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Context Engineering: What Goes Into the Window Is the Whole Ballgame

Submitted by jordan on Tue, 08/18/2026 - 17:00

Context engineering vs. prompt engineering is not a pedantic distinction — it is the difference between an agent that reliably produces good work and one that reliably produces plausible-sounding garbage. Prompt engineering is about choosing words. Context engineering is about deciding, on every single model call inside an Agent Loop, exactly what information enters the context window and exactly what stays out. The first skill is useful. The second is the one that determines whether your agentic system ships.

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Benchmarking the Bleeding Edge: How We Score New AI Tools on a Real Test Bench

Submitted by jordan on Mon, 07/27/2026 - 17:00

A new model dropped this morning. By the time you read this, there is probably another one. Somewhere in your engineering org, a smart, well-intentioned developer has already swapped it into their workflow because the launch thread looked impressive and the demo was undeniable.

That is the problem.

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Mastering AI Deployment: Adapting the AWS Well-Architected Framework for Business Success

Submitted by jordan on Fri, 05/22/2026 - 07:04

In today's rapidly evolving technological landscape, artificial intelligence (AI) has become essential for businesses aiming to maintain a competitive edge. However, deploying AI solutions effectively requires a well-structured approach to ensure they are reliable, secure, efficient, and cost-effective. TheAWS Well-Architected Framework, renowned for guiding cloud-based architectures, provides a solid foundation adaptable for AI implementations. This article explores how businesses can tailor the principles of this framework to optimize their AI solutions.

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The Future of SaaS in the Age of AI Agents — What Survives, What Doesn't

Submitted by jordan on Wed, 04/08/2026 - 17:00

Every major platform vendor is racing to add AI features to their SaaS products. Copilots in your CRM. AI assistants in your project management tool. Chatbots bolted onto your help desk. They're polishing the deck chairs.

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Secrets Management for AI Development: Why Your Vault Wasn't Built for Agents

Submitted by jordan on Thu, 04/02/2026 - 17:00

Secrets management for AI development requires rethinking everything your vault takes for granted. Traditional tools handle microservices beautifully — rotating database credentials, issuing short-lived tokens, and keeping API keys out of source code. They were designed for a world where applications request the same credentials, the same way, every time.

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How to NOT Have Your Data Trained on by AI Across Leading LLMs

Submitted by peter on Thu, 11/27/2025 - 08:00

Large Language Models (LLMs) have become indispensable tools for businesses, but a key concern persists: How do you ensure your private or proprietary data isn’t used to train these models? This guide covers the most notable LLMs, their policies, and actionable steps to safeguard your data. Links to turn off data training for each tool are provided for easy access.

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Quality Systems Factors in Automation Engineering with AI

Submitted by jordan on Thu, 07/18/2024 - 17:00

Automation engineering with AI has become a cornerstone of modern quality systems. As organizations strive to improve efficiency, reduce costs, and enhance overall performance, understanding the key factors that contribute to a robust quality system is essential. Below, we explore the crucial components of quality systems in the context of automation engineering with AI, providing insights into how these elements can drive success.