python
python on Beyond Market Intelligence: a running collection of 43 stories we have gathered and hand-picked because they are worth your time. Every post here touches on python 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 python, 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.
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.
Turn Any CSV into an Executive Report with Python and AI
Transform raw CSV data into compelling executive reports with this practical Python and AI pipeline. Learn to automate data cleaning, uncover key insights, and generate clear, narrative summaries—all in a repeatable process. This empowers data-driven decision-making without manual effort. Discover a future-focused approach to data storytelling, moving beyond spreadsheets to unlock actionable intelligence. For those diving deeper into AI/ML project collaboration, consider the discussion started by /u/Economy_Cicada8756 on contributing to related projects.
The Medallion Data Architecture: An Introduction
Navigating modern data pipelines can feel complex, but the Medallion Data Architecture offers a clear, practical framework. This guide introduces the Bronze, Silver, and Gold layers—a proven approach to structuring data for reliability and analytical readiness. We’ll explore each tier with a working Python and DuckDB example, empowering you to build robust data workflows. For a deeper dive into related challenges in AI agent memory management, see "Asana's AI agents share memory across your company — but not your secrets."
Relevant tech stack for 2026/2027
As a data scientist transitioning to team leadership, future-proofing your tech stack is a smart move. By 2026/2027, expect a shift towards more robust data engineering practices and cloud-native solutions. Prioritize expanding beyond SQL and Python to include tools like Apache Spark for distributed processing and exploring cloud platforms like AWS or Azure for scalability. Familiarize yourself with orchestration tools like Airflow to automate workflows.
How to Build CLI Agents with Python & Ollama
Unlock the power of local AI with this practical guide to building Command Line Interface (CLI) agents using Python and Ollama. This tutorial empowers you to create custom agents from scratch, entirely free of charge. Explore the fundamentals of agent design and implementation, leveraging the efficiency of local LLMs. For a deeper dive into the engineering layers underpinning these systems, see our article, "Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On." Discover a future-focused approach to data interaction and automation.
I Replaced a 15-Minute Booking Process with a LangGraph AI Agent
Tired of cumbersome processes? In a recent Towards Data Science post, we detail how a 15-minute booking process was streamlined using a LangGraph AI agent. This practical guide walks you through building, running, and monitoring a stateful customer support agent with Python, LangGraph, and Langfuse. Discover a powerful alternative to traditional workflows and unlock new levels of efficiency.
LanceDB Vector Database Guide: Features, Python Demo
Large language models thrive on text, but struggle when data is fragmented across formats or sources. Modern AI increasingly relies on vector databases to efficiently store and retrieve information through similarity search. LanceDB emerges as a powerful vector database specifically engineered for AI workloads, offering native support for multimodal data—text, images, and more. Explore our comprehensive guide to LanceDB's features and a practical Python demo, and discover how it can transform your AI data management.
The Python Ecosystem That Changed AI Development
The rise of modern AI is inextricably linked to the Python ecosystem. This open-source environment fostered unprecedented accessibility, democratizing state-of-the-art techniques previously confined to research labs. Explore how Python's libraries – from NumPy and Pandas to TensorFlow and PyTorch – empowered a generation of developers and transformed AI development. Discover the collaborative spirit and rapid innovation that defined this shift, fundamentally reshaping the landscape of data science and machine learning. For a deeper dive into related challenges, see “Dili raises $21.
7 Machine Learning Algorithms That Still Matter
Before diving into the world of large language models and generative AI, ensure a solid foundation in core machine learning principles. Discover 7 essential algorithms – from linear regression to support vector machines – that remain vital for any data scientist. Each is explained simply, accompanied by practical Python code examples. Mastering these fundamentals empowers you to build robust, reliable models. For deeper insights into leveraging AI strategically, explore our article, "AI-Assisted Software Development: Team Profiles and Capabilities for Putting Research into Action."
Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search
Tackle complex pickup-and-delivery logistics with "Los Movimientos, Part II," a practical guide to solving large-scale routing problems. This post details the construction of an Adaptive Large Neighborhood Search (ALNS) heuristic in Python, addressing vehicle routing, time windows, capacity constraints, and essential driver breaks. We demonstrate a future-focused approach to optimization, empowering data scientists to build efficient solutions. For a broader perspective on leveraging AI within business contexts, explore "What Professionals Should Know About Data Science and AI" for essential considerations.
