--- title: Best Towards AI posts — January 2025 | daily.dev description: The most upvoted Towards AI posts from January 2025, curated by the daily.dev community. canonical: https://daily.dev/sources/tai/best-of/2025/01 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/tai/best-of/2025/01 og:type: website og:site_name: daily.dev og:title: Best Towards AI posts — January 2025 | daily.dev og:description: The most upvoted Towards AI posts from January 2025, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Towards AI — January 2025 1. 1 [](https://daily.dev/posts/lets-build-simple-rag-application-ydxlddczb "Lets Build Simple RAG Application") Article Towards AI · 2y Lets Build Simple RAG Application Large Language Models (LLMs) have significantly advanced technology interactions but possess limitations like the inability to access real-time information, affecting applications requiring current data. Enhancements using techniques like in-context learning are discussed, particularly for building effective RAG applications using Langchain. 110 1 2. 2 [](https://daily.dev/posts/mastering-generative-ai-architectural-patterns-a-comprehensive-guide-6tjmepzio "Mastering Generative AI Architectural Patterns: A Comprehensive Guide") Article Towards AI · 2y Mastering Generative AI Architectural Patterns: A Comprehensive Guide Generative AI has transformed various industries by creating new content such as text, images, music, and video. This comprehensive guide explores different generative AI architectural patterns like GANs, VAEs, autoregressive models, diffusion models, and emerging hybrid approaches, detailing their functionalities, popular models, and real-world applications. 31 3. 3 [](https://daily.dev/posts/data-scientists-in-the-age-of-ai-agents-and-automl-c8z3iuhu1 "Data Scientists in the Age of AI Agents and AutoML") Article Towards AI · 2y Data Scientists in the Age of AI Agents and AutoML The role of data scientists is transforming with the advent of AI agents, AutoML, and pre-trained models. Traditional skills like Python scripting and model building are no longer sufficient. Modern data scientists need to focus on end-to-end solutions, understanding the entire data lifecycle, cloud platforms, CI/CD practices, and possess strong business acumen. Mastery of tools like Docker, Kubernetes, and major cloud services is essential. The emphasis is shifting from coding to integrating models into scalable, business-critical systems. 29 1 4. 4 [](https://daily.dev/posts/building-graph-rag-for-structured-and-unstructured-data--sjd1qopbn "Building Graph RAG for structured and unstructured data.") Article Towards AI · 2y Building Graph RAG for structured and unstructured data. RAG (Retriever-Augmented Generation) architecture helps solve the issue of missing contextualization in LLMs (Large Language Models) without the need for fine-tuning. While Vector RAGs offer some contextualization, graph-based RAGs capture more intricate relationships, making them more effective. This post discusses how to build knowledge graphs from both unstructured data (like PDFs) and structured data (like CSVs) using tools such as Langchain and Neo4j. It also outlines steps for extracting text, chunking documents, constructing graphs, and querying the graph databases using LLMs. 29 5. 5 [](https://daily.dev/posts/pytorch-vs-pytorch-lightning-a-practical-exploration-ciotovrl7 "PyTorch vs PyTorch Lightning: A Practical Exploration") Article Towards AI · 2y PyTorch vs PyTorch Lightning: A Practical Exploration PyTorch is a popular framework for deep learning, known for its dynamic computational graph, flexibility, and extensive community support, but requires writing a lot of boilerplate code. PyTorch Lightning is a high-level interface built on top of PyTorch that automates many low-level details like training loops, logging, and distributed learning, making it ideal for production and team projects. Lightning enhances code readability, reproducibility, and speeds up development while preserving PyTorch’s flexibility. 19 1 6. 6 [](https://daily.dev/posts/my-6-secret-tips-for-getting-an-ml-job-in-2025-z6mjag2l4 "My 6 Secret Tips for Getting an ML Job in 2025") Article Towards AI · 2y My 6 Secret Tips for Getting an ML Job in 2025 Landing a machine learning job in 2025 can be challenging but knowing certain 'secret' tips can help. The author shares key strategies, such as demonstrating skills through personal projects and identifying opportunities for improvement in existing code, to stand out to potential employers. 18 7. 7 [](https://daily.dev/posts/best-laptop-for-data-science-yszrvveiw "Best Laptop For Data Science") Article Towards AI · 2y Best Laptop For Data Science Choosing the right laptop for data science is not as critical as it may seem; the best one is the one you have. However, some laptops offer advantages. Explore the best options for data science work across the three main operating systems: Windows, MacOS, and Linux, considering their unique features and user experiences. 17 1 8. 8 [](https://daily.dev/posts/scaling-llm-experimentation-with-sagemaker-pipelines-and-mlflow-bxd2qrrdk "Scaling LLM Experimentation with SageMaker Pipelines and MLflow") Article Towards AI · 2y Scaling LLM Experimentation with SageMaker Pipelines and MLflow Large language models (LLMs) are revolutionizing NLP tasks across various industries and often need customization for specific domains. Amazon SageMaker and MLflow offer scalable solutions for fine-tuning and evaluating these models. The post explains how to use SageMaker JumpStart and SageMaker Clarify to evaluate models, and SageMaker Pipelines for comparison. Additionally, it covers using MLflow to track training and evaluation data, and employing Parameter-Efficient Fine-Tuning (PEFT) using the transformers library for customization. 16 9. 9 [](https://daily.dev/posts/i-switched-from-windows-to-linux-for-1-month-here-is-what-happened-v0wxyienz "I Switched From Windows To Linux For 1 Month — Here Is What Happened") Article Towards AI · 2y I Switched From Windows To Linux For 1 Month — Here Is What Happened When the author's Windows PC broke, they had to rely on a Linux laptop for all their computing needs for a month. They used Pop!