WorkBuddy
最近拿 trae 写东西感觉经常会犯一些很低级的错误，写的页面也排版比较随意，不知道是什么原因。而且好像 IDE 模式写出来会准确率高一些，挺奇怪的，问题 IDE 写多个项目又不是很好用。 想着换一个 agent 试试，openCode 和 WorkBuddy 会不会好一些呢？ codex 我也在用，但是里面在用 gpt 的模型，不太想切换。大佬们有什么建议吗？
This explanation quotes or summarizes the cited source record. Saving creates a bookmark on this device, with no automatic account sync.
- First observed in our collection
- Oct 30, 2026, 4:15 AM UTC
- Earliest cited publication
- Nov 1, 2026, 4:10 PM UTC
- Last observed
- Nov 12, 2026, 6:30 AM UTC
Evidence in the last complete seven days
- Source mentions
- 2 Previous seven days 1
- Distinct sources
- 2 Previous 1
- Comparable growth
- Not comparable
- Ranking appearances
- 0
Collection observations and undated records are separate from dated publication counts. Observed in 7d：2 · Undated records：0
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：2026-11-06 → 2026-11-13 (end excluded) · History 25 days
| Source | Current days | Previous days | Last success |
|---|---|---|---|
| arXiv | 7 | 6 | Nov 12, 2026, 11:22 AM UTC |
| arXiv | 7 | 7 | Nov 12, 2026, 11:21 AM UTC |
| Ask HN | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Ask HN | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Ask HN | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Ask HN | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| GitHub | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Issues | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Releases | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Issues | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Releases | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| GitHub | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| GitHub Issues | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Releases | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| GitHub Discovery | 7 | 1 | Nov 13, 2026, 1:16 AM UTC |
| GitHub Trending | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| 7 | 7 | Nov 12, 2026, 9:30 AM UTC | |
| Hugging Face | 7 | 7 | Nov 12, 2026, 9:30 AM UTC |
| Hugging Face Spaces | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| Hacker News | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Hacker News | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Hacker News | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| Hacker News | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| Show HN | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| MIT News | 6 | 7 | Nov 13, 2026, 1:16 AM UTC |
| OpenAI | 7 | 7 | Nov 12, 2026, 12:45 PM UTC |
| Product Hunt | 7 | 7 | Nov 13, 2026, 1:17 AM UTC |
| 量子位 | 6 | 6 | Nov 12, 2026, 12:15 PM UTC |
| OpenRouter | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| GitHub Trending | 7 | 7 | Nov 12, 2026, 4:00 AM UTC |
| Hugging Face Models | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| HF Daily Papers | 6 | 1 | Nov 13, 2026, 1:17 AM UTC |
| OpenRouter | 7 | 1 | Nov 13, 2026, 1:17 AM UTC |
| Product Hunt | 7 | 7 | Nov 12, 2026, 4:00 AM UTC |
| SEC EDGAR | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| Sequoia Capital | 7 | 7 | Nov 12, 2026, 7:45 AM UTC |
| Simon Willison | 7 | 1 | Nov 12, 2026, 4:45 PM UTC |
| TechCrunch | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| Contracts Finder | 7 | 7 | Nov 13, 2026, 1:16 AM UTC |
| V2EX | 7 | 7 | Nov 13, 2026, 1:00 AM UTC |
- Counts describe monitored public sources, not the whole web or search demand.
Daily history · up to 90 days
Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.
View accessible daily data
| UTC date | Mentions | Sources | Observations | Ranking appearances |
|---|---|---|---|---|
| 2026-10-30 | 0 | 0 | 0 | 0 |
| 2026-10-31 | 0 | 0 | 0 | 0 |
| 2026-11-01 | 1 | 1 | 1 | 0 |
| 2026-11-02 | 0 | 0 | 0 | 0 |
| 2026-11-03 | 0 | 0 | 0 | 0 |
| 2026-11-04 | 0 | 0 | 0 | 0 |
| 2026-11-05 | 0 | 0 | 0 | 0 |
| 2026-11-06 | 0 | 0 | 0 | 0 |
| 2026-11-07 | 0 | 0 | 0 | 0 |
| 2026-11-08 | 0 | 0 | 0 | 0 |
| 2026-11-09 | 1 | 1 | 1 | 0 |
| 2026-11-10 | 0 | 0 | 0 | 0 |
| 2026-11-11 | 0 | 0 | 0 | 0 |
| 2026-11-12 | 1 | 1 | 1 | 0 |
| 2026-11-13 | 0 | 0 | 0 | 0 |
![Source reference for [程序员] 感觉 TRAE 的 Agent 模式有点笨啊，换 WorkBuddy 会好些吗. Screenshot not yet available.](/media/a5cbe4ac5ee0e8a7a1a54ed6)
Source evidence
Sources in the evidence shown：V2EX 2 · 量子位 1
V2EX · Published Nov 12, 2026
[程序员] 感觉 TRAE 的 Agent 模式有点笨啊，换 WorkBuddy 会好些吗
最近拿 trae 写东西感觉经常会犯一些很低级的错误，写的页面也排版比较随意，不知道是什么原因。而且好像 IDE 模式写出来会准确率高一些，挺奇怪的，问题 IDE 写多个项目又不是很好用。 想着换一个 agent 试试，openCode 和 WorkBuddy 会不会好一些呢？ codex 我也在用，但是里面在用 gpt 的模型，不太想切换。大佬们有什么建议吗？
View source record →量子位 · Published Nov 9, 2026
腾讯混元跻身最强开源模型！一手实测Hy4：搭配WorkBuddy，多模态Agent交付力拉满
View source record →V2EX · Published Nov 1, 2026
[分享创造] 给开源 AI 雷达做了 WorkBuddy 技能，想把选型从名单变成可核查记录
之前做了 AI 开源项目雷达，这次把研究与选型流程适配成 WorkBuddy 技能「开源 AI 项目研究与选型」，已审核上架。 主要解决一个小问题：问 AI“帮我选个知识库”时，容易得到几个名字，但硬条件、偏好和证据缺口常混在一起。 可以直接这样问： “帮我选自托管中文知识库，Docker 和 Web 界面必须具备，低代码只是偏好。关键能力给来源和数据时点；找不到匹配就说明，不要偷偷放宽要求。” 也可以比较指定候选、核对许可证据、找替代方案，或整理待验证的候选技术栈。输出区分事实、建议、未知项与风险，不把 Star 当适用性，不替你安装或执行仓库。 我是开发者，使用的是公开数据，原有实现公开可获得； WorkBuddy 版提供平台内适配。购买不含 WorkBuddy 积分或网站会员，费用和条件看当前平台页面。以
View source record →
Review priority 51 / 100
This score orders research candidates. It is not search volume, a forecast or verified commercial demand.
Evidence
12.7 / 30Distinct dated articles or first observations; repeat crawling adds nothing.
Publisher diversity
16 / 25Conservative owner grouping; syndicated copies count once.
Comparable change
0 / 20Comparable complete weeks only; no percentage from a zero baseline.
Recent evidence
14.2 / 15Latest publication or first observation, not repeated fetch time.
Name specificity
7.8 / 10Explicit entities and technical concepts outrank ambiguous acronyms.
Limitations and interpretation
- First observation is a collection milestone, not proof that a word or product is new to the world.
- Counts describe monitored public sources, not the whole web or search demand.
- First observed here is not the invention date of a name or idea.
- Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
- Owner grouping is conservative; undisclosed syndication may remain.
- The previous week is too small for a reliable percentage change.