Crawlee for Python: Build a Web Crawling Pipeline with Robots Handling, Link Graphs, and RAG Chunk Export

In this tutorial, we build a Prefab application that creates interactive dashboards entirely in Python. We design an operations dashboard with reactive state, charts, tables, filters, forms, tabs, and metrics. We generate synthetic pipeline monitoring data and connect it to live UI controls. We then export the app as static HTML and preview it directly inside Google Colab. The post How to Design Python-First Interactive Dashboards with Prefab Reactive UI Components and Static HTML Export appeared first on MarkTechPost.

LLMs are stateless by default. Agent memory fixes that. This guide breaks down all 7 types — working, semantic, episodic, procedural, retrieval, parametric, and prospective. It covers what each stores, where it lives, and when to build it. Includes a comparison table and working Python code. The post The 7 Types of Agent Memory: A Technical Guide for AI Engineers appeared first on MarkTechPost.

Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #5sexies] - image_df tells you where every picture is. Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science.

SpatialClaw is a training-free agent that writes Python in a persistent kernel, composing perception tools for 3D spatial reasoning The post NVIDIA AI Introduce SpatialClaw: A Training-Free Agent That Treats Code as the Action Interface for Spatial Reasoning appeared first on MarkTechPost.

Master these tips, and your dictionary code will become shorter, safer, and easier to read.

For many developers, the hard part of building an AI application isn’t the model anymore. It’s keeping the application’s knowledge current. Retrieval-augmented generation (RAG) has become a popular technique for grounding AI applications in enterprise data, but it also introduces a steady stream of operational work, including tasks such as updating embeddings and indexes, synchronizing data sources, and tuning retrieval performance. AWS is seeking to remove much of that burden with Bedrock Managed Knowledge Base, a new managed service that automates the retrieval layer behind enterprise AI applications. “By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself,” Daniel Abib, senior solutions architect at AWS, wrote in a blog post. In order to help maintain data pipelines without building and managing custom integrations

Meta’s long-awaited Pyrefly linter is out in a 1.0 version, and the forthcoming Python 3.15 has a super-efficient sampling profiler. Plus we have a comprehensive rundown of Python’s indispensable virtual environments — and a warning about a novel breed of malware that exploits Python’s package ecosystem. Top picks for Python readers on InfoWorld How to use virtual environments in Python Isolate and protect your Python projects from each other, and empower them to do more, with virtual environments and their native-to-Python tooling. Pyrefly 1.0: A fast, forward-looking Python linter The first full release of Meta’s long-awaited linting and type checking tool for Python delivers speed and offers advanced features for type-checking PyTorch and Django projects. Hands-on with the new sampling profiler in Python 3.15 Among Python 3.15’s best new features is a sampling profiler, for instrumenting your code and finding its bottlenecks with a minimum of performance impact or fuss. See up-close