这个项目展示了如何用 Weaviate 和 Gemini 快速构建一个从趋势发现到个性化内容生成的完整 AI 工作流，做社交媒体运营或内容创作的团队可以直接借鉴其思路。
在柏林黑客松中，一个团队用 36 小时构建了结合 Twitter 趋势检测与个性化 AI 内容生成的系统。技术栈包括 Weaviate 用于语义搜索、Gemini 模型生成内容、Tavily 提供上下文检索，以及基于 Pioneer 的评分模型。系统通过语义聚类识别新兴趋势，并分析用户写作风格建立人格模型，从而生成符合个人风格的推文。该项目最终赢得 1 万美元大奖，展示了 AI 在社交媒体内容创作中的创新应用。
A few weeks ago, @philipvollet, @victorialslocum and @aestheticedwar1 joined the 𝗕𝗶𝗴 ...
A few weeks ago, @philipvollet , @victorialslocum and @aestheticedwar1 joined the 𝗕𝗶𝗴 𝗕𝗲𝗿𝗹𝗶𝗻 𝗛𝗮𝗰𝗸 with a mission: build an epic project with Weaviate, and win the top prize. They only had 36 hours start to finish, and the concept was ambitious - combine trend-dectection on Twitter with persona-based AI content generation. 𝗧𝗵𝗲 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: • Weaviate for storing and semantically searching Twitter data • Gemini models for content generation • Tavily for additional context retrieval • A custom scoring model built with Pioneer to rank trends The system monitors Twitter to identify emerging trends using semantic clustering. When it finds something relevant, it analyzes your writing style and previous content to build a persona model. Then it generates new posts about those trends - but in 𝘺𝘰𝘶𝘳 voice, not generic AI-speak. The question is: did their sleep-deprived hackathon project manage to take home the 10k top prize? Watch youtu.be/i1cYfxB8-mw?si… out 👀 https://t.co/Uy1CeQT1TU 💬 0 🔄 1 ❤️ 2 👀 335 📊 1 ⚡