๐ HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 18 min read
How approximate vector search silently degrades Recallโand what to do about It
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 18 min read
How approximate vector search silently degrades Recallโand what to do about It
#DataScience #AI #Python
๐ I Evaluated Half a Million Credit Records with Federated Learning. Hereโs What I Found
๐ Category: DATA SCIENCE
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 12 min read
Why privacy breaks fairness at small scaleโand how collaboration fixes both without sharing a singleโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 12 min read
Why privacy breaks fairness at small scaleโand how collaboration fixes both without sharing a singleโฆ
#DataScience #AI #Python
๐ Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 21 min read
Human-guided AI collaboration
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 21 min read
Human-guided AI collaboration
#DataScience #AI #Python
โค1
Forwarded from Machine Learning with Python
๐๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ_๐๐๐๐ญ๐จ๐ซ_๐๐๐๐ก๐ข๐ง๐๐ฌ_๐๐๐โฃ.pdf
5.8 MB
๐ ๐๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ ๐๐๐๐ญ๐จ๐ซ ๐๐๐๐ก๐ข๐ง๐๐ฌ (๐๐๐)โฃ
๐น What I covered todayโฃ
What SVM is and how it worksโฃ
Concept of hyperplane, margin, and support vectorsโฃ
Hard margin vs Soft marginโฃ
Role of kernel trickโฃ
โฃ
When SVM performs better than other classifiersโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐๐ถ๐ฑ๐ฑ๐ฐ๐ณ๐ต ๐๐ฆ๐ค๐ต๐ฐ๐ณ ๐๐ข๐ค๐ฉ๐ช๐ฏ๐ฆ (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐ข๐ต ๐ข๐ณ๐ฆ ๐ด๐ถ๐ฑ๐ฑ๐ฐ๐ณ๐ต ๐ท๐ฆ๐ค๐ต๐ฐ๐ณ๐ด?โฃ
3๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ ๐ช๐ฏ ๐๐๐?โฃ
4๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐ฉ๐ข๐ณ๐ฅ ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ ๐ข๐ฏ๐ฅ ๐ด๐ฐ๐ง๐ต ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ ๐ต๐ณ๐ช๐ค๐ฌ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ช๐ด ๐ช๐ต ๐ฏ๐ฆ๐ฆ๐ฅ๐ฆ๐ฅ?โฃ
6๏ธโฃ ๐๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ๐ด ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐ (๐๐ช๐ฏ๐ฆ๐ข๐ณ, ๐๐ฐ๐ญ๐บ๐ฏ๐ฐ๐ฎ๐ช๐ข๐ญ, ๐๐๐)?โฃ
7๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ณ๐ฐ๐ญ๐ฆ ๐ฐ๐ง ๐ (๐ณ๐ฆ๐จ๐ถ๐ญ๐ข๐ณ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ ๐ฑ๐ข๐ณ๐ข๐ฎ๐ฆ๐ต๐ฆ๐ณ)?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐จ๐ข๐ฎ๐ฎ๐ข ๐ช๐ฏ ๐๐๐ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ?โฃ
9๏ธโฃ ๐๐ข๐ฏ #๐๐๐ ๐ฃ๐ฆ ๐ถ๐ด๐ฆ๐ฅ ๐ง๐ฐ๐ณ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ? (๐๐๐)โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ ๐๐๐?โฃ
https://t.me/CodeProgrammerโ๏ธ
๐น What I covered todayโฃ
What SVM is and how it worksโฃ
Concept of hyperplane, margin, and support vectorsโฃ
Hard margin vs Soft marginโฃ
Role of kernel trickโฃ
โฃ
When SVM performs better than other classifiersโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐๐ถ๐ฑ๐ฑ๐ฐ๐ณ๐ต ๐๐ฆ๐ค๐ต๐ฐ๐ณ ๐๐ข๐ค๐ฉ๐ช๐ฏ๐ฆ (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐ข๐ต ๐ข๐ณ๐ฆ ๐ด๐ถ๐ฑ๐ฑ๐ฐ๐ณ๐ต ๐ท๐ฆ๐ค๐ต๐ฐ๐ณ๐ด?โฃ
3๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ ๐ช๐ฏ ๐๐๐?โฃ
4๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐ฉ๐ข๐ณ๐ฅ ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ ๐ข๐ฏ๐ฅ ๐ด๐ฐ๐ง๐ต ๐ฎ๐ข๐ณ๐จ๐ช๐ฏ?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ ๐ต๐ณ๐ช๐ค๐ฌ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ช๐ด ๐ช๐ต ๐ฏ๐ฆ๐ฆ๐ฅ๐ฆ๐ฅ?โฃ
