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.
Whether you are using an AI code generator, vibe coding, or applying spec-driven development methodologies, your job doesn’t end with AI writing the code. Whether you’re using AI to develop applications, APIs, data pipelines, AI agents, or other automations, writing the code is just one part of the job. Developers must still perform code validation, test applications, automate deployment, and configure infrastructure.
According to one survey, only 16% of a developer’s time is spent writing code. The remaining 84% is spent on other activities including defining requirements, triaging bugs, and addressing vulnerabilities.
Additionally, while AI code generation speeds up development, it can come at the cost of quality and collaboration. In Atlassian’s State of Teams 2026 survey, nearly 50% of respondents say their AI outputs aren’t reliably high quality and admit that using AI is a compromise between speed and quality. Knowledge workers say the pressure to execute is also problematic, wit
TKROBOTS is expanding its AI-powered crypto trading ecosystem, combining automation, analytics, and machine learning for digital asset investors. As artificial intelligence continues to transform global financial markets, TKROBOTS is positioning itself as an innovative AI-powered cryptocurrency trading platform focused on…
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.
Shift is paying cleaners to wear camera headsets inside customers’ homes, building the datasets that could shape the future of domestic work. Is the price for the future of robotics worth it?
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.
