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春雨医生
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How DeepSeek Is Transforming AI Application Development: 34 Insights from 7 Practitioners | Ronghui --- The past two months have been a "DeepSeek moment" for China's AI industry. From the open-source release of DeepSeek-R1 to the subsequent frenzy of adoption across the ecosystem, the model has become a focal point of discussion among developers, product managers, and investors alike. But beyond the hype, how is DeepSeek actually changing the way AI applications are built? What new possibilities does it unlock, and what limitations remain? We spoke with seven practitioners who have been working hands-on with DeepSeek across different domains — from consumer apps and enterprise software to creative tools and infrastructure. Below are 34 of their most salient observations, organized by theme. --- ## On Model Capabilities and Trade-offs 1. **"DeepSeek-R1's reasoning ability is genuinely step-function better on certain tasks."** One founder building legal AI noted that R1 outperformed GPT-4o on complex contract analysis where multi-step logical deduction was required, with error rates dropping roughly 30%. 2. **But that reasoning comes with latency costs.** Several developers emphasized that R1's chain-of-thought output makes it unsuitable for real-time applications. "You can't use it
From consumer-facing apps to vertical domains like travel, finance, and healthcare, and on to AI agents.
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