6๏ธโฃ ๐๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ๐ด ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐ (๐๐ช๐ฏ๐ฆ๐ข๐ณ, ๐๐ฐ๐ญ๐บ๐ฏ๐ฐ๐ฎ๐ช๐ข๐ญ, ๐๐๐)?โฃ
7๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ณ๐ฐ๐ญ๐ฆ ๐ฐ๐ง ๐ (๐ณ๐ฆ๐จ๐ถ๐ญ๐ข๐ณ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ ๐ฑ๐ข๐ณ๐ข๐ฎ๐ฆ๐ต๐ฆ๐ณ)?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐จ๐ข๐ฎ๐ฎ๐ข ๐ช๐ฏ ๐๐๐ ๐ฌ๐ฆ๐ณ๐ฏ๐ฆ๐ญ?โฃ
9๏ธโฃ ๐๐ข๐ฏ #๐๐๐ ๐ฃ๐ฆ ๐ถ๐ด๐ฆ๐ฅ ๐ง๐ฐ๐ณ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ? (๐๐๐)โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ ๐๐๐?โฃ
https://t.me/CodeProgrammer
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๐ Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)
๐ Category: DATA SCIENCE
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 13 min read
My take after 10 years in Supply Chain on why this can be an excellentโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-07 | โฑ๏ธ Read time: 13 min read
My take after 10 years in Supply Chain on why this can be an excellentโฆ
#DataScience #AI #Python
โค3
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The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices.
Encoding matrices as graphs is a cheat code, making complex behavior simple to study.
https://t.me/DataScienceM
Encoding matrices as graphs is a cheat code, making complex behavior simple to study.
https://t.me/DataScienceM
โค5
๐ Beyond Prompting: The Power of Context Engineering
๐ Category: ARTIFICIAL INTELLIGENCE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 60 min read
Using ACE to create self-improving LLM workflows and structured playbooks
#DataScience #AI #Python
๐ Category: ARTIFICIAL INTELLIGENCE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 60 min read
Using ACE to create self-improving LLM workflows and structured playbooks
#DataScience #AI #Python
โค2
๐ Retrieval for Time-Series: How Looking Back Improves Forecasts
๐ Category: DATA SCIENCE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 13 min read
Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series dataโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 13 min read
Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series dataโฆ
#DataScience #AI #Python
Machine Learning
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Just send what you want and it will design everything for you and the possibility to make money from your app ๐
๐ How to Improve the Performance of Visual Anomaly Detection Models
๐ Category: COMPUTER VISION
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
Apply the best methods from academia to get the most out of practical applications
#DataScience #AI #Python
๐ Category: COMPUTER VISION
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
Apply the best methods from academia to get the most out of practical applications
#DataScience #AI #Python
๐ Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)
๐ Category: DATA ENGINEERING
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
PostgreSQL is fast. Whether your Python code can or should keep up depends on context.โฆ
#DataScience #AI #Python
๐ Category: DATA ENGINEERING
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
PostgreSQL is fast. Whether your Python code can or should keep up depends on context.โฆ
#DataScience #AI #Python
