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New Term Radar
Discover names and concepts appearing in public sources, with dated evidence, source coverage and comparable growth.
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Collection coverage · Comparable windowsCurrent / previous observed days 7 / 7
Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.
Current window:2026-10-31 → 2026-11-07 (end excluded) · History 19 days
| Source | Current days | Previous days | Last success |
|---|---|---|---|
| arXiv | 6 | 5 | Nov 7, 2026, 12:45 AM UTC |
| arXiv | 7 | 4 | Nov 7, 2026, 5:30 AM UTC |
| Ask HN | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| Ask HN | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| Ask HN | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| Ask HN | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| GitHub | 7 | 5 | Nov 7, 2026, 1:15 PM UTC |
| GitHub Issues | 7 | 5 | Nov 7, 2026, 4:15 PM UTC |
| GitHub Releases | 7 | 3 | Nov 7, 2026, 12:45 PM UTC |
| GitHub | 7 | 5 | Nov 7, 2026, 2:45 PM UTC |
| GitHub Issues | 7 | 5 | Nov 7, 2026, 5:15 PM UTC |
| GitHub Releases | 7 | 5 | Nov 7, 2026, 5:15 PM UTC |
| GitHub | 7 | 5 | Nov 7, 2026, 2:45 PM UTC |
| GitHub Issues | 7 | 5 | Nov 7, 2026, 5:30 PM UTC |
| GitHub Releases | 7 | 5 | Nov 7, 2026, 5:15 PM UTC |
| GitHub Discovery | 2 | 0 | Nov 7, 2026, 11:00 PM UTC |
| GitHub Trending | 7 | 5 | Nov 7, 2026, 8:00 PM UTC |
| 7 | 4 | Nov 7, 2026, 8:45 AM UTC | |
| Hugging Face | 7 | 4 | Nov 7, 2026, 8:45 AM UTC |
| Hugging Face Spaces | 2 | 0 | Nov 7, 2026, 10:30 PM UTC |
| Hacker News | 7 | 5 | Nov 7, 2026, 8:00 PM UTC |
| Hacker News | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| Hacker News | 7 | 5 | Nov 7, 2026, 11:00 PM UTC |
| Hacker News | 2 | 0 | Nov 7, 2026, 11:00 PM UTC |
| Show HN | 2 | 0 | Nov 7, 2026, 11:00 PM UTC |
| MIT News | 7 | 4 | Nov 7, 2026, 5:45 PM UTC |
| OpenAI | 7 | 5 | Nov 7, 2026, 11:45 AM UTC |
| Product Hunt | 7 | 5 | Nov 7, 2026, 8:00 PM UTC |
| 量子位 | 6 | 5 | Nov 6, 2026, 7:00 PM UTC |
| OpenRouter | 2 | 0 | Nov 7, 2026, 11:07 PM UTC |
| GitHub Trending | 7 | 7 | Nov 6, 2026, 1:15 PM UTC |
| Hugging Face Models | 2 | 0 | Nov 7, 2026, 11:07 PM UTC |
| HF Daily Papers | 2 | 0 | Nov 7, 2026, 11:07 PM UTC |
| OpenRouter | 2 | 0 | Nov 7, 2026, 11:07 PM UTC |
| Product Hunt | 7 | 7 | Nov 6, 2026, 1:15 PM UTC |
| SEC EDGAR | 7 | 5 | Nov 7, 2026, 10:45 PM UTC |
| Sequoia Capital | 7 | 4 | Nov 7, 2026, 6:45 AM UTC |
| Simon Willison | 2 | 0 | Nov 7, 2026, 3:30 PM UTC |
| TechCrunch | 7 | 5 | Nov 7, 2026, 9:45 PM UTC |
| Contracts Finder | 7 | 5 | Nov 7, 2026, 8:15 PM UTC |
| V2EX | 7 | 5 | Nov 7, 2026, 9:45 PM UTC |
- Counts describe monitored public sources, not the whole web or search demand.
