Why AI-Native IDP Platforms Outperform ABBYY and Kofax in Modern Document Workflows

Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design. But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer. This piece explores those three skills and why developers who master them will have a significant edge over those who don’t. Beyond coding: Mastering the art of the prompt Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent

A step-by-step road map for building the enterprise architecture required to deploy AI safely, quickly, and at scale.

Traditional data centers only stored, retrieved and processed data. In the generative and agentic AI era, these facilities have evolved into AI token factories. With AI inference becoming their primary workload, their primary output is intelligence manufactured in the form of tokens.  This transformation demands a corresponding shift in how the economics of AI infrastructure, […]

Deployment is not just about calling an API or hosting a model. It involves decisions around architecture, cost, latency, safety, and monitoring.

In an age of constrained compute, learn how to optimize GPU efficiency through understanding architecture, bottlenecks, and fixes ranging from simple PyTorch commands to custom kernels. The post A Guide to Understanding GPUs and Maximizing GPU Utilization appeared first on Towards Data Science.

The evolution of artificial intelligence from stateless models to autonomous, goal-driven agents depends heavily on advanced memory architectures. While Large Language Models (LLMs) possess strong reasoning abilities and vast embedded knowledge, they lack persistent memory, making them unable to retain past interactions or adapt over time. This limitation leads to repeated context injection, increasing token […] The post Architecture and Orchestration of Memory Systems in AI Agents appeared first on Analytics Vidhya.