# The context gold rush: Why everyone is building the same thing.
**作者**: Sam Z Liu
**日期**: 2026-07-23T08:37:57.000Z
**来源**: [https://x.com/samzliu/status/2080210797465379147](https://x.com/samzliu/status/2080210797465379147)
---
You either die building product or live long enough to do context management. Whether you call it a context graph, company brain, or LLM wiki, it seems that many start-ups and larger companies alike are building the same thing: a place to store data and context for tomorrow's agentic workforce. This is one of the four main ideas apparently left in AI: research lab, RL environment, infrastructure, or context management.
From an ecosystem perspective, a context management product checks all the boxes for a high growth start-up or internal innovation team:
- Jevon's paradox (timing) - Lowering the cost (time, friction, labour) of producing code and writing will cause an explosion of text-based data that our current tools (e.g. Slack, Notion, Github, etc.) are not designed to handle. We are still on the early part of that curve as agent adoption penetrates the wider economy.
- Self-improvement (vision) - There's an enticing vision of an autonomous self improving system where agents become better and better over time without human intervention. Capturing and managing context is a big part of what we believe will enable that capability. Moreover, it's a narrative that's sellable to executives: you better develop your own context layer now before your competitors do so or you'll never catch-up.
- Data sovereignty (tailwinds) - Companies and governments are becoming increasingly worried about the frontier labs training on their internal data. An external, trusted party to store and manage that data will become increasingly important.
- Context moat (business model) - The previous generation of SaaS was build on moats and the monopoly power. A lot of what drove this was high switching costs: once you become embedded into a company, it's extremely hard for them to switch off. Managing a company's context seems to have all the similar properties of yesteryear's glory days. Moreover, for some types of business models (e.g. Harvey), you can develop your own context moat by servicing customers. This is Nadella's reverse information paradox.
- Model capability saturation (tech edge and competition) - There's this growing belief that models can do anything we want them to as long as they have the right context. As frontier models have started to discover new math, there's a sense that we have saturated the intelligence needed for most tasks. The bottleneck, then, is context. This suggests that context is one of the few big areas of opportunity left that will not become commoditized by better models. Moreover, this also seems largely out of the main strike zone for the labs, making it a compelling focus area lest Anthropic kill your company.
All Roads Lead to Rome
Try to guess what company or category each screenshot belongs to
It's clear that something like a company brain is needed. But what's striking is how similar products can seem, even if they started from vastly difference places. Part of this is agents collapsing product into a singularity: a coding agent is fundamentally not that different from a marketing agent or email agent. And so it goes across the stack. But another big part of it is that the space is still early with lots of players trying to lay claim to a fuzzy field.
– Personal knowledge base — Github repo, Gbrain, Claude Code + @obsdmd. This is a folder of markdown files that serve as skills and memory for your agents. Where people start when experimenting. They fail when trying to expand to an entire team.
– Agent memory — @Letta_AI. @honchodotdev. @EngramLab. These are per-agent memory layers that scale vertically in time. The companies tend to be research focused on a goal of building super long horizon agents. There's a split between companies that believe in token spaces vs weight space.
– Observability tools — @braintrust, @raindrop_ai. They capture the traces your external, production agents emit. The outputs here tend to be dashboards and evals rather than an accumulating data store for future agents. Not directly playing here but they are a natural place where the data accumulates.
– Agent Dev tools — @EntireHQ, @mintlify. They provide a similar service to observability tools except these are for your internal coding agents. The outputs here are a set of docs so that your coding agents produce less slops and run for longer.
- Data moat builders — @appliedcompute. @PrimeIntellect. They sell enterprises on a vision of custom models and context layer that are hyper-specific to their workflows. Part research lab, part AI context consulting firm, part GPU provider, the strategy here is to own the customer relationship E2E and be a one-stop shop.
– Companies Reinventing Themselves — @NotionHQ, @clickup, @AirbyteHQ, @Glean, @PromptQL. These are companies that were established in related areas which joined in on the context gold rush.
- AI Employee — @viktor__com, @getlindy. Starting at the level of individual users, they are presumably that they are well positioned to capture context and build up a PLG notion that maps context across entire orgs. While Claude Tag was criticized as a trojan horse for doing this very thing, these smaller companies will face less resistance.
