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Many of you are likely familiar with amazing AI image generators like Flux, ChatGPT's image features, Midjourney, and others. They generally operate in a similar fashion: you open a chat interface, type in a description (a 'prompt'), and then wait for the AI to create your

You've probably experienced this or something similar: you feel a slight ache, maybe a light headache, and out of curiosity, you quickly search for the reason online. Or, more recently, you might ask an AI like ChatGPT. Depending on how you ask, the results you get back can

Github hosts thousands of remarkable open-source projects, many of which are at the forefront of AI innovation. While a project's stars and forks aren't the sole indicators of its importance, they certainly provide a compelling snapshot of what's currently gaining traction and influencing the

Everyone's excited about AI these days. From writing amazing stories to creating stunning images, large language models (LLMs) have really grabbed our attention. But beyond the impressive achievements of a chatbot, there's a bigger, more ambitious area in AI that's starting to show up:

When fine-tuning large language models (LLMs), the quality and diversity of your training data are crucial. However, a common pitfall is generating repetitive examples; calling an LLM with similar prompts multiple times often yields very similar results, which does not lead to robust model improvements. To build truly capable and