Imagine a world where languages on the brink of extinction could be revived, their stories and wisdom preserved for future generations. This isn't science fiction, but the potential of a groundbreaking new approach to language translation using AI. Researchers are tackling the immense challenge of "no-resource" language translation – languages with extremely limited digital presence, sometimes with fewer than 100 recorded sentences. Traditional machine translation techniques, which rely on extensive data, simply break down in these scenarios. But the rise of large language models (LLMs) like those from xAI, is changing the game. This research explores how LLMs, powered by their ability to reason and infer, can decipher these lost languages. The study tested three approaches: fine-tuning specialized translation models, using LLMs with "chain-of-reasoning" prompts, and direct prompting. Surprisingly, the traditional fine-tuning method, a workhorse of low-resource language translation, completely failed. Direct prompting offered decent results with small datasets but struggled as the data increased. The real star was the chain-of-reasoning approach. By providing the LLM with a small set of translated phrases and asking it to deduce the meaning of new ones, the model achieved remarkable accuracy, even surpassing human-level performance in some cases. As the dataset grew, the chain-of-reasoning method only got better, suggesting its potential to unlock a treasure trove of linguistic knowledge hidden within endangered languages. While vocabulary remains a challenge, this approach represents a giant leap forward, opening doors to not only preserving cultural heritage but also understanding the vast diversity of human language.