📌 How to Scale Your LLM usage
🗂 Category: AGENTIC AI
🕒 Date: 2025-11-29 | ⏱️ Read time: 7 min read
Effectively scaling your Large Language Model (LLM) usage is crucial for unlocking major productivity improvements. This guide outlines key strategies for expanding LLM integration from proof-of-concept to full-scale deployment, enabling your teams to harness the full power of AI for enhanced operational efficiency and innovation. Learn the best practices for managing costs, ensuring reliability, and maximizing the impact of LLMs across your organization.
#LLM #AIScaling #Productivity #ArtificialIntelligence
🗂 Category: AGENTIC AI
🕒 Date: 2025-11-29 | ⏱️ Read time: 7 min read
Effectively scaling your Large Language Model (LLM) usage is crucial for unlocking major productivity improvements. This guide outlines key strategies for expanding LLM integration from proof-of-concept to full-scale deployment, enabling your teams to harness the full power of AI for enhanced operational efficiency and innovation. Learn the best practices for managing costs, ensuring reliability, and maximizing the impact of LLMs across your organization.
#LLM #AIScaling #Productivity #ArtificialIntelligence
❤1
📌 Metric Deception: When Your Best KPIs Hide Your Worst Failures
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-29 | ⏱️ Read time: 7 min read
Your best-performing KPIs could be hiding your worst failures. This article explores 'metric deception,' where trusted legacy metrics become misleading and mask underlying problems. The most dangerous KPIs aren't the ones that are obviously broken, but those that are trusted long after they've lost their strategic relevance. It's a critical reminder for leaders and data teams to continuously audit their metrics to ensure they drive correct business decisions and reflect true performance.
#KPI #DataAnalytics #BusinessIntelligence #Metrics #DataStrategy
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-29 | ⏱️ Read time: 7 min read
Your best-performing KPIs could be hiding your worst failures. This article explores 'metric deception,' where trusted legacy metrics become misleading and mask underlying problems. The most dangerous KPIs aren't the ones that are obviously broken, but those that are trusted long after they've lost their strategic relevance. It's a critical reminder for leaders and data teams to continuously audit their metrics to ensure they drive correct business decisions and reflect true performance.
#KPI #DataAnalytics #BusinessIntelligence #Metrics #DataStrategy
❤1
📌 The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
❤1
📌 The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 19 min read
The Greedy Boruta algorithm offers a significant performance enhancement for feature selection. As a modification of the standard Boruta method, it dramatically reduces computation time. This speed increase is achieved without sacrificing recall, ensuring high sensitivity in identifying all relevant features. It's a powerful optimization for data scientists seeking to accelerate their machine learning workflows while preserving model quality.
#FeatureSelection #MachineLearning #DataScience #Algorithms
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 19 min read
The Greedy Boruta algorithm offers a significant performance enhancement for feature selection. As a modification of the standard Boruta method, it dramatically reduces computation time. This speed increase is achieved without sacrificing recall, ensuring high sensitivity in identifying all relevant features. It's a powerful optimization for data scientists seeking to accelerate their machine learning workflows while preserving model quality.
#FeatureSelection #MachineLearning #DataScience #Algorithms
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📌 The Problem with AI Browsers: Security Flaws and the End of Privacy
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-01 | ⏱️ Read time: 5 min read
Current AI-powered browsers, such as Atlas, are facing scrutiny for significant failures in privacy, security, and censorship. These critical flaws raise serious concerns about user data protection and could signal a major threat to digital privacy as the technology becomes more widespread.
#AIBrowsers #Cybersecurity #DataPrivacy #Censorship
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-01 | ⏱️ Read time: 5 min read
Current AI-powered browsers, such as Atlas, are facing scrutiny for significant failures in privacy, security, and censorship. These critical flaws raise serious concerns about user data protection and could signal a major threat to digital privacy as the technology becomes more widespread.
#AIBrowsers #Cybersecurity #DataPrivacy #Censorship
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📌 Learning, Hacking, and Shipping ML
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2025-12-01 | ⏱️ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2025-12-01 | ⏱️ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
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📌 Why AI Alignment Starts With Better Evaluation
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Achieving true AI alignment is fundamentally dependent on robust evaluation. To ensure AI systems operate according to human values and intentions, we must first develop sophisticated methods to measure their behavior, test for potential risks, and identify misalignments. This goes beyond standard performance benchmarks, requiring a deeper focus on creating comprehensive testing frameworks. Without the ability to accurately assess a model's alignment, any attempt to steer it becomes guesswork, highlighting why better evaluation is the critical first step toward building safer and more reliable AI.
