OpenEvolve, When Code Learns to Improve Itself

For years, the dream of AI-assisted coding has felt like science fiction slowly turning into reality. We’ve watched large language models write functions, suggest syntax, and even generate entire applications from a single prompt. Yet for all their skill, these systems are still limited by one thing: they don’t learn from their own mistakes. They generate and you review. They suggest, you refine. The process ends where human feedback begins. 

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How Model Size Impacts Accuracy, Efficiency, and Cost 

In the world of artificial intelligence, bigger often seems better. Every few months, we hear about a new model with more parameters, more training data, and more impressive benchmarks. From GPT-style large language models to advanced vision architectures, the race to scale AI systems shows no signs of slowing down. But while increasing model size can boost performance, it’s not a free upgrade. Larger models come with trade-offs in efficiency, cost, and even accessibility. Understanding how scaling impacts each of these areas is important for anyone building, deploying, or managing AI systems. 
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The Art of Feature Engineering

When people talk about machine learning, the spotlight usually lands on the model. Whether it’s the neural network, algorithm, or architecture. But behind every great model is something far less glamorous and far more important: the data it learns from. And not just the data itself, but how that data is represented.
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Why Dropout Layers Matter in Neural Networks 

Neural networks are powerful, but they’re also prone to a classic problem: overfitting. When a model performs perfectly on its training data but fails to generalize to new, unseen data, it’s not really learning; it’s memorizing. In real-world applications, that’s a big issue. To address this, researchers have developed several regularization techniques that help models learn patterns instead of noise. One of the most effective and widely used options is dropout. It’s simple, elegant, and surprisingly powerful. 
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What is Self-Supervised Learning?

When most people think about training artificial intelligence, they think of massive datasets that need to be labeled. It could be thousands of annotated images, carefully tagged text, or structured tables telling the model exactly what each piece of data represents. But labeling data like this is expensive, time-consuming, and sometimes downright impossible. That’s where self-supervised learning comes in. 
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Feedforward Neural Networks vs. Deep Neural Networks: What’s the Difference? 

When people think of artificial intelligence, they often picture massive, complex neural networks powering self-driving cars or language models that can write like humans. But at the foundation of all these systems lies a much simpler idea: the feedforward neural network (FNN). It’s the original blueprint for how machines can learn from data. Over time, this concept has evolved into what we now call deep neural networks (DNNs), which are larger, more powerful versions capable of tackling far more complex tasks.
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