- Vertical AI-Native Service Companies — Too many to count. By building custom E2E workflows, they too build up an accumulating context layer. By going vertical, the strategy is to solve their customer's pain points better than any horizontal player.
- Company Brain — @joinstashAI (full disclosure - this is us!), @sentra_app, @hyperspell, @supermemory. All relatively early start-ups, these companies were started to natively solve the org-level context layer problem. The bet is that directly focusing on this problem from day 1 rather than laterally moving into it from the side will enable this category to win out.
And this doesn't even count the frontier labs which are doing similar work across their FDE & product teams or the related players such as @hydra_db, @pinecone (expanding vertically up from infrastructure), @ExaAILabs (seem to be moving from web search to enterprise search), agent orchestration companies that are building memory into their product, agent workflow companies, or integration companies like @composio that connect to existing context and sources of truth. These all could laterally move into context management if there's an opportunity. For instance, an open question is as models get smarter, are integrations all you need?
There is likely space for many of these companies to thrive. Despite the similar marketing copy, they are probably not competitors: going after different customers with different use-cases at different parts of the stack. Context management is a problem that anyone using agents in the future will need to solve. And if the hype is to believed, that "anyone" will become "everyone" shortly enough. We will likely see a stratification as the temperature around context management dies down and it moves from a buzz word to a disciplined business. But it is a strange new world where a company selling to a 4 person design agency has the same essential product as a company selling cutting edge research to billion dollar tech companies.
The current patterns
Even across these very different starting points, a few patterns are becoming clear:
– Multiplayer and version control — Ensure that team members and their agents can collaborate. Version control is already critical for single player agents since not every multi-thousand line PR your coding agent puts up is desired. It's even more critical for multi-player as different team members modify and fork each other's work.
– Accumulating skills — An agent's token spend should not only benefit the direct task at hand but also the entire organization. The context layer becomes this "reasoning cache" which makes future agents perform better. Overtime, more and more workflows can be automated in this way.
– Hybrid retrieval layer — No single retrieval method wins. Combine vector search, knowledge graphs, and agentic search with keyword search for best results.
– Dreaming and sleep-time Compute — Separate out the agent that is doing the work (e.g. Claude Code session, workflow agent) from the agent that is a custodian over the knowledge base. The sleep-time agent indexes, de-dups, and updates.
– External connections — Pull from as many external sources as possible (e.g. Granola, email, calendar, slack, CRM, etc.). Treat these as raw data sources that your sleep-time agents read but don't edit over. To keep data isolation and permissioning simple, inherent the sharing scopes of these external connections.
An important note is that the current implementation of these patterns will become out of date as the industry evolves. We hosted a company brain and memory event a few weeks ago and found that most people believe the current paradigms will not stick around.
What's missing
We are still very early however and missing several key components to mass adoption:
– Combining structured and unstructured data — A clean ontology or mental model between all the different data types. Should the data be structured as database tables or file systems? How to deal with unstructured data such as Slack channels? What happens if some of the memory becomes stored in weights rather than pure text? Each type of data has different permission models, shapes, and update cadences. While humans can blend these seamlessly for different contexts (sorry overloaded term!), agents need a bit more guidance.
– Data ingestion everywhere — Company brains only become useful once you reach a tipping point of information that is inside of them. Otherwise, it's typically more efficient to go to each source directly. This requires ingesting data in all different formats from PDFs to database tables and integrating with all the tools at an organization. This makes sales cycles long due to security reviews and hefty integration FDE work.
– Data access and controls — Making sure the right people access the right information and the wrong people cannot access it. This is tricky to get right because each architecture has its own trade offs. Do you provision each agent a user? What information is shared between teams vs private? Can you build effective guard rails to prevent data leakage?
– Good evals — Does a company brain actually help your team become more productive? Or is it just a productivity nerd's dream? What makes one implementation measurably better from another? And since memory is inherently a long-horizon problem, rollouts on evals will becoming increasingly expensive and hard to do.
– Stability over long timeframes — Put it another way, we need to solve continual learning. We need to trust adding another skill or data source won't dilute the performance of the existing ones or that the knowledge bases will not slopify over time. Context rot is still a very real problem with LLMs today.
- Blast radius — Our internal name for how retrieved information is bounded by scope (time, context, prioritization). It's becoming clear that pure retrieval is not sufficient for good memory and we will need to develop better ways to instill common sense about which pieces of information matter more.