๐ Data Science Spotlight: Selected Problems from Advent of Code 2025
๐ Category: DATA SCIENCE
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 19 min read
Hands-on walkthroughs of problems and solution approaches that power realโworld data science use cases
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 19 min read
Hands-on walkthroughs of problems and solution approaches that power realโworld data science use cases
#DataScience #AI #Python
๐ Mastering Non-Linear Data: A Guide to Scikit-Learnโs SplineTransformer
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 7 min read
Forget stiff lines and wild polynomials. Discover why Splines are the โGoldilocksโ of feature engineering,โฆ
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 7 min read
Forget stiff lines and wild polynomials. Discover why Splines are the โGoldilocksโ of feature engineering,โฆ
#DataScience #AI #Python
๐ Teaching a Neural Network the Mandelbrot Set
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
And why Fourier features change everything
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
And why Fourier features change everything
#DataScience #AI #Python
๐ TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More
๐ Category: THE VARIABLE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 3 min read
Donโt miss our most popular articles of the previous month
#DataScience #AI #Python
๐ Category: THE VARIABLE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 3 min read
Donโt miss our most popular articles of the previous month
#DataScience #AI #Python
๐ฉโ๐ป FREE 2026 IT Learning Kits Giveaway
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๐ Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI
๐ Category: DATA SCIENCE
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 11 min read
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessonsโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 11 min read
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessonsโฆ
#DataScience #AI #Python
๐ How LLMs Handle Infinite Context With Finite Memory
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
Achieving infinite context with 114ร less memory
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
Achieving infinite context with 114ร less memory
#DataScience #AI #Python
โค1
๐ Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
๐ Category: FEDERATED LEARNING
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 10 min read
Understanding the foundations of federated learning
#DataScience #AI #Python
๐ Category: FEDERATED LEARNING
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 10 min read
Understanding the foundations of federated learning
#DataScience #AI #Python
Forwarded from Machine Learning with Python
๐_๐๐๐๐ซ๐๐ฌ๐ญ_๐๐๐ข๐ ๐ก๐๐จ๐ซ๐ฌ_๐๐๐โฃ.pdf
2.4 MB
๐ง ๐-๐๐๐๐ซ๐๐ฌ๐ญ ๐๐๐ข๐ ๐ก๐๐จ๐ซ๐ฌ (๐๐๐)โฃ
๐น ๐๐ก๐๐ญ ๐ ๐๐จ๐ฏ๐๐ซ๐๐ ๐ญ๐จ๐๐๐ฒโฃ
๐๐ก๐๐ญ ๐๐๐ ๐ข๐ฌ ๐๐ง๐ ๐ก๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌโฃ
๐๐ข๐๐๐๐ซ๐๐ง๐๐ ๐๐๐ญ๐ฐ๐๐๐ง ๐๐๐ ๐๐จ๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐ฏ๐ฌ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐งโฃ
๐๐จ๐ฅ๐ ๐จ๐ ๐ (๐ก๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ)โฃ
๐๐ข๐ฌ๐ญ๐๐ง๐๐ ๐ฆ๐๐ญ๐ซ๐ข๐๐ฌ: ๐๐ฎ๐๐ฅ๐ข๐๐๐๐ง ๐ฏ๐ฌ ๐๐๐ง๐ก๐๐ญ๐ญ๐๐งโฃ
