There are more than 2000 living languages in Africa, most of which have been bypassed by advances in language technology. Current leading LLMs exhibit strong performance on a number of the most common languages (e.g. Swahili or Yoruba), but prioritise support for the languages with the most speakers first, resulting in piecemeal ability across disparate languages. We contend that a regionally focussed approach is more efficient, and present a case study for Uganda, a country with high linguistic diversity. We describe the development of Sunflower 14B and 32B, a pair of models based on Qwen 3 with state of the art comprehension in the majority of all Ugandan languages. These models are open source and can be used to reduce language barriers in a number of important practical applications.

Introduction: Poor educational quality in Secondary Schools is still regarded as one of the major struggles in 21st century Uganda - especially in rural areas. Research identifies several problems, including low quality or absent teacher lesson planning. As the government pushes towards the implementation of a new curriculum, exiting lesson plans become obsolete and the problem is worsened. Using a Retrieval Augmented Generation approach, we developed a prototype that generates customized lesson plans based on the government-accredited textbooks. This helps teachers create lesson plans more efficiently and with better quality, ensuring they are fully aligned the new curriculum and the competence-based learning approach. Methods: The prototype was created using Cohere LLM and Sentence Embeddings, and LangChain Framework - and thereafter made available on a public website. Vector stores were trained for three new curriculum textbooks (ICT, Mathematics, History), all at Secondary 1 Level. Twenty-four lessons plans were generated following a pseudo-random generation protocol, based on the suggested periods in the textbooks. The lesson plans were analyzed regarding their technical quality by three independent raters following the Lesson Plan Analysis Protocol (LPAP) by Ndihokubwayo et al. (2022) that is specifically designed for East Africa and competence-based curriculums. Results: Evaluation of 24 lesson plans using the LPAP resulted in an average quality of between 75 and 80%, corresponding to "very good lesson plan". None of the lesson plans scored below 65%, although one lesson plan could be argued to have been missing the topic. In conclusion, the quality of the generated lesson plans is at least comparable, if not better, than those created by humans, as demonstrated in a study in Rwanda, whereby no lesson plan even reached the benchmark of 50%.

Many of her neighbors have fallen on hard times since Covid-19 shut the city last month, but she's been lifted into the lap of luxury. Akol, who is 28, works for Samasource, a company that labels images and other data for companies such as Google, creating the feedstock for artificial intelligence projects like self-driving cars. She's the main breadwinner in the busy Nairobi apartment she shares with her 7-year-old son and her two brothers, ages 8 and 24. But Akol hasn't seen her family or apartment for around a month because, like most of Samasource's Nairobi staff, she now lives and works from a resort hotel. Her window at the four-star Ole Sereni overlooks the grassy plains of Nairobi National Park--a major change from the company's open-plan office next to a freeway.

Artificial Intelligence is the biggest threat to mankind, right? Even if robots aren't taking over the planet by force, the yarn goes, computers will surely push us all into unemployment in the next decade or so. Let's meet someone who can give us a slightly different perspective. This is Joel, standing in front of his house, a few kilometers outside Gulu, Uganda, where he lives with his 14 brothers and sisters. Joel works for Zillow, the leading online real estate marketplace in the US with 1.1B of revenue in 2017.