Seven-day figures use the complete UTC window above. Priority is a research triage score, not search volume, market size or a probability of success.
PreReason
First observed Oct 14, 2026 · Hacker News
- 7d mentions
- 0 previous 0
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
前发出来
分享一下我最近一直在开发的项目 Picloom —— 以插件编织你的自托管图床 ,一个支持插件扩展和自定义主题的开源自托管图床。 这个项目已经持续迭代了挺长一段时间,目前功能和使用体验都逐渐趋于稳定,想趁正式发布前发出来,邀请有自建图床需求的朋友试用,听听大家的意见。 图床项目已经蛮多的了,但往往有需要个性化自定义的时候,所以说在图床上 反复造轮子 已经屡见不鲜,特别是当前 AI 大大降低了编码门槛。所以我为什么再「造一个轮子」呢? 从零开始(或者 fork 一个开源图床),往往需要很多精力——你需要自己处理依赖,实现一个图床项目的基本功能,再去拓展你的个性化实现,并且后续维护项目,debug——消耗自己的时间与精力。所以为什么不提供一个通用的框架,让插件编织自己的个性化图床呢? Picloom 的一个主要思路
First observed Oct 25, 2026 · V2EX
- 7d mentions
- 0 previous 1
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
JSFactCheckBench
arXiv:2610.30504v1 Announce Type: new Abstract: Large language models (LLMs) are widely used to answer factual questions, but their answers can contain errors and must be fact-checked claim by claim. Yet the resulting score depends on three design decisions: which questions the claims answer, under what conditions each claim is judged, and which sources coun
First observed Oct 24, 2026 · arXiv
- 7d mentions
- 0 previous 1
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
几个月前在
几个月前在 V2EX 分享过 LaunchManager ,感谢当时大家的反馈。现在项目到了 v1.8.0 ,想带着目前的版本再来交流一下。 旧帖: https://v2ex.com/t/1217043 LaunchManager 是一个 Swift / SwiftUI 编写的原生工具,用来管理 LaunchAgents 、LaunchDaemons 和后台自动化任务,免费,MIT 协议开源,支持 macOS 14+。 最近一个很实用的场景是:让 AI 帮忙写定时脚本和 plist ,写完后还需要检查它到底配置了什么、什么时候触发、有没有正常运行。LaunchManager 可以把这部分放到一个窗口里处理: 导入 .plist ,或直接粘贴 AI 生成的 XML ,再用表单或 XML 检查、修改配置。 在一个任
First observed Oct 21, 2026 · V2EX
- 7d mentions
- 0 previous 0
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
传言
传言 Seedream 5.5 即将发布,能力可以超过 nano banana 和 GPT image 吗? 我搞了个套壳站 Seedream 5.5 ,关注最新消息! 有啥建议评论区互动!
First observed Oct 21, 2026 · V2EX
- 7d mentions
- 0 previous 0
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
用了一天
First observed Oct 11, 2026 · V2EX
- 7d mentions
- 0 previous 0
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100
超过
我认为 DeepSeek 、MiniMax 、Kimi 、GLM 这些 LLM 公司被严重高估了: 月活( MAU ) : 豆包 4.2 亿,千问 1.7 亿,DeepSeek 1.2 亿,智谱 2000 万,Kimi 1000 万; 商业化( ARR ) : 豆包 272 亿,智谱 68 亿、阿里 80 亿、DeepSeek 27-34 亿、Kimi 20 亿; 开源贡献 : Qwen:开源了超过 460 个模型,衍生模型超 30 万个,全球下载量破 20 亿次; GLM:开源 50 余款模型,GLM-5.2 以 51 分位居全球开源智能指数榜首; DeepSeek:开源了 DeepSeek-V4 、DeepSeek-R1 、DeepSeek-VL 等模型。 豆包的战略和 Google 的 Gemini 是很
First observed Oct 24, 2026 · V2EX
- 7d mentions
- 0 previous 0
- 7d sources
- 0
- 7d growth
- Not comparable
- Priority
- 8 / 100