发布节奏
最近 12 周每周中位浏览
最近 12 周发布时间
颜色 = 中位浏览爆款 vs 日常
爆款 = 浏览前 10%| 指标 | 爆款 | 日常 |
|---|---|---|
| 帖子 | 2 | 17 |
| 带媒体 | 100% | 82.4% |
| 回复 | 0% | 0% |
| 平均字数 | 281 | 249 |
| 中位浏览 | 14.2万 | 6,825 |
打法建议
只陈述相关性- 互动最好的时段是 23:00 前后(北京时间),中位浏览 4.3万。
- 一周里 周三 的中位浏览最高。
- 形式上 带图/视频 的中位浏览更高(8,645 vs 5,035)。
- 爆款里带媒体占 100%、回复占 0%;日常分别是 82.4%、0%。
- 当前节奏约 0.6 条/天,日均浏览 4.2万。
内容结构
按本站分析的分类(近 120 天)- 产品10
- 模型3
- tip3
- 论文2
- 产业1
形式与语言
帖子占比- 图片10
- 视频6
- 纯文字3
回复占比 0% · 引用 0% · 带媒体 84.2%
互动最高的帖子
按浏览排序- 1
Introducing Fast Search in the Perplexity Search API. Fast Search runs on Photon, our new Rust-based retrieval and ranking service that we built with a small team of engineers and hundreds of agents. It returns 95% of search results in 230 ms or less.
- 2
We’re partnering with Nvidia and 100+ industry partners to build infrastructure that contains rogue AI agents. In this research, we gave 9 AI models root access inside SPACE and told them to break out. Across 108 runs, none breached the VM boundary. https://www.perplexity.ai/hub/blog/escaping-space-part-i
- 3
Introducing Automations in Perplexity Computer. Automations are for ongoing work. They can take action in response to event-based triggers or on a schedule. Automations work with your memory, skills, and connected apps like Slack, Gmail, Outlook, and Linear.
- 4
New research: We post-trained a Computer model to learn from its own errors using hint-guided self-distillation. In a live A/B test, a later trained checkpoint reduced tool-call failures by 21.2% relative to an earlier checkpoint.
- 5
Agent governance is an engineering problem. We’ve built safeguards into Perplexity’s infrastructure, harnesses, and tools, and put them to work across our products. Today we're sharing how we engineer safer agents: https://www.perplexity.ai/hub/blog/how-we-engineer-safer-agents
- 6
Portable Computer for Windows is now available on @AMD Ryzen AI Max Series processors. It makes it easy to run local AI agents that work with your connected apps and local files. Kick off tasks or schedule recurring work that runs entirely on your device.
- 7
Claude Opus 5.5 is now available as the Standard effort level in Perplexity Computer. On our WANDR benchmark, it scored 0.610 at $4.13 per task, slightly outperforming Claude Fable 5.1 while costing 67.6% less per task.
- 8
GPT-6 Sol is now available in Perplexity and Computer. It's now the default Light option in Computer's effort selector.
- 9
We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage
- 10
Perplexity Computer now creates interactive charts and visualizations directly in your thread. For financial data, Computer uses @tradingview Lightweight Charts for candlesticks, volume, and moving averages.
- 11
This is how we built the filesystem behind Perplexity Projects as a Git forge for agents. Each agent works in its own sandbox and commits to a shared repository where people and other agents can build on its work across sessions. Read more: https://www.perplexity.ai/hub/blog/building-shared-workspaces-for-people-and-agents
- 12
Perplexity Computer can now run fully local on your Mac. Work with your native Mac apps and local files, and run tasks without sending sensitive data to the cloud. The model and the agent both run on your Mac. Available now in the Perplexity Mac app.
点赞、转发、浏览是采集那一刻的快照,不是帖子最终数据;时间为北京时间。KPI 卡片里的同比只在账号有 8 周以上数据时显示, 否则只看近 4 周。“打法建议”只说明数据里的相关性,不代表因果。