\_OS for various tasks including browsing, gaming, studying, machine learning, and development. The post explores whether Linux can fully replace Windows, especially from a data scientist's perspective, by discussing the benefits, challenges, and differences experienced during this period. 15 10. 10 [](https://daily.dev/posts/ai-mathematicians-how-llms-are-redefining-mathematics-1fvfy4cgn "AI Mathematicians: How LLMs Are Redefining Mathematics") Article Towards AI · 2y AI Mathematicians: How LLMs Are Redefining Mathematics Large Language Models (LLMs) like ChatGPT are revolutionizing mathematics by processing vast amounts of data quickly, uncovering unseen patterns, and proposing novel proofs and algorithms. They aid in prime number analysis, provide insights into the Riemann Zeta function, simulate fluid dynamics, solve symbolic mathematics problems, optimize heat transfer designs, and enhance machine learning through mathematical insights. By accelerating mathematical discoveries and expanding research horizons, LLMs are redefining the future of mathematical exploration. 15 11. 11 [](https://daily.dev/posts/fine-tuning-embeddings-for-rag-applications-isyvbpn4g "Fine-tuning Embeddings for RAG applications") Article Towards AI · 2y Fine-tuning Embeddings for RAG applications Fine-tuning embeddings can significantly improve the accuracy and relevance of Retrieval-Augmented Generation (RAG) applications. This involves pre-training embeddings to align closely with the types of questions users might ask, optimizing for better performance in real-world scenarios. This approach is validated by experimental results showing enhanced retrieval accuracy. Code repositories and methods for fine-tuning, such as TripletMarginLoss and CosineEmbeddingLoss, are provided for further experimentation. 14 12. 12 [](https://daily.dev/posts/introducing-deepseek-artifacts-ucwny0sqd "Introducing Deepseek Artifacts") Article Towards AI · 2y Introducing Deepseek Artifacts Deepseek Artifacts is an online tool that leverages the Deepseek V3 LLM model to create, run, and share React and Tailwind web applications based on user prompts. Users can view and edit the source code, which updates the preview in real time. The app can also be accessed in a VSCODE-like sandbox environment. 13 13. 13 [](https://daily.dev/posts/scikit-learn-from-a-to-z-the-complete-guide-to-mastering-machine-learning-in-python-ym71xvlel "Scikit-learn from A to Z: The Complete Guide to Mastering Machine Learning in Python") Article Towards AI · 2y Scikit-learn from A to Z: The Complete Guide to Mastering Machine Learning in Python Scikit-learn is an open-source machine learning library built on Python, designed to handle various ML tasks such as supervised learning, unsupervised learning, model evaluation, and data preprocessing. This guide explores its features, offering insights into how it can be used from basic model creation to efficient deployment. 10 [See all Towards AI archives](/sources/tai/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/tai/best-of/2025/01#page","url":"https://daily.dev/sources/tai/best-of/2025/01","name":"Best Towards AI Posts — January 2025","description":"The most upvoted Towards AI posts from January 2025, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/tai/best-of/2025/01#items","numberOfItems":13,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/lets-build-simple-rag-application-ydxlddczb","name":"Lets Build Simple RAG Application"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/mastering-generative-ai-architectural-patterns-a-comprehensive-guide-6tjmepzio","name":"Mastering Generative AI Architectural Patterns: A Comprehensive Guide"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/data-scientists-in-the-age-of-ai-agents-and-automl-c8z3iuhu1","name":"Data Scientists in the Age of AI Agents and AutoML"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/building-graph-rag-for-structured-and-unstructured-data--sjd1qopbn","name":"Building Graph RAG for structured and unstructured data."},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/pytorch-vs-pytorch-lightning-a-practical-exploration-ciotovrl7","name":"PyTorch vs PyTorch Lightning: A Practical Exploration"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/my-6-secret-tips-for-getting-an-ml-job-in-2025-z6mjag2l4","name":"My 6 Secret Tips for Getting an ML Job in 2025"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/best-laptop-for-data-science-yszrvveiw","name":"Best Laptop For Data Science"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/scaling-llm-experimentation-with-sagemaker-pipelines-and-mlflow-bxd2qrrdk","name":"Scaling LLM Experimentation with SageMaker Pipelines and MLflow"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/i-switched-from-windows-to-linux-for-1-month-here-is-what-happened-v0wxyienz","name":"I Switched From Windows To Linux For 1 Month — Here Is What Happened"},{"@type":"ListItem","position":10,"url":"https://daily.dev/posts/ai-mathematicians-how-llms-are-redefining-mathematics-1fvfy4cgn","name":"AI Mathematicians: How LLMs Are Redefining Mathematics"},{"@type":"ListItem","position":11,"url":"https://daily.dev/posts/fine-tuning-embeddings-for-rag-applications-isyvbpn4g","name":"Fine-tuning Embeddings for RAG applications"},{"@type":"ListItem","position":12,"url":"https://daily.dev/posts/introducing-deepseek-artifacts-ucwny0sqd","name":"Introducing Deepseek Artifacts"},{"@type":"ListItem","position":13,"url":"https://daily.dev/posts/scikit-learn-from-a-to-z-the-complete-guide-to-mastering-machine-learning-in-python-ym71xvlel","name":"Scikit-learn from A to Z: The Complete Guide to Mastering Machine Learning in Python"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"Towards AI","item":"https://daily.dev/sources/tai"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/tai/best-of"},{"@type":"ListItem","position":5,"name":"January 2025"}]}]} ```