- Killer use-case — This is the big one. For all the hype around context graphs and company brains, the clear business case and ROI for these products are still being developed. The metrics have not caught up to the technology and vision. A few emerging hypotheses that are working okay so far:
- Retrieve information - this is the original Glean killer-use case. Find information across scattered datasets and sources of truth.
- Automate workflows - have agents do the repetitive, boring tasks. And have them do more and more of them as time goes on.
- Cost and latency savings - memory and context layers have been shown to decrease token costs by ~60-90%
- Better agent performance - have your agents complete tasks they would not have been able to otherwise.
These are the questions we're spending our time solving. If you're building or thinking about your own brain, we'd love to chat!
## 相关链接
- [Sam Z Liu](https://x.com/samzliu)
- [@samzliu](https://x.com/samzliu)
- [3.5K](https://x.com/samzliu/status/2080210797465379147/analytics)
- [reverse information paradox](https://x.com/satyanadella/article/2076323181154230284)
- [right context](https://alexzhang13.github.io/blog/2026/harness/)
- [Gbrain](https://github.com/garrytan/gbrain)
- [@obsdmd](https://x.com/@obsdmd)
- [@Letta_AI](https://x.com/@Letta_AI)
- [@honchodotdev](https://x.com/@honchodotdev)
- [@EngramLab](https://x.com/@EngramLab)
- [@braintrust](https://x.com/@braintrust)
- [@raindrop_ai](https://x.com/@raindrop_ai)
- [@EntireHQ](https://x.com/@EntireHQ)
- [@mintlify](https://x.com/@mintlify)
- [@appliedcompute](https://x.com/@appliedcompute)
- [@PrimeIntellect](https://x.com/@PrimeIntellect)
- [@NotionHQ](https://x.com/@NotionHQ)
- [@clickup](https://x.com/@clickup)
- [@AirbyteHQ](https://x.com/@AirbyteHQ)
- [@Glean](https://x.com/@Glean)
- [@PromptQL](https://x.com/@PromptQL)
- [@viktor__com](https://x.com/@viktor__com)
- [@getlindy](https://x.com/@getlindy)
- [Claude Tag](https://x.com/ashwingop/status/2069814177624121469)
- [@joinstashAI](https://x.com/@joinstashAI)
- [@sentra_app](https://x.com/@sentra_app)
- [@hyperspell](https://x.com/@hyperspell)
- [@supermemory](https://x.com/@supermemory)
- [@hydra_db](https://x.com/@hydra_db)
- [@pinecone](https://x.com/@pinecone)
- [@ExaAILabs](https://x.com/@ExaAILabs)
- [@composio](https://x.com/@composio)
