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In a recent reflection on the use of large language models (LLMs) in software engineering, a developer shared their experience primarily with Opus 5.5. The model has become their go-to tool, as it balances speed and intelligence without overwhelming complexity. The author highlights a shift in their workflow, stating they largely rely on Opus 5.5 for coding tasks while maintaining a preference for local environments over cloud solutions, which they find less reliable. This analysis underlines a growing sentiment in the engineering community about the role of LLMs in everyday development processes, showcasing that while these tools enhance productivity, they don’t inherently improve problem-solving skills or creativity. The significance of this experience lies in the evolving relationship between engineers and AI tools. Despite the convenience offered by models like Opus 5.5, the author notes that the deep thinking and internal tool investment still require human input to drive true innovation. This perspective suggests that while LLMs can streamline certain tasks, engineers must engage critically with the technology to tackle complex system-level challenges, rather than relying on AI for shortcuts. The reflection ultimately emphasizes the importance of balancing AI assistance with human expertise, fostering a culture of thoughtful problem-solving in software engineering.