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NEW

The Next Five Years for Digital Twins: From Mirror to Agent

Over the next five years, digital twins move from passive mirrors to active operators. The shift is easy to state. Twins that once just reflected what a machine or building was doing will start reading live data, suggesting the next move, and taking limited action inside hard rules. In regulated…
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NEW

Why Most AI Agents Fail in Production

Understanding why AI agents fail in production is critical because these failures cost businesses hundreds of thousands of dollars per project and erode customer trust. Industry data reveals 88% of AI agent projects fail before reaching production, with 61% of these failures tied to preventable…
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NEW

Why AI Struggles with Practical Optimization Problems

Traditional AI models face significant challenges in practical optimization settings due to their inherent design limitations. These challenges often stem from their inability to efficiently process high-dimensional data, handle noisy or sparse datasets, and adapt to complex problem structures. As…
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NEW

AI evals vs AI Hype: what wins ?

Watch: The State of AI Code Quality: Hype vs Reality. Itamar Friedman, Qodo by AI Engineer Evals win. The number we keep running into is blunt: about 75% of AI projects fail, and the common thread is skipped evaluation. Hype buys you a demo. Evals buy you something that holds up when real users…
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NEW

Why Local Models Are Enough for Enterprise AI

Local models moved from hobbyist territory to a real deployment option for one reason: you can run AI without shipping sensitive data to someone else's servers. For a large share of internal enterprise work, that single property settles the decision. You keep the data, you control the cost, and you…
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