- [hosted a company brain and memory](https://x.com/samzliu/status/2077482285960733068?s=20)
- [Context rot](https://www.trychroma.com/research/context-rot)
- [retrieval is not sufficient](https://plasticlabs.ai/blog/posts/Memory-as-Reasoning)
- [decrease token costs by ~60-90%](https://www.pinecone.io/blog/inside-askdata/)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
- [4:37 PM · Jul 23, 2026](https://x.com/samzliu/status/2080210797465379147)
- [3,531 Views](https://x.com/samzliu/status/2080210797465379147/analytics)
- [View quotes](https://x.com/samzliu/status/2080210797465379147/quotes)
---
*导出时间: 2026/7/24 00:25:26*
---
## 中文翻译
# 语境淘金热：为什么所有人都在做同一件事。
**作者**：Sam Z Liu
**日期**：2026-07-23T08:37:57.000Z
**来源**：[https://x.com/samzliu/status/2080210797465379147](https://x.com/samzliu/status/2080210797465379147)
---
你要么在做产品的路上死掉，要么活得够久去做上下文管理。无论你称之为上下文图谱、公司大脑还是 LLM wiki，似乎许多初创公司和大型公司都在构建同一种东西：一个为未来智能体员工存储数据和上下文的场所。这是 AI 领域显然仅存的四大方向之一：研究实验室、RL 环境、基础设施，或上下文管理。
从生态系统的角度来看，上下文管理产品符合高增长初创公司或内部创新团队的所有标准：
- 杰文斯悖论（时机）—— 降低生成代码和写作的成本（时间、摩擦、人力）将导致文本数据爆炸式增长，而我们现有的工具（如 Slack、Notion、Github 等）并不是为处理这些数据而设计的。随着智能体的应用渗透到更广泛的经济领域，我们仍处于这条曲线的早期阶段。
- 自我改进（愿景）—— 有一个诱人的愿景：一个自主的自我改进系统，智能体在无需人工干预的情况下随时间变得越来越好。捕获和管理上下文被认为是实现这一能力的重要组成部分。此外，这也是一个可以向高管兜售的叙事：你最好在竞争对手之前建立自己的上下文层，否则你将永远无法赶上。
- 数据主权（顺风的推动）—— 公司和政府越来越担心前沿实验室用他们的内部数据进行训练。一个外部的、可信赖的第三方来存储和管理这些数据将变得越来越重要。
- 上下文护城河（商业模式）—— 上一代 SaaS 是建立在护城河和垄断力量之上的。其中很大一部分驱动力是高昂的转换成本：一旦你深入嵌入一家公司，他们极难切换离开。管理一家公司的上下文似乎具备昔日辉煌时代的所有类似特性。此外，对于某些类型的商业模式（如 Harvey），你可以通过服务客户来打造自己的上下文护城河。这就是纳德拉的逆向信息悖论。
- 模型能力饱和（技术优势和竞争）—— 人们越来越相信，只要给模型正确的上下文，它们就能做任何我们想让它做的事。随着前沿模型开始发现新的数学成果，人们感觉到我们已经使大多数任务所需的智能达到饱和。那么瓶颈就是上下文。这表明上下文是仅存的少数不会被更好的模型商品化的大型机会领域之一。此外，这个领域似乎在很大程度上处于实验室的主要打击范围之外，使其成为一个引人注目的焦点领域——免得 Anthropic 干掉你的公司。
条条大路通罗马
试着猜猜每个截图属于哪家公司或哪个类别
显然，我们需要类似公司大脑这样的东西。但令人惊讶的是，即使这些产品起点截然不同，它们看起来却如此相似。部分原因是智能体将产品坍缩成了一个奇点：编码智能体与营销智能体或邮件智能体从根本上没有太大区别。整个技术栈都是如此。但另一个重要原因是这个领域仍处于早期，许多玩家都在试图占据这片模糊的领域。
– 个人知识库—— Github 仓库、Gbrain、Claude Code + @obsdmd。这是一个 Markdown 文件夹，用作智能体的技能和记忆。这是人们做实验时的起点。但在尝试扩展到整个团队时会失败。
– 智能体记忆—— @Letta_AI、@honchodotdev、@EngramLab。这些是按智能体划分的记忆层，随时间纵向扩展。这些公司往往以研究为重心，目标是构建超长时程的智能体。在相信 token 空间的公司和相信权重空间的公司之间存在分歧。
– 可观测性工具—— @braintrust、@raindrop_ai。它们捕获你的外部生产环境智能体发出的轨迹。这里的输出通常是仪表盘和评估，而不是为未来智能体积累的数据存储。它们并非直接参与这个领域，但它们是数据自然积累的地方。
– 智能体开发工具—— @EntireHQ、@mintlify。它们提供与可观测性工具类似的服务，只是针对你的内部编码智能体。这里的输出是一套文档，使你的编码智能体产生更少的垃圾代码并运行更长时间。