I built a compiler that turns computation graphs into the weights of a vanilla transformer — no training anywhere [P]
Explore a novel approach to transformer architecture with TorchWright, a compiler that generates transformer weights directly from Python computation graphs – eliminating the need for any training. This innovative system, detailed in a recent post on ood.dev, allows users to define algorithms independently of the learning process, producing standard Phi-3 checkpoints compatible with vanilla Hugging Face. See how this achieves expressiveness within a transformer, building upon work like RASP while prioritizing accessibility and a stock architecture.
How to Give an LLM Agent a Browser
Empower your LLM agents to navigate the web with confidence. This guide explores building a browser-enabled agent using OpenAI's Agents SDK and Playwright’s MCP, unlocking a new dimension of data access and automation. Discover how to equip your AI with the ability to interact with websites, extract information, and perform tasks previously beyond its reach. This approach moves beyond static datasets, enabling dynamic, real-time data processing. For further insights into AI agent capabilities, see "You Can Hand One AI Agent Your Worst Recurring Task.
Building an AI-text detector from scratch [P]
Delve into the intricacies of AI-native data detection with a practical tutorial from Ordinary Intelligence. This project, submitted by /u/gamedev-exe, guides you through building an AI-text detector from scratch—a valuable skill in navigating the evolving digital landscape. Explore the full tutorial and accompanying notebook on GitHub to empower your understanding of AI-driven analysis. For those interested in related explorations, consider the discussion around GPU-accelerated AI projects, highlighting the intersection of performance and learning.
Build an LLM Agent That Can Write and Run Code
Unlock the potential of AI-powered code generation and execution. This hands-on walkthrough guides you through building an LLM agent using the OpenAI Agents SDK and Docker. Learn to empower your workflows by seamlessly integrating code writing and running capabilities. We’ll demonstrate a practical approach to leveraging these tools, offering a future-focused solution for data professionals. For those interested in a deeper dive into LLM runtimes, explore "How To Build Your Own LLM Runtime From Scratch" for a comprehensive understanding of the underlying infrastructure.
KDnuggets Weekly Roundup: Week of July 13, 2026
This week’s KDnuggets Weekly Roundup delivers practical insights for data professionals. We're prioritizing efficiency, starting with a clear alternative to cumbersome if-else chains in Python – embrace the Registry Pattern. Level up your portfolio with five real-world SQL projects, stay current with ten top AI YouTube channels, and explore structured language model generation. For deeper exploration of related topics, consider "Pinecone Introduces Nexus Engine," now generally available, for compiling business context into structured data for AI agents.
Best current tools for Multi-Objective Surrogate-Based Optimization (MOSBO) on heterogeneous study data meta-analysis?[P]
Navigating Multi-Objective Surrogate-Based Optimization (MOSBO) on heterogeneous study data demands a robust workflow. For your project involving ~40 studies and continuous response surfaces, the strongest 2026 stack likely converges on PyMC for hierarchical modeling, coupled with pymoo and pysamoo for surrogate-assisted optimization. SMT provides solid surrogate options, while Matlab's Global Optimization Toolbox offers an alternative. Colab-friendly Python experience simplifies implementation. Explore resources like tutorials and applied examples to accelerate your progress—similar to the focused loop engineering discussed in "Context Engineering Isn’t Enough."
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
The conversation around loop engineering often centers on Large Language Models (LLMs), but can the architecture itself drive improved performance? This article presents a novel experiment, rigorously testing a deterministic, zero-dependency Python benchmark to isolate failures—without an LLM. Results across 300 random seeds demonstrate that goal-directed controllers consistently outperform linear pipelines in completing independent branches. Explore the architecture, benchmark, and debugging process, revealing that failure isolation is, in fact, a measurable property of control flow.
Stop Using If-Else Chains: Use the Registry Pattern in Python Instead
Stop wrestling with convoluted if-else chains in your Python code. The Registry Pattern offers a cleaner, more extensible approach to dispatching logic – a vital upgrade for maintainable projects. By centralizing your function mappings, you’ll streamline your codebase and simplify future modifications. Explore this pattern to unlock greater flexibility and readability. For broader insights into orchestrating complex workflows, see our article, "7 Python Frameworks for Orchestrating Local AI Agents," and discover tools engineers are using to build sophisticated agent systems.
7 Python Frameworks for Orchestrating Local AI Agents
As local AI agent development accelerates, engineers require robust orchestration frameworks. This article details seven Python tools actively employed in 2026 to build, coordinate, and run these agents on local infrastructure, providing a practical guide for implementation. These tools empower developers to manage complex agent interactions and resource utilization efficiently. For broader context on the evolving landscape, explore "Vint Cerf is working on a plan to unleash AI agents on the open internet," offering insights into the standardization efforts shaping the future of AI agency.