Wall Street AI 华尔街 📈 买美股上币安 engagement report
@WallStAI_ - 186K followers on X
Measured over 11 original posts from a 30-day window, last computed on October 13, 2026.
Engagement
A typical post picks up 56 interactions against 186K followers, an engagement rate of 0.03%. Measured over 11 original posts, its engagement rate beats 38% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 38K times each, and 0.149% of those impressions turn into an interaction. That is about 20.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.73 post a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 06:00 UTC, and Tuesday is the busiest day of the week. Of the 11 posts sampled, 91% carry an image or video and 73% link out. The account's strongest tracked post pulled 1.1K interactions, about 19x its own typical post. Recurring topics include #hyperliquid, #nashnova, #pionex.
Measured over 11 original posts from a 30-day window, last computed on October 13, 2026. Recurring tags: #hyperliquid, #nashnova, #pionex.
Compared with accounts its own size
Wall Street AI 华尔街 📈 买美股上币安's engagement rate beats 38% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 9 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 9% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.03%, Wall Street AI 华尔街 📈 买美股上币安 sits above the 25th percentile of the 204,956 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 06:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 91% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 73% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 91% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 73% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 405 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Oct 11, 202619x their median
https://t.co/uJoE1dEglY
- Jul 7, 20269.2x their median
We've raised $7.5M at a $100M valuation to build the creative asset clearinghouse. Capital provided by @1kxnetwork and @bcap, with continued support from existing investors @RepublicCrypto, @animocabrands, @alumniventures, @avax, @SevenXVentures, @Sfermion_, and @solana. AI collapsed the cost of creating music, video, and media. What it hasn't solved is how talent gets discovered, matched to the right label or agency, and paid. KOR is the clearinghouse that closes that gap, built on @base.
- Jul 6, 20263.7x their median
ADI Chain is public infrastructure built to carry regulated assets. This means institutions can run their own L3 instead, with compliance set to their jurisdiction and security inherited from the L2. Scale that out, and ADI Chain becomes where regulated finance lives. https://t.co/CoOzHQrt6u
- Aug 24, 20262.8x their median
兄弟们，最近 Stable 上有个 Meme 值得盯一下，$FEFER 我先老实跟大家说，这项目之前其实已经凉过一次 老 Dev 退出，社区信心下滑，原本冲到过 880 万美元市值，最高单日链上交易量一度超过 16.7 万笔，最后还是慢慢沉寂 为什么我要聊这个呢？ 因为，现在最大的变化来了，我正在观察 新团队 @savefefer 接手，直接做 CTO，通过 @coinsdot 在 Stable 上重新发行 FEFER 我在看老持币者有没有被直接丢掉 新币按照原 FEFER 持仓地址进行空投，不用填表，也不用手动 Claim，原社区可以直接拿到新币 这对 Meme 来说其实挺重要，因为 Meme 真正的资产从来不只是一个合约，核心还是社区和共识 现在$FEFER 还处在重建初期，后面能不能重新做起来，主要看团队执行力，社区能不能重新聚起来，还有流动性能不能跟上 所以我不能保证这次一定能起飞 一个已经死过一次、现在重新 CTO 的 Meme，我是放观察名单 新 FEFER CA在这儿 0xDEeE8f25fe3B5C33AeF78637278ACBFF23EeBFa6 不是投资建议DYOR
- Sep 10, 20262.4x their median