๐๐ก๐ฒ ๐๐๐ ๐ข๐ฌ ๐๐๐ฅ๐ฅ๐๐ ๐ ๐ฅ๐๐ณ๐ฒ / ๐ข๐ง๐ฌ๐ญ๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐๐ซโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐-๐๐ฆ๐ข๐ณ๐ฆ๐ด๐ต ๐๐ฆ๐ช๐จ๐ฉ๐ฃ๐ฐ๐ณ๐ด (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐บ ๐ช๐ด ๐๐๐ ๐ค๐ข๐ญ๐ญ๐ฆ๐ฅ ๐ข ๐ญ๐ข๐ป๐บ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐ญ๐จ๐ฐ๐ณ๐ช๐ต๐ฉ๐ฎ?โฃ
3๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐๐๐ ๐ค๐ญ๐ข๐ด๐ด๐ช๐ง๐ช๐ค๐ข๐ต๐ช๐ฐ๐ฏ ๐ข๐ฏ๐ฅ ๐๐๐ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ?โฃ
4๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐บ๐ฐ๐ถ ๐ค๐ฉ๐ฐ๐ฐ๐ด๐ฆ ๐ต๐ฉ๐ฆ ๐ท๐ข๐ญ๐ถ๐ฆ ๐ฐ๐ง ๐?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฉ๐ข๐ฑ๐ฑ๐ฆ๐ฏ๐ด ๐ธ๐ฉ๐ฆ๐ฏ ๐ ๐ช๐ด ๐ต๐ฐ๐ฐ ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฐ๐ณ ๐ต๐ฐ๐ฐ ๐ญ๐ข๐ณ๐จ๐ฆ?โฃ
6๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฅ๐ช๐ด๐ต๐ข๐ฏ๐ค๐ฆ ๐ฎ๐ฆ๐ต๐ณ๐ช๐ค๐ด ๐ข๐ณ๐ฆ ๐ค๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ๐ญ๐บ ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐?โฃ
7๏ธโฃ ๐๐ฉ๐บ ๐ฅ๐ฐ๐ฆ๐ด ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ ๐ฑ๐ฐ๐ฐ๐ณ๐ญ๐บ ๐ฐ๐ฏ ๐ฉ๐ช๐จ๐ฉ-๐ฅ๐ช๐ฎ๐ฆ๐ฏ๐ด๐ช๐ฐ๐ฏ๐ข๐ญ ๐ฅ๐ข๐ต๐ข?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ต๐ช๐ฎ๐ฆ ๐ค๐ฐ๐ฎ๐ฑ๐ญ๐ฆ๐น๐ช๐ต๐บ ๐ฐ๐ง ๐๐๐?โฃ
9๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐๐-๐๐ณ๐ฆ๐ฆ ๐ข๐ฏ๐ฅ ๐๐ข๐ญ๐ญ-๐๐ณ๐ฆ๐ฆ ๐ช๐ฎ๐ฑ๐ณ๐ฐ๐ท๐ฆ ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ?โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ #๐๐๐?โฃ
https://t.me/CodeProgrammerโญ๏ธ
๐น ๐๐ก๐๐ญ ๐ ๐๐จ๐ฏ๐๐ซ๐๐ ๐ญ๐จ๐๐๐ฒโฃ
๐๐ก๐๐ญ ๐๐๐ ๐ข๐ฌ ๐๐ง๐ ๐ก๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌโฃ
๐๐ข๐๐๐๐ซ๐๐ง๐๐ ๐๐๐ญ๐ฐ๐๐๐ง ๐๐๐ ๐๐จ๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐ฏ๐ฌ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐งโฃ
๐๐จ๐ฅ๐ ๐จ๐ ๐ (๐ก๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ)โฃ
๐๐ข๐ฌ๐ญ๐๐ง๐๐ ๐ฆ๐๐ญ๐ซ๐ข๐๐ฌ: ๐๐ฎ๐๐ฅ๐ข๐๐๐๐ง ๐ฏ๐ฌ ๐๐๐ง๐ก๐๐ญ๐ญ๐๐งโฃ
๐๐ก๐ฒ ๐๐๐ ๐ข๐ฌ ๐๐๐ฅ๐ฅ๐๐ ๐ ๐ฅ๐๐ณ๐ฒ / ๐ข๐ง๐ฌ๐ญ๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐๐ซโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
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1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐-๐๐ฆ๐ข๐ณ๐ฆ๐ด๐ต ๐๐ฆ๐ช๐จ๐ฉ๐ฃ๐ฐ๐ณ๐ด (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐บ ๐ช๐ด ๐๐๐ ๐ค๐ข๐ญ๐ญ๐ฆ๐ฅ ๐ข ๐ญ๐ข๐ป๐บ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐ญ๐จ๐ฐ๐ณ๐ช๐ต๐ฉ๐ฎ?โฃ
3๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐๐๐ ๐ค๐ญ๐ข๐ด๐ด๐ช๐ง๐ช๐ค๐ข๐ต๐ช๐ฐ๐ฏ ๐ข๐ฏ๐ฅ ๐๐๐ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ?โฃ
4๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐บ๐ฐ๐ถ ๐ค๐ฉ๐ฐ๐ฐ๐ด๐ฆ ๐ต๐ฉ๐ฆ ๐ท๐ข๐ญ๐ถ๐ฆ ๐ฐ๐ง ๐?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฉ๐ข๐ฑ๐ฑ๐ฆ๐ฏ๐ด ๐ธ๐ฉ๐ฆ๐ฏ ๐ ๐ช๐ด ๐ต๐ฐ๐ฐ ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฐ๐ณ ๐ต๐ฐ๐ฐ ๐ญ๐ข๐ณ๐จ๐ฆ?โฃ
6๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฅ๐ช๐ด๐ต๐ข๐ฏ๐ค๐ฆ ๐ฎ๐ฆ๐ต๐ณ๐ช๐ค๐ด ๐ข๐ณ๐ฆ ๐ค๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ๐ญ๐บ ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐?โฃ
7๏ธโฃ ๐๐ฉ๐บ ๐ฅ๐ฐ๐ฆ๐ด ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ ๐ฑ๐ฐ๐ฐ๐ณ๐ญ๐บ ๐ฐ๐ฏ ๐ฉ๐ช๐จ๐ฉ-๐ฅ๐ช๐ฎ๐ฆ๐ฏ๐ด๐ช๐ฐ๐ฏ๐ข๐ญ ๐ฅ๐ข๐ต๐ข?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ต๐ช๐ฎ๐ฆ ๐ค๐ฐ๐ฎ๐ฑ๐ญ๐ฆ๐น๐ช๐ต๐บ ๐ฐ๐ง ๐๐๐?โฃ
9๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐๐-๐๐ณ๐ฆ๐ฆ ๐ข๐ฏ๐ฅ ๐๐ข๐ญ๐ญ-๐๐ณ๐ฆ๐ฆ ๐ช๐ฎ๐ฑ๐ณ๐ฐ๐ท๐ฆ ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ?โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ #๐๐๐?โฃ
https://t.me/CodeProgrammer
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