Continuous fine tuning involves adjusting a model to a specific code base which initially seems beneficial, but becomes static over time unless continuously pre-trained. Despite challenges, continuous fine tuning is becoming more accessible and could be effective if done regularly. On the other hand, context-aware retrieval (RAG) is a powerful method to enhance context by retrieving relevant code snippets from large bases, although the ranking problem is complex. Techniques like Gentic RAG and RAG fine tuning are gaining traction to optimize context utilization. Companies like Codeium leverage advanced techniques such as crawling imports and user intent to augment the retrieval logic. In the quest for better AI agents for coding tasks, progress is being made by various companies and projects. Evaluations show varying levels of issue fixes by different models, with Open-Devon achieving up to 21% improvement. The concept of flow engineering, emphasizing iterative code generation and testing, has been highlighted as a promising approach for enhanced performance. However, the effectiveness of prompting in increasing performance as a long-term competitive advantage remains uncertain. It's important to note limitations in evaluation methods like Sweetbench, as they may introduce bias by providing only a subset of code for testing instead of the entire repository.