LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships

In this article, you'll learn how to use the Claude API in Python, make your first request, and handle responses with the official SDK.

As organizations rush to move AI into production, they’re finding that the tools they rely on to monitor traditional software don’t translate cleanly to AI systems. The reason is fundamental: AI doesn’t fail as software does. It doesn’t throw clean error codes or follow predictable execution paths. It drifts, hallucinates, and degrades in ways that are often subtle, intermittent, and hard to reproduce. The result is a growing gap between what teams think observability should provide and what current tools actually deliver. The uncomfortable truth? The AI observability tools we have today are built for yesterday’s problems. To understand where the industry is headed, we need to look at where it is today and why that’s not enough. AI observability today: The era of evals Today’s AI observability landscape is dominated by one concept: evaluation. Most tools focus on scoring model outputs after the fact. They rely on test datasets, human graders, or, increasingly, “LLM-as-a-judge” approach

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.

Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets.

In this tutorial, we build a complete, self-contained OCRmyPDF pipeline in Python. We generate synthetic image-only PDFs so we can test OCR without external files, then convert them into searchable PDFs and PDF/A outputs. We extract sidecar text, validate results, measure word-recall, and compare file sizes. We also tune Tesseract, clean noisy scans, correct orientation, run OCR in memory, and batch-process whole folders. The post OCRmyPDF Tutorial: Convert Scanned Documents into Searchable PDF/A Files with Sidecar Text Extraction and Batch Processing appeared first on MarkTechPost.

Microsoft is continuing its push to bring generative AI (genAI) into Excel, with new Microsoft 365 Copilot skills designed to automate common processes and a “plan” mode to provide more control over Copilot’s outputs when handling financial data. Microsoft made Microsoft 365 Copilot generally available in Excel in late 2024 and since then has added several capabilities, including agentic tools, a Copilot function within Excel, and Python support for advanced data analysis.   On Thursday, Microsoft unveiled a skills feature that lets users define processes Copilot can perform in Excel — such as building a discounted cash flow, Microsoft suggested, preparing a variance analysis, or refreshing a monthly reporting model.   “Instead of starting from scratch each time, a skill guides Copilot through the steps, applying the right structure and formatting, and helping produce an output that is easier to review, reuse, and trust,” Brian Jones, vice president for Excel at Microsoft, said in a bl

SpatialClaw is NVIDIA Research’s latest AI framework that enables agents to write, execute, and refine their own reasoning through executable Python code rather than relying on predefined tool calls. The approach delivers significant gains in spatial intelligence across complex 3D and 4D tasks without requiring additional training.

In this tutorial, we build a fully offline Graphify pipeline that turns a multi-module Python application into a knowledge graph. We install Graphify, generate a connected sample app, and extract the graph locally using tree-sitter, with no API key or LLM backend. We load graph.json into NetworkX and analyze file types, relationship types, centrality scores, community detection, and shortest paths. We then create static and interactive visualizations to see how modules, classes, functions, and database objects connect. The post Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations appeared first on MarkTechPost.