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Kled V3：按需向 50 万贡献者网络派发数据采集任务
Kled V3 允许实验室指定所需数据，并在 72 小时内向 50 万以上自愿贡献者网络部署采集任务，提供 108 个覆盖图像、视频、音频、文本与标注的可配置模板。其思路是把模型失败点转化为采集任务，从真实世界获取新样本后再训练评估，形成更紧的反馈闭环。相关规模与时效数据来自厂商自述，尚无独立验证。
AI推荐理由 介绍 Kled V3 数据采集平台：实验室可指定所需数据，平台声称可在 72 小时内向 50 万以上自愿贡献者网络派发采集任务，提供 108 个覆盖图像、视频、音频、文本和标注的可配置模板，形成“发现模型失败→转化为采集任务→获取真实样本→再训练评估”的闭环。属于第三方转述的产品宣传，72 小时、50 万贡献者等数字均来自厂商自述，无独立验证；但数据采集瓶颈与反馈闭环思路对模型训练有实际参考价值。
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You can't scrape a dataset that doesn't exist yet.
Think about training a model on people performing a specific task, in a specific environment, from a specific camera angle.
Someone has to go and capture those examples.
This is what makes Kled V3 interesting.
Labs can specify the data they need, and Kled can deploy collection tasks to its network of 500,000+ opt-in contributors within 72 hours.
108 configurable templates across image, video, audio, text, and annotation. Contributors capture the data on their phones, with instructions and examples of what qualifies.
The opportunity here is a much tighter feedback loop:
Identify where a model fails.
Turn that failure into a collection task.
Get new examples from the real world.
Train and evaluate again.
The ability to repeatedly collect the exact data a model is missing could be very powerful.
Kled AI@useKled
This is Kled V3. We've solved data collection for artificial general intelligence.
Any consumer dataset that can exist, can now be collected from physical reality in under 72 hours.
All powered by the largest and most comprehensive data application layer on the planet. (Thread)
[Your browser does not support the video tag.](https://video.twimg.com/amplify_video/2105311524495437824/vid/avc1/3126x2160/-yNvpLUU2F5Mkugh.mp4?tag=29)
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