Most people in prediction markets are not short on information. They are short on a way to decide which information actually deserves a position. I used to treat every headline as a potential edge. Elections, rate paths, ETF flows, a senator’s wording, a sudden liquidity vacuum. After a while the feed stops being intelligence. It becomes weather. You feel informed and still late. That is the part of this market I keep coming back to. Prediction markets are good at pricing a question. They are much worse at telling you why this question is live now, and what kind of strategy should sit on top of it. A signal is not a strategy. A prediction is not a strategy either. A strategy is a repeatable way of reading signals, sizing uncertainty, and updating when the world changes. That is why Flux clicked for me. @PrismNetwork_io is building #PrismNetwork as an AI Strategy Account Network — less “another dashboard of takes,” more an intelligence layer that connects market information, AI analysis, and strategy. Flux is the surface you actually live in. You can move between Event Intelligence, Trend Intelligence, and Frontier / liquidity-style views, see confidence and risk next to the thesis, and follow strategy accounts instead of chasing isolated calls. What I like is not that AI “has an opinion.” Plenty of models have opinions. What matters is whether the logic is inspectable: what the strategy is watching, how confident it is, how it treats risk, and whether the same lens can be reused when the next event hits. If you only collect more signals, you get noise at higher resolution. If you collect strategies, you start to see the market as a set of competing ways of thinking. I am not looking for a machine that trades for me in the dark. I am looking for a cleaner way to answer two questions before I act: Which signals are worth my attention today? And how do those signals become a strategy I can actually stand behind? That is the job Flux is trying to do. Explore it here: https://t.co/qwKbMxgeu0 #PrismNetwork @PrismNetwork_io #web3 #AI
- Sep 6, 20262.3x their median
兄弟们，我这两天终于把 @MossAI_Official 的 Agent Marketplace 跑了一遍 我主要是好奇，如果一个人的交易策略本身不错，但他不会写代码，能不能把“自动交易”这套东西直接做出来？ Moss 平台让这件事实现！ 把交易逻辑用自然语言写进去，AI 会帮你拆成具体的策略条件，参数也可以自己改 确认之后创建 Agent，它就能持续运行 我做测试的时候，刻意没有用特别复杂的策略 因为我更想看最基础的一套流程能不能跑通 把想法和策略做成Agent ，然后让他自动执行 这部分体验下来确实比自己从零写代码轻松很多 然后就是 Marketplace市场 这里的玩法跟传统跟单挺不一样 你看到一个 Agent，不需要照着它每次开仓平仓，而是可以直接 铸造它的 Agent Share Moss 就像一个策略市场，有人负责研究策略、创建 Agent，有人负责寻找自己认可的策略，中间还有链上记录可以拿来验证 当然，看到收益不错的 Agent 也别直接冲 历史表现只能用来研究，不能当成未来收益保证 我目前还是小额测试阶段 先把不同 Agent 的逻辑、执行记录和实际表现都看一遍，再决定哪些值得继续研究 有兴趣的可以到官网看看： https://t.co/VjW6NkLEW7
- Sep 4, 20262.3x their median
最近看到不少项目都在讲 AI Agent，会聊天、会发推、会调用 API，甚至还能自己在链上转账 但这些东西其实都不算特别难，真正难的问题反而是，如果把背后的人拿走，它还能不能继续工作？ 我自己判断一个 Agent 会不会真的成为链上经济主体，有一个很简单的测试，就是关掉运营者电脑，撤掉 Keeper，把私钥从人手里拿走，再停止人工唤醒，然后放 24 小时 如果它还能自己醒过来、自己付 gas、自己调用模型、自己继续执行任务，我觉得这才算开始接近真正的 Agent，否则本质上还是一个自动化 Bot 这也是我为什么会重新去看 @ritualnet Ritual 的重点并不是再做一个“AI + Blockchain”的展示层，而是试图把计算、推理、钱包、密钥和执行放到同一套可验证的环境里 因为 Agent 一旦开始自己花钱，问题马上就变了。x402 可以解决支付怎么发生，ERC-8004 可以解决这个 Agent 是谁，但还有一个更麻烦的问题，就是为什么这笔钱应该花？ 比如一个 Agent 去买数据、租算力或者执行交易，链上可以看到钱确实转出去了，却不一定知道它到底是按照原本规则做出的决定，还是被错误信息、恶意输入甚至 Prompt Injection 带偏了 所以我越来越觉得，Agent 经济真正缺的可能不是另一套支付系统，而是可验证执行 你得知道它做了什么，更重要的是，还得知道它为什么这么做，以及这个决定是不是符合预先设定的规则 Ritual 测试网阶段已经跑过 5500 万级