Opportunity dossier O-0309

Recurring need documented in 1 independent public source item.

Original-language research note

低信息量职业身份匹配查找工具
通过姓名、年龄和低分辨率头像截图，在LinkedIn等职业社交平台快速定位目标联系人

First seen 2026-07-15 · Last updated 2026-07-15 · Recalculated daily

4.2

Evidence confidence, not a return forecast

Independent source items, deduplicated by post

Public source families

Payment evidence: current spend or explicit intent

ProblemThe source-linked evidence below documents the recurring problem.

People affectedPeople represented in the linked public discussions.

Named alternativesNo existing alternative is named in the qualifying evidence yet.

Weekly mentions · 12 weeksNEW

04-2705-2506-2207-13

Trend factor ×1.0, capped at 2.0.

Representative evidence

1 public excerpts · 1 items in the full chain

Need★★★☆☆Feature request

最近因为某个 messaging platform 上的技术故障，和一位职业联系人失去了联系。我正在尝试在 LinkedIn、Xing 或类似的社交网络上找到他们的职业资料，以便重新建立联系。我有以下数据点：他们的姓名（名和姓）他们的近似年龄（这让我大致了解他们的大学毕业生或职业开始年份）他们圆形聊天头像的截图（分辨率相对较低）我在寻找一种软件工具、网络服务或浏览器扩展程序，能帮助我结合 Chinese research excerpt

Use the source link for the original-language context.

SoftwareRecs SE2026-07-15View source ↗

Follow the full evidence chain

Traceable items · inspectable scoring · warming and payment alerts

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Scoring summary

Evidence confidence = strength × evidence volume × source diversity × payment × competition × trend × 10. Counts use independent content items; only first-hand pain, current spend, explicit willingness to pay and concrete feature requests affect the score. Repeated posts by one author are discounted. Scoring and status changes follow deterministic rules.

“Evidence-backed” means the problem appears real, recurring and connected to spending. It does not mean the business is worth building. Read the full methodology.