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Why Data Engineering Is No Longer Optional for Mid-Market Companies

3 min By Lucas Hendrich

Mitigating the AI Productivity Tax in Software Engineering: Best Practices for Adoption

3 min By Lucas Hendrich

Bridging the Gap Between Business and Data: The Critical Importance of the Semantic Layer

3 min By Lucas Hendrich

From Pilot to Production: Why Data Lineage Is Critical for AI at Scale

2 min By Lucas Hendrich

Strategic Priorities for 2026: Building AI-Ready Data Foundations

3 min By Lucas Hendrich

AI Is Transforming Software Engineering Workflows: Five Key Findings

2 min By Lucas Hendrich

Winning the AI and Data Talent Race with Managed Services

2 min By Lucas Hendrich

There are no new ideas in AI, only new datasets

3 min By Lucas Hendrich
ile tree view of a Python project named 'context_engineering_qatool'. Top-level files include: main.py, schema.py, and memory.py. There is a folder named 'agents' with three files inside: planner.py, generator.py, and validator.py. Another folder, 'examples', contains a single file: task_input.json.

From Prompt Craft to System Design: Context Engineering as a Core Discipline for AI-Driven Delivery

2 min By Lucas Hendrich

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