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A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration

Data integration projects before starting with Data Science and Machine Learning (DS/ML) may not be ideal, as integrating data without knowing its use can lead to unfit data for ML use cases.
It is suggested to integrate data on a use-case-per-use-case basis by working backwards to identify the required data, optimizing value for money in integration efforts.
Drivers for premature data integration include difficulty in identifying AI/ML use cases due to unknown data availability, but this can be better solved by communication and dialogue within teams.
Integrating data without clarity on the ML use case may result in unnecessary data integration leading to increased cost and storage of unused data.
Cultural barriers to data sharing can be better addressed by involving relevant team members in projects and fostering communication rather than mandating data integration.
Setting up a data platform strategy and creating a catalog of dataset descriptions for search can be a cost-effective data discovery method for ML projects.
Data integration should focus on necessities for each use case, and solving organizational, political, and technical challenges prior to ML projects can help tackle data access issues.
In summary, tackling data integration by prioritizing use-case-based integration, fostering communication among teams, and utilizing low-cost data discovery tools can enhance the success of ML projects.

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Tech Radar

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I tried Mind Maps in NotebookLM and it's my new favorite feature

Google's NotebookLM has introduced a new Mind Maps feature, which uses AI to create visual webs of information for organizing thinking and ideas.
Users can upload articles and resources on specific topics, and NotebookLM's AI model generates Mind Maps with branches and subtopics related to the content.
Mind Maps help in organizing information and understanding connections between concepts, making it helpful for learning and planning projects.
While the core experience of Mind Maps in NotebookLM is solid, some users may want more manual control and find that AI-generated branches can occasionally be irrelevant.

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Medium

219

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The DRY Principle in Programming: Are You Truly Applying It?

DRY stands for “Don’t Repeat Yourself” — a principle that emphasizes reducing redundancy in code.
The idea is to write reusable, modular, and maintainable code.
Developers go through different stages of learning and improvement, where DRY fits into their journey.
The stages include learning the fundamentals and writing code that works.

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Netflixtechblog

234

Image Credit: Netflixtechblog

Foundation Model for Personalized Recommendation

Netflix's personalized recommender system faced challenges in maintaining multiple specialized models, leading to the development of a new foundation model focusing on centralized member preference learning.
The foundation model assimilates information from users' comprehensive interaction histories and content at a large scale, enabling distribution of learnings to other models for fine-tuning or through embeddings.
Inspired by large language models (LLMs), the model emphasizes a data-centric approach and leverages semi-supervised learning for enhanced recommendation accuracy.
Tokenization of user interactions helps in structuring sequences for meaningful insights while balancing between detailed data and processing efficiency.
Sparse attention mechanisms and sliding window sampling are utilized during training to handle extensive user interaction histories while maintaining computational efficiency.
The model's architecture includes request-time and post-action features to predict next interactions, with a multi-token prediction objective to capture longer-term dependencies.
The foundation model addresses unique challenges like entity cold-starting by employing incremental training, inference with unseen entities, and combining learnable item ID embeddings with metadata information.
Downstream applications of the model include predictive tasks, utilizing embeddings for various purposes, and fine-tuning with specific data for diverse applications.
Scaling the foundation model for Netflix recommendations involves robust evaluation, efficient training algorithms, and substantial computing resources to enhance generative recommendation tasks.
The transition to a comprehensive system from multiple specialized models signifies a significant advancement in personalized recommendation systems, offering promising results for downstream integrations.

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Medium

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How to Effortlessly Create Memecoins and Profit Daily

Phantom Mint offers a user-friendly platform for minting memecoins.
Users have seen significant profits, with some earning up to $2,000 in less than a month.
Anyone can participate in minting memecoins with zero risk and no prior skills required.
Phantom Mint provides step-by-step instructions and updates to stay ahead in the rapidly changing crypto landscape.

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Medium

224

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Computer Parts Explained: The Team That Powers Your PC

The Central Processing Unit (CPU) is the brain of your computer, responsible for processing instructions and making decisions.
Random Access Memory (RAM) stores temporary data during computer operations, enabling smoother multitasking with more RAM.
The Hard Drive (HDD) or Solid State Drive (SSD) stores files, software, and the operating system of your computer.
The Graphics Processing Unit (GPU) is essential for rendering images, videos, and games, particularly for gaming, video editing, and 3D modeling.

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Medium

390

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Confessions of an AI Whisperer: Dodging Pitfalls and Mastering the Art of the Prompt

The 'Be More Specific!' Vague Request Syndrome
Forgetting the Context: The Amnesiac AI Problem
Ignoring the AI’s 'Personality' & Limits
Provide Rich Context, Assign a Role/Persona, Specify the Output Format, Use Few-Shot Examples, Break It Down, Iterative Refinement

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Medium

158

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Bezos and the Super Rich Get Married While We Wait for Code Review

Jeff Bezos and Lauren Sánchez's wedding is set to take place in Venice, Italy, with a guest list including Oprah, Kim Kardashian, and Katy Perry.
The wedding is being planned by a private company without a website portfolio.
Lauren Sánchez was reportedly amazed by the $2.5 million engagement ring.
The wedding has drawn comparisons to a poorly planned tech product release.

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Idownloadblog

Image Credit: Idownloadblog

Topaz Photo AI v3.6.0 brings second-generation Super Focus & Adjust Lighting models, support for more files

Topaz Photo AI version 3.6.0 brings second-generation Super Focus and Adjust Lighting models.
The Super Focus V2 model is their most powerful sharpening and focus recovery model ever.
The second-generation Adjust Lighting model can correct colors, highlights, and shadows in images.
The update also adds support for more file formats including .tif, .jpg, .heic, and .webp.

