towards data science
towards data science on Beyond Market Intelligence: a running collection of 125 stories we have gathered and hand-picked because they are worth your time. Every post here touches on towards data science in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around towards data science, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
Explore a novel approach to data processing with "Running SQL Concurrently Across Three Remote DuckDB Servers with Quack." This experiment demonstrates a practical application of remote SQL execution, empowering users to leverage distributed resources for enhanced performance. Discover how Quack facilitates this process, offering a streamlined solution for complex queries. For those interested in building applications that accumulate understanding, consider "Designing a Persistent Knowledge Layer That Refuses to Guess," which details a vendor-neutral blueprint for RAG systems.
How to Shine as a Data Scientist in the Vibe Coding Era
The rise of AI coding tools like those explored in "How to Install Codex CLI" signals a significant shift for data scientists. Coding proficiency is increasingly becoming a commodity; the future belongs to those who leverage these tools strategically. This post outlines how to thrive in this "Vibe Coding Era," focusing on higher-level skills like problem framing, insightful analysis, and communicating data-driven narratives. Discover how to evolve beyond coding and become the indispensable data scientist of tomorrow.
Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming
The landscape of mathematical experimentation is rapidly evolving, driven by the power of human-machine collaboration. Recent breakthroughs demonstrate this potential: two significant open problems—exact-arithmetic checking and the development of a proof assistant—were tackled and advanced over a single weekend through this synergistic approach. This signals a future where AI tools significantly accelerate research. For those seeking to leverage AI assistance directly, explore "How to Install Codex CLI: A Step-by-Step Guide" to begin your journey.
My Model Was Cheating on Its Own Test
Data scientists often strive for model accuracy, but what happens when a model gains an unfair advantage? In a recent *Towards Data Science* post, an author discovered their car price prediction model was "cheating" – a preprocessing pipeline inadvertently allowed it to glimpse the test set. This resulted in a deceptively high R-squared score. The experience highlights a critical pitfall in machine learning workflows and the importance of rigorous validation.
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Unlock the next level of Retrieval-Augmented Generation (RAG) with our latest exploration of Loop Engineering and the Dispatcher pattern. Enterprise Document Intelligence, Vol. 1 #13, details a crucial advancement: intelligently controlling when to loop and when to stop within a RAG workflow. This approach defines what “agentic RAG” *should* look like, moving beyond simplistic iterations. Discover how this architecture puts patterns together for more efficient and reliable results.
A Day in the Life of a Data Scientist in 2026
The role of the data scientist is undergoing a profound transformation. In "A Day in the Life of a Data Scientist in 2026," we explore how AI has fundamentally reshaped daily workflows, moving beyond traditional spreadsheet limitations. Discover how automation, intelligent insights, and streamlined model deployment now define the modern data scientist's experience. This post offers a future-focused perspective on leveraging AI to empower data-driven decision-making—a shift that's already underway, as highlighted by innovations like Kog’s work to optimize GPU inference for agentic workflows.
I Made an LLM Lay Siege to My Minecraft House
Can a language model actively design a challenging Minecraft level? We put it to the test, tasking an LLM with laying siege to a player-built house – a compelling experiment in adversarial level design. The results are surprisingly dynamic and reveal the potential for AI to generate complex, reactive environments. Explore the full story and see how this experiment unfolded. For further insights into AI agents, consider "5 Fun Agentic AI Papers to Read," offering a curated selection of foundational research.
LangChain vs LangGraph: 4 Key Differences and When to Use Each
Navigating agentic workflows demands the right tools. LangChain and LangGraph are both vital for building AI systems, but understanding their differences is key to optimal performance. This guide delivers a practical comparison, outlining 4 key distinctions to empower your decision-making. Discover when to leverage LangChain’s versatility versus LangGraph’s focused approach to graph-based agent design. For deeper insights into knowledge exchange within LLMs, explore "How to Utilize OKF Efficiently."
Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model
Enterprise RAG pipelines often introduce unnecessary latency by repeatedly calling Large Language Models (LLMs). Article 9 explores a practical solution: strategically bypassing the LLM for straightforward queries. By implementing a simple keyword-based routing signal, organizations can achieve significant reductions in both latency—approximately two seconds per question—and operational costs. This approach demonstrates that optimizing LLM usage, not simply upgrading models, is key to efficient Enterprise Document Intelligence. Discover further insights into knowledge exchange with "How to Utilize OKF Efficiently."
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Unlock seamless knowledge exchange between AI agents with Google’s Open Knowledge Format (OKF). This post demonstrates a practical application—facilitating efficient data transfer between three Qwen2.5-Coder models—achieving a significant 28–37% reduction in time-to-first-token (TTFT) and ensuring data integrity through full-vocabulary equivalence checks. Explore how OKF's Markdown+YAML structure empowers streamlined agent collaboration. For further insights into optimizing AI agent costs, consider "Writer says its new Palmyra X6 model cuts AI agent costs by 52%."
Building Multimodal Workflows with a Local LLM
Unlock new possibilities in data processing by building multimodal workflows directly on your machine. This post explores leveraging Gemma 4 and Ollama to create powerful systems capable of accepting image inputs and generating structured outputs – a significant step beyond traditional spreadsheet limitations. Discover how local LLMs empower accessible and future-focused data manipulation. For a foundational understanding of the underlying mechanics, explore "Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works," to deepen your knowledge of the neural networks at play.
Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works
## Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works Ready to understand the core of neural network training? This post dives into how backpropagation truly functions, moving beyond the initial concept to explore the cascade of gradients. We'll break down the process of calculating gradients from a single point to every parameter, illuminating how this iterative refinement shapes model learning. For a deeper dive into the broader context of data intelligence and decision-making, see "Before Full Agentic RAG.
Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From
Unlock the full potential of Retrieval-Augmented Generation (RAG) with a critical first step: understanding your data selection process. Our latest post, "Before Full Agentic RAG," details a dispatcher system that intelligently analyzes each PDF's nature—its *type*—and selects the optimal parsing method from a range of options, including fitz, Docling, and PaddleOCR. This structured approach, closing a key "brick" in the AI data pipeline, ensures higher accuracy and efficiency.
Can a Local LLM Run My AI Assistant?
Can a local Large Language Model (LLM) truly replace cloud-based AI assistants like Claude? We put that question to the test, replaying 27 real-world production tasks through two local models, differentiated by hardware. Our findings reveal a practical roadmap for achieving this transformation, detailing the necessary infrastructure and performance benchmarks. Discover what it *actually* takes to bring AI assistance home. For further insights on optimizing AI workflows, explore our analysis of Polars versus Pandas.
Should AI Developers Make the Switch from Polars to Pandas?
Not all Python data libraries offer equal performance for AI development. Polars and Pandas are both popular choices, but their architectures differ significantly. This post explores whether AI developers should consider transitioning from Pandas to Polars, particularly given Polars’ optimized query engine and memory efficiency. Discover how these factors impact speed and scalability in modern data workflows. For deeper insights into agentic AI applications, see our recent article, "We built the Agentic World Cup - LLMs that compete in 1v1 Soccer [P]."
Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick
Delve into Variational Autoencoders (VAEs), a powerful generative modeling technique, with our comprehensive, math-first walkthrough. This post systematically explores VAE theory, from the core concepts to the crucial Evidence Lower Bound (ELBO) and the reparameterization trick—essential for enabling efficient training. Understand how VAEs learn to generate new data by mastering these key components. For those seeking to build robust data infrastructure for AI agents, consider our related article, "Building an Agent-Ready Data Warehouse," which highlights common architectural pitfalls.
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
For six decades, the data warehousing industry has prioritized storage and structure. However, simply granting an AI agent access to this data doesn't equate to readiness. The core challenge lies in equipping the agent with the contextual understanding to interpret data meaning and assess its reliability. Traditional architectures fall short here. Explore how to bridge this gap and unlock the true potential of agentic data access—discover a future-focused approach to building truly agent-ready data warehouses.
How to Effectively Deploy Code With Claude Code
Optimizing your CI/CD pipeline for coding agents like Claude Code is critical for efficient development workflows. This post details proven strategies for effective code deployment, moving beyond traditional methods to leverage the power of AI-assisted coding. Discover practical techniques to streamline your processes and maximize productivity. If you're seeking a deeper understanding of foundational concepts, consider “I never understood positional encoding until I read this article,” for valuable insights into related AI principles.
SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint
Spatial Pyramid Pooling (SPP-Net) fundamentally transformed Convolutional Neural Networks (CNNs) by dismantling the fixed-size image constraint. This walkthrough provides a clear, accessible exploration of the SPP-Net paper, detailing how this innovative technique enables CNNs to process images of any dimension. We’ve built a from-scratch PyTorch implementation to illustrate the core concepts. Discover how SPP-Net unlocks greater flexibility in image analysis—a concept closely related to generative models; for a deeper dive into generative techniques, explore our explanation of Variational Autoencoders (VAEs).
How to Implement Structured Output with Local LLMs
Unlock the power of local Large Language Models (LLMs) with structured output – a critical technique for reliable data extraction and automation. This post explores why structured output is essential, detailing implementation strategies and addressing potential failure scenarios. Gain clarity on how to transform LLM responses into predictable, usable formats, empowering more robust applications. Learn how to troubleshoot common issues and maintain system integrity.
I Thought Loading Data Was the Finish Line. It Was the Starting Point.
Many believe data loading marks the end of a project, but it’s often just the beginning. My recent journey building dbt models illuminated the true meaning of "analysis-ready" data—a concept far beyond simply moving data from point A to point B. Discovering this shift transformed my approach to data management, emphasizing the importance of structured, reliable datasets. If you’re exploring the nuances of data transformation, consider "Before Q, K, and V: Reconstructing the Transformer" for a deeper look at foundational architecture.
Building a Streamlit UI for My LangGraph AI Agent
Developing a production-ready web interface for your LangGraph AI agent is a crucial step towards practical application. This post details building a Streamlit UI, offering a straightforward path to visualizing and interacting with stateful LangGraph agents. We’ll explore techniques to create an accessible and functional interface, empowering users to leverage the full potential of your AI workflows. For a deeper understanding of the underlying architecture powering these advancements, consider "Before Q, K, and V: Reconstructing the Transformer."
Before Q, K, and V: Reconstructing the Transformer
Many Transformer explainers begin by detailing the final architecture, but we believe understanding *why* it looks the way it does is crucial. This post, "Before Q, K, and V: Reconstructing the Transformer," delves into the foundational reasoning behind this pivotal AI architecture. We reverse-engineer the design process, revealing the motivations and incremental steps that led to the familiar components. For those interested in a broader perspective on data exploration tools, see our comparison of Matplotlib and Plotly.
Matplotlib vs Plotly: Which Python Chart Tool Should You Choose?
Navigating the world of Python charting can feel overwhelming. Choosing between Matplotlib and Plotly depends on your goals. Matplotlib remains a reliable choice for generating static, publication-ready plots—a foundational tool for many data scientists. However, Plotly excels in interactive data exploration, enabling dynamic visualizations and user engagement. For those seeking to build data agents and conversational interfaces, as explored in "I Built an AI Data Agent," Plotly’s interactivity offers a significant advantage. Discover which tool best empowers your data journey.