Deep Reflection – A Cluster of SLMs (Small Language Models)

Currently, we are witnessing a true race among Big Techs to develop the most powerful and advanced LLMs (Large Language Models). DeepSeek, rather than merely challenging these major companies technologically, has created an opportunity for other companies and startups to think outside the box, especially for independent technology developers.

In this context, Deep Reflection takes a completely different path from Big Techs by not attempting to create yet another general-purpose LLM. While DeepSeek’s example is inspiring, the reality is that such a model is still far beyond the resources available to small developers. Therefore, innovation and alternative thinking become essential.

The Deep Reflection approach revolutionizes how generative AI is conceived today. Instead of focusing on a massive general-purpose model, we are moving toward specialized SLMs (Small Language Models) designed for specific use cases. This means that Deep Reflection focuses on training highly specialized SLMs targeted for narrower applications. This approach is more viable for small developers, as the computational cost of training smaller models is significantly more accessible.

However, the true innovation in this model is not just about avoiding the creation of a massive general-purpose LLM—it lies in building a cluster of SLMs that interact and cooperate, forming a community-driven intelligence. The idea is simple: if I can’t have a supercomputer, I create an ecosystem of smaller models that work together and complement each other. This decentralized approach opens up new possibilities for innovation and democratizes access to AI development.