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Medium

355

Why Your Business Needs a Custom Web and Mobile App in 2025

Mobile and web apps provide direct communication channels for businesses and customers, enhancing engagement through features like push notifications and personalized recommendations.
Well-designed apps increase a brand's visibility and credibility, building trust and loyalty among customers.
Custom apps offer unique features tailored to a business, standing out from off-the-shelf solutions and growing with the business.
Apps with integrated e-commerce and in-app purchases can generate new revenue streams and provide insights into customer behavior for optimizing marketing strategies.

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Siliconangle

338

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DOGE reportedly planning to rewrite Social Security Administration’s software

The U.S. Department of Government Efficiency (DOGE) plans to rewrite the software used by the U.S. Social Security Administration.
The planned initiative aims to switch the COBOL code to a different programming language.
Concerns have been raised regarding the technical issues that might arise from attempting to rewrite the entire code base quickly.
The SSA's software contains over 60 million lines of COBOL code, and attempts to move away from it swiftly could lead to payment calculation errors or non-issuance.

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Towards Data Science

192

Master the 3D Reconstruction Process: A Step-by-Step Guide

Understanding the 3D Reconstruction process is essential for creating high-quality reconstructions, which involves structured steps building spatial information from flat images.
Successful 3D reconstructions rely on pipelines that work efficiently, spend less time processing, and allow for faster troubleshooting.
The article provides a step-by-step guide covering the complete 3D Reconstruction Workflow, starting with Natural Feature Extraction using feature extraction algorithms like SIFT, SURF, and ORB.
Feature Matching connects images by finding correspondences between matched points, visualizing spatial relationships and helping assess image quality and camera positioning.
Structure From Motion (SfM) reconstructs 3D scene structure and camera motion, estimating camera poses and triangulating 3D points to create a sparse point cloud.
Bundle Adjustment optimizes reconstruction accuracy by refining camera parameters and 3D point positions, ensuring global consistency of the model.
Dense Matching creates detailed reconstructions through Multi-View Stereo, Patch-based Multi-View Stereo, or Semi-Global Matching, followed by tools and practical automation examples for efficient processing.
Understanding the full pipeline empowers users to troubleshoot issues effectively, target problem areas, and ensure high-quality 3D reconstructions.
Practicing and automating the process with high-quality images and gradually tackling more complex scenes is recommended to enhance expertise in 3D reconstruction.
Provided references and resources include notable software tools like COLMAP, OpenMVG, Meshroom, and RealityCapture, along with algorithms such as SIFT, SURF, ORB, RANSAC, and more.
The author, Florent Poux, Ph.D., emphasizes educating engineers on AI and 3D Data Science, leading research teams and teaching 3D Computer Vision to tackle 3D challenges and drive innovations.

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Towards Data Science

393

From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC

Hamiltonian mechanics provides a framework based on energy to describe physical systems, offering insights into conservation laws and symmetries.
By using generalized coordinates and momenta, Hamiltonian mechanics simplifies complex system dynamics, aiding in finding patterns.
The framework transforms Newtonian second-order equations into two first-order equations, revealing hidden geometric structures in phase space.
Hamilton's equations govern the evolution of systems in phase space, providing a geometric understanding of their behavior and symmetries.
Symplecticity, a sacred invariant in Hamiltonian systems, ensures the preservation of area in phase space under Hamiltonian flows.
Symplectic numerical integrators like Leapfrog Verlet maintain phase space volume conservation and are crucial for stable simulations in Hamiltonian systems.
Hamiltonian Monte Carlo (HMC) leverages Hamiltonian dynamics to sample efficiently from complex probability distributions, useful in Bayesian statistics and Machine Learning.
HMC bridges physics and ML by interpreting distributions in phase space and using Hamiltonian principles for exploration and sampling.
Using symplecticity in machine learning models, exploring HMC for complex distributions, and combining Hamiltonian mechanics with ML techniques are potential research directions.
Hamiltonian GANs represent a potential application of Hamiltonian formalism for generating physically plausible videos with neural networks.
Hamiltonian mechanics offers stability, energy conservation, and efficiency for simulations and sampling in complex systems, essential for various scientific and ML applications.

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Tech Radar

377

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Why Bill Gates is wrong about AI and 3 things he needs to realize

Bill Gates envisions a future driven by AI, where intelligence will be freely available and AI will replace most human roles within a decade.
However, there are limitations to current AI models, with issues like mistakes and lack of real understanding persisting.
Human factors such as emotional intelligence, body experience, and nuanced interactions are key elements AI struggles to replicate.
While AI can assist in tasks like logistics and data analysis, the complexity of human needs and behaviors remains a challenge for complete automation.
Even if AI could match or surpass human performance, many people value the human touch and connection in professions like medicine and education.
Gates' vision of AI as a universal assistant that enhances human capabilities is promising, but the idea of AI doing everything oversimplifies the technology and human nature.
The transformation brought by AI will be gradual, messy, and met with human resistance, as people appreciate and trust the unique qualities of human professionals.

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Medium

144

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Is Your Life TypeScript or JavaScript ?

Life can be compared to TypeScript or JavaScript. During exam week, life is fully typed, accepting only arguments from notes and oneself. On holidays, life is on 'type any', being unpredictable and open to different possibilities. Fasting can be seen as a strictly typed type, leaving no room for exceptions. Some people approach life like a strictly typed function, needing every detail pinned down, while others embrace spontaneity and live on 'type any'. Our approach to life reflects our approach to code, as we choose between the safety of defined boundaries or the freedom of spontaneity.
When referring to their approach to life, the author mentions that their preference is JavaScript, indicating a more spontaneous and flexible approach.

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