#AIAlignment #AISafety #AIEvaluation #ResponsibleAI
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Achieving true AI alignment is fundamentally dependent on robust evaluation. To ensure AI systems operate according to human values and intentions, we must first develop sophisticated methods to measure their behavior, test for potential risks, and identify misalignments. This goes beyond standard performance benchmarks, requiring a deeper focus on creating comprehensive testing frameworks. Without the ability to accurately assess a model's alignment, any attempt to steer it becomes guesswork, highlighting why better evaluation is the critical first step toward building safer and more reliable AI.
#AIAlignment #AISafety #AIEvaluation #ResponsibleAI
❤2
📌 How to Generate QR Codes in Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-02 | ⏱️ Read time: 7 min read
Unlock the ability to generate QR codes with Python. This beginner-friendly tutorial provides a step-by-step guide to using the popular "qrcode" package. Learn how to easily create and customize QR codes for your applications, from encoding URLs to embedding custom data.
#Python #QRCode #Programming #PythonTutorial
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-02 | ⏱️ Read time: 7 min read
Unlock the ability to generate QR codes with Python. This beginner-friendly tutorial provides a step-by-step guide to using the popular "qrcode" package. Learn how to easily create and customize QR codes for your applications, from encoding URLs to embedding custom data.
#Python #QRCode #Programming #PythonTutorial
❤4
📌 The Machine Learning Lessons I’ve Learned This Month
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
❤3
📌 The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
❤3
📌 The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-02 | ⏱️ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-02 | ⏱️ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
❤2
📌 JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-02 | ⏱️ Read time: 12 min read
When processing large JSON payloads, the choice of a parsing library is critical for system performance. This benchmark analysis explores the trade-offs between various libraries, focusing on key metrics like parsing speed, memory consumption, and overall scalability. Discover which tools offer the optimal balance for high-volume data scenarios, helping you make informed decisions for building efficient and resilient applications.
#JSON #Performance #Benchmarking #DataEngineering #Backend
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-02 | ⏱️ Read time: 12 min read
When processing large JSON payloads, the choice of a parsing library is critical for system performance. This benchmark analysis explores the trade-offs between various libraries, focusing on key metrics like parsing speed, memory consumption, and overall scalability. Discover which tools offer the optimal balance for high-volume data scenarios, helping you make informed decisions for building efficient and resilient applications.
#JSON #Performance #Benchmarking #DataEngineering #Backend
❤3
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❤2
Found a great resource for everyone who wants to improve their math skills for deep learning — this section
No fluff, just what you need to work in ML. Mathematical analysis, linear algebra, probability theory — all in a convenient format and immediately with code
A nice bonus: you can choose the dialect in which examples are shown (PyTorch, Keras, or MXNET).
https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html
By the way, the other chapters are just as worthy👾
😱 @DataScienceM
No fluff, just what you need to work in ML. Mathematical analysis, linear algebra, probability theory — all in a convenient format and immediately with code
A nice bonus: you can choose the dialect in which examples are shown (PyTorch, Keras, or MXNET).
https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html
By the way, the other chapters are just as worthy
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📌 The Architecture Behind Web Search in AI Chatbots
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-04 | ⏱️ Read time: 16 min read
Explore the technical architecture powering web search in AI chatbots. This analysis breaks down how generative models retrieve and integrate live web data to provide current answers, highlighting the crucial shift towards Generative Engine Optimization (GEO). Learn what this new paradigm means for content visibility in an AI-first search landscape, moving beyond traditional SEO.
#AI #GEO #Chatbots #Search #RAG
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-04 | ⏱️ Read time: 16 min read
Explore the technical architecture powering web search in AI chatbots. This analysis breaks down how generative models retrieve and integrate live web data to provide current answers, highlighting the crucial shift towards Generative Engine Optimization (GEO). Learn what this new paradigm means for content visibility in an AI-first search landscape, moving beyond traditional SEO.
#AI #GEO #Chatbots #Search #RAG
❤2
📌 Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
❤3
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❤2
📌 The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
❤4
📌 How to Turn Your LLM Prototype into a Production-Ready System
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
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