- 数据护城河建设者—— @appliedcompute、@PrimeIntellect。他们向企业推销一个愿景：为其工作流量身定制模型和上下文层。一部分是研究实验室，一部分是 AI 上下文咨询公司，一部分是 GPU 提供商，其策略是端到端地拥有客户关系，成为一站式商店。
– 重新改造自己的公司—— @NotionHQ、@clickup、@AirbyteHQ、@Glean、@PromptQL。这些公司成立于相关领域，后来加入了上下文淘金热。
- AI 员工—— @viktor__com、@getlindy。从个人用户层面起步，他们大概认为自己处于有利位置，可以捕获上下文，并建立一个将上下文映射到整个组织的 PLG 概念。虽然 Claude Tag 曾因做同样的事而被批评为特洛伊木马，但这些规模较小的公司将面临更少的阻力。
- 垂直 AI 原生服务公司—— 多到数不清。通过构建定制的端到端工作流，它们同样建立起一个不断积累的上下文层。通过走向垂直化，其策略是比任何横向玩家更好地解决客户的痛点。
- 公司大脑 — @joinstashAI（完全公开——这就是我们！）、@sentra_app、@hyperspell、@supermemory。这些都是相对早期的初创公司，它们的创立初衷就是原生地解决组织级上下文层的问题。押注在于，从第一天起就直接专注于这个问题，而不是从旁边侧向切入，将使这一品类胜出。
这还不算在各家前沿实验室的 FDE 和产品团队中做类似工作的团队，也不算相关玩家，比如 @hydra_db、@pinecone（从基础设施向上垂直扩展）、@ExaAILabs（似乎正从网络搜索转向企业搜索）、在产品中内置记忆功能的智能体编排公司、智能体工作流公司，以及像 @composio 这样连接现有上下文和事实来源的集成公司。如果有机会，这些公司都可能侧向进入上下文管理领域。例如，一个悬而未决的问题是：随着模型变得更聪明，集成是否就是你所需要的全部？
这些公司中的许多应该都有蓬勃发展的空间。尽管营销文案相似，它们很可能并非竞争对手：它们在技术栈的不同层面、以不同用例面向不同客户。上下文管理是未来任何使用智能体的人都需要解决的问题。而如果炒作可信，那个“任何人”很快就会变成“每个人”。随着上下文管理的热度降温、从一个流行词变成一门严谨的生意，我们很可能会看到市场分层。但这是一个奇怪的新世界：一家向四人设计工作室销售产品的公司，与一家向十亿美元级科技公司销售前沿研究的公司，本质上卖的是同一种产品。
当前的模式
即使起点如此不同，一些模式已经逐渐清晰：
– 多人协作与版本控制 — 确保团队成员及其智能体能够协作。版本控制对单机智能体来说已经至关重要，因为你的编码智能体提交的每一个数千行的 PR 并不都是你想要的。对多人协作来说它更为关键，因为不同的团队成员会修改并分叉彼此的工作。
– 技能积累 — 智能体的 token 消耗不仅应使眼前的直接任务受益，还应使整个组织受益。上下文层成为这种“推理缓存”，让未来的智能体表现更好。久而久之，越来越多工作流可以通过这种方式实现自动化。
– 混合检索层 — 没有任何单一检索方法能胜出。将向量搜索、知识图谱和智能体搜索与关键词搜索相结合，才能获得最佳效果。
– 做梦与睡眠时计算 — 将执行工作的智能体（如 Claude Code 会话、工作流智能体）与作为知识库管理者的智能体分离开来。睡眠时智能体负责索引、去重和更新。
– 外部连接 — 尽可能多地从外部来源拉取数据（如 Granola、电子邮件、日历、Slack、CRM 等）。将这些视为原始数据源，你的睡眠时智能体可以读取但不会在其上编辑。为保持数据隔离和权限管理简单，继承这些外部连接的共享范围。
需要注意的重要一点是，这些模式的当前实现将随着行业发展而过时。几周前我们举办了一场关于公司大脑与记忆的活动，发现大多数人认为当前范式不会长久存在。
缺失的部分
不过我们仍处于非常早期的阶段，要实现大规模普及还缺少几个关键组件：
– 结构化与非结构化数据的结合 — 在所有不同数据类型之间需要一个清晰的本体或心智模型。数据应该以数据库表还是文件系统的形式进行结构化？如何处理 Slack 频道等非结构化数据？如果部分记忆存储在权重中而非纯文本中会怎样？每种数据类型都有不同的权限模型、结构和更新频率。虽然人类可以为不同情境（抱歉，又是一个被过度使用的术语！）无缝融合这些内容，但智能体需要更多指导。
– 数据的全方位摄取 — 公司大脑只有在其内部信息达到临界点后才会变得有用。否则，直接访问每个数据源通常效率更高。这要求以从 PDF 到数据库表等各种格式摄取数据，并与组织内的所有工具集成。由于安全审查和繁重的集成 FDE 工作，这使销售周期变得很长。
– 数据访问与控制 — 确保正确的人访问正确的信息，而错误的人无法访问。这一点很难做好，因为每种架构都有其自身的权衡。你要为每个智能体配置一个用户吗？哪些信息在团队之间共享，哪些是私有的？你能否构建有效的防护机制来防止数据泄露？
– 良好的评估 — 公司大脑真的能帮助你的团队提高生产力吗？还是只是生产力极客的梦想？是什么让一种实现比另一种可衡量地更好？而且由于记忆本质上是一个长周期问题，评估的推广将变得越来越昂贵和困难。
– 长时间跨度下的稳定性 — 换句话说，我们需要解决持续学习的问题。我们需要确信添加新技能或新数据源不会稀释现有技能的性能，知识库也不会随时间推移而劣化。上下文衰减在今天仍然是 LLM 非常现实的问题。
- 爆炸半径 — 这是我们内部的说法，指检索到的信息如何被范围（时间、上下文、优先级）所限定。越来越明显的是，单纯的检索不足以实现良好的记忆，我们需要开发更好的方法来灌输关于哪些信息更重要的常识。
