LangChain
β’ tool calling
β’ agent memory
β’ simple agents
β’ CrewAI
β’ agents with roles
β’ collaboration of several agents
β’ SmolAgents
β’ lightweight agents
β’ quick experiments
π‘ Medium
β’ LangGraph
β’ stateful workflow
β’ agent orchestration
β’ LlamaIndex
β’ RAG pipelines
β’ data indexing
β’ knowledge agents
β’ OpenAI Agents SDK
β’ tool integrations
β’ agent workflows
β’ Strands
β’ agent orchestration
β’ task coordination
β’ Semantic Kernel
β’ skills / plugins
β’ AI process orchestration
β’ PydanticAI
β’ typed LLM applications
β’ structured agent workflows
β’ Langroid
β’ message exchange between agents
β’ interaction with tools
π΄ Difficult
β’ AutoGen
β’ multi-agent dialogues
β’ autonomous agent cooperation
β’ DSPy
β’ programmable prompting
β’ optimization of LLM pipelines
β’ A2A
β’ agent-to-agent protocol
β’ distributed agent systems
https://t.me/CodeProgrammer
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Forwarded from Code With Python
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
β
https://t.me/addlist/8_rRW2scgfRhOTc0
β
https://t.me/Codeprogrammer
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π I Stole a Wall Street Trick to Solve a Google Trends Data Problem
π Category: DATA SCIENCE
π Date: 2026-03-09 | β±οΈ Read time: 14 min read
A methodology for comparing Google Trends data across countries.
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-09 | β±οΈ Read time: 14 min read
A methodology for comparing Google Trends data across countries.
#DataScience #AI #Python
π Building a Like-for-Like solution for Stores in Power BI
π Category: DATA ANALYSIS
π Date: 2026-03-10 | β±οΈ Read time: 10 min read
Like-for-Like (L4L) solutions are essential for comparing elements. Itβs about comparing only comparable elements, inβ¦
#DataScience #AI #Python
π Category: DATA ANALYSIS
π Date: 2026-03-10 | β±οΈ Read time: 10 min read
Like-for-Like (L4L) solutions are essential for comparing elements. Itβs about comparing only comparable elements, inβ¦
#DataScience #AI #Python
π What Are Agent Skills Beyond Claude?
π Category: AGENTIC AI
π Date: 2026-03-10 | β±οΈ Read time: 6 min read
How to design and implement agent skills for custom agents outside the Claude ecosystem
#DataScience #AI #Python
π Category: AGENTIC AI
π Date: 2026-03-10 | β±οΈ Read time: 6 min read
How to design and implement agent skills for custom agents outside the Claude ecosystem
#DataScience #AI #Python
Forwarded from Machine Learning with Python
π A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
β€2
π Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
π Category: DEEP LEARNING
π Date: 2026-03-10 | β±οΈ Read time: 14 min read
I really thought I was onto something big: add a couple of simple domain rulesβ¦
#DataScience #AI #Python
π Category: DEEP LEARNING
π Date: 2026-03-10 | β±οΈ Read time: 14 min read
I really thought I was onto something big: add a couple of simple domain rulesβ¦
#DataScience #AI #Python
β€1
π When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory
π Category: DATA SCIENCE
π Date: 2026-03-10 | β±οΈ Read time: 9 min read
A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-10 | β±οΈ Read time: 9 min read
A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making
#DataScience #AI #Python
Forwarded from Machine Learning with Python
π 23 Years of SPOTO β Claim Your Free IT Certs Prep Kit!
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π₯Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification β SPOTO has got you covered!
β Free Resources :
γ»Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4lk4m3c
γ»IT Certs E-book: https://bit.ly/4bdZOqt
γ»IT Exams Skill Test: https://bit.ly/4sDvi0b
γ»Free AI material and support tools: https://bit.ly/46TpsQ8
γ»Free Cloud Study Guide: https://bit.ly/4lk3dIS
π Join SPOTO 23rd anniversary Lucky Draw:
π± iPhone 17
πfree order
π Amazon Gift Card $50/$100
π AI/CCNA/PMP Course Training + Study Material + eBook
Enter the Draw π: https://bit.ly/3NwkceD
π Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/Cnc5M5353oSBo3savBl397
π¬ Want exam help? Chat with an admin now!
wa.link/rozuuw
β°Last Chance β Get It Before Itβs Gone!