In this tutorial, we build a full PDF-to-structured-data workflow around Lift, built for controlled evaluation rather than a one-off demo. We prepare a Colab GPU environment, load Lift in 4-bit NF4, and generate synthetic research reports with deliberate distractors. We then run schema-guided extraction, score every field against ground truth, and assemble the results into a queryable knowledge base. The result is a repeatable extraction benchmark, not just raw model outputs.
The post Using Lift to Turn Research PDFs into Structured JSON with Controlled, Schema-Guided Field-Level Evaluation appeared first on MarkTechPost.
In this tutorial, we build a RAG-Anything workflow to explore how multimodal retrieval works across text, tables, equations, and images. We prepare a Colab environment, enter our OpenAI API key at runtime, and generate a synthetic report with a chart and PDF. We convert that content into RAG-Anything's direct content_list format and insert it into the retrieval system. We then configure OpenAI chat, vision, and embedding functions and test naive, local, global, and hybrid modes.
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In this tutorial, we explore CUP, Baidu's Common Useful Python library, as a practical utility toolkit for stronger Python workflows. We install it in a Colab-friendly environment and walk its subsystems step by step. We cover logging, decorators, nested configuration, caching, ID generation, thread pools, scheduling, and Linux resource monitoring. Along the way, we connect each module to real tasks like automation, concurrency, and reliability checks.
The post CUP (Common Useful Python): Building Reliable Python Workflows with Baidu’s Utility Toolkit appeared first on MarkTechPost.
We build a Colab-ready PyGraphistry workflow for interactive graph analytics on enterprise access data. We generate a synthetic dataset of users, devices, IPs, services, roles, and geos, then convert it into nodes and edges. We enrich the graph with risk scores, centrality metrics, community detection, Isolation Forest anomaly scores, and UMAP layout embeddings. We then bind the graph in PyGraphistry and produce local PyVis visualizations for full, ego, and high-risk views.
The post PyGraphistry Implementation Workflow for Interactive Graph Intelligence Pipelines in Security Analytics and Risk Investigation appeared first on MarkTechPost.
In this tutorial, we build a stable workflow around the Fable 5 Traces dataset from Hugging Face. We avoid fragile dependencies and manually parse the merged JSONL file to keep Colab reliable. We inspect repository files, normalize tool calls, audit structure, redact secrets, and visualize key distributions. We also export safe no-CoT chat datasets and train pure-Python Naive Bayes baselines on the traces.
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Datalab released lift, a 9B open-weights vision model that turns PDFs and images into schema-matching JSON. It uses schema-constrained decoding for valid structure and trained abstention to return null instead of hallucinating absent fields, scoring 90.2% field accuracy on a 225-document benchmark.
The post Datalab Releases lift: A 9B Open-Weights Vision Model That Extracts Structured JSON From PDFs Using Schemas appeared first on MarkTechPost.
Marc Isaacs’ film Synthetic Sincerity may look like a documentary, but its fictional premise – a lab that scrapes movies to harvest human emotions – shines a hard light on just how far AI can go
In Marc Isaacs’ latest film, the subversive documentary maker reveals that an AI research laboratory recently licensed his entire body of work. That’s a quarter-century of droll, deadpan studies of ordinary life in Britain – from the poetic Lift, about the comings and goings in a London tower block, and The Curious World of Frinton-on-Sea, set in the sleepy retirement town dubbed “God’s waiting room”, to Philip and His Seven Wives, in which a secondhand furniture dealer declares himself to be a Hebrew king. Isaacs agreed to let data analysts at the University of Southern England feed these and other documentaries into their system to harvest authentic human emotions from which AI characters could then be created. His film about the experience takes its name from the university’s lab: Synthetic
In this tutorial, we build a workflow that uses Docling Parse to analyze PDF documents at a detailed structural level. We prepare a stable Python environment, handle common Colab dependency issues, and generate a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then extract words, characters, and lines with page-level coordinates, render visual overlays, and save results into structured JSON and CSV. We see how low-level parsing supports layout analysis, reading-order reconstruction, and retrieval-ready document preparation.
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In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the […]
The post How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing appeared first on MarkTechPost.