- 杀手级用例 — 这是最关键的一点。尽管围绕上下文图谱和公司大脑有很多炒作，但这些产品明确的商业案例和投资回报率仍在探索之中。各项指标尚未跟上技术和愿景的步伐。目前来看效果尚可的几个新兴假设：
- 检索信息 — 这是 Glean 最初的杀手级用例。在分散的数据集和各类事实来源中查找信息。
- 自动化工作流 — 让智能体完成重复、枯燥的任务，并随着时间推移让它们承担越来越多的此类任务。
- 成本和延迟的节省 — 记忆层和上下文层已被证明可将 token 成本降低约 60-90%。
- 更好的智能体表现 — 让你的智能体完成原本无法完成的任务。
这些正是我们投入时间解决的问题。如果你正在构建或思考自己的"大脑"，我们很乐意与你交流！
## 相关链接
- [Sam Z Liu](https://x.com/samzliu)
- [@samzliu](https://x.com/samzliu)
- [3.5K](https://x.com/samzliu/status/2080210797465379147/analytics)
- [reverse information paradox](https://x.com/satyanadella/article/2076323181154230284)
- [right context](https://alexzhang13.github.io/blog/2026/harness/)
- [Gbrain](https://github.com/garrytan/gbrain)
- [@obsdmd](https://x.com/@obsdmd)
- [@Letta_AI](https://x.com/@Letta_AI)
- [@honchodotdev](https://x.com/@honchodotdev)
- [@EngramLab](https://x.com/@EngramLab)
- [@braintrust](https://x.com/@braintrust)
- [@raindrop_ai](https://x.com/@raindrop_ai)
- [@EntireHQ](https://x.com/@EntireHQ)
- [@mintlify](https://x.com/@mintlify)
- [@appliedcompute](https://x.com/@appliedcompute)
- [@PrimeIntellect](https://x.com/@PrimeIntellect)
- [@NotionHQ](https://x.com/@NotionHQ)
- [@clickup](https://x.com/@clickup)
- [@AirbyteHQ](https://x.com/@AirbyteHQ)
- [@Glean](https://x.com/@Glean)
- [@PromptQL](https://x.com/@PromptQL)
- [@viktor__com](https://x.com/@viktor__com)
- [@getlindy](https://x.com/@getlindy)
- [Claude Tag](https://x.com/ashwingop/status/2069814177624121469)
- [@joinstashAI](https://x.com/@joinstashAI)
- [@sentra_app](https://x.com/@sentra_app)
- [@hyperspell](https://x.com/@hyperspell)
- [@supermemory](https://x.com/@supermemory)
- [@hydra_db](https://x.com/@hydra_db)
- [@pinecone](https://x.com/@pinecone)
- [@ExaAILabs](https://x.com/@ExaAILabs)
- [@composio](https://x.com/@composio)
- [hosted a company brain and memory](https://x.com/samzliu/status/2077482285960733068?s=20)
- [Context rot](https://www.trychroma.com/research/context-rot)
- [retrieval is not sufficient](https://plasticlabs.ai/blog/posts/Memory-as-Reasoning)
- [decrease token costs by ~60-90%](https://www.pinecone.io/blog/inside-askdata/)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
- [4:37 PM · Jul 23, 2026](https://x.com/samzliu/status/2080210797465379147)
- [3,531 Views](https://x.com/samzliu/status/2080210797465379147/analytics)
- [View quotes](https://x.com/samzliu/status/2080210797465379147/quotes)
---
*导出时间: 2026/7/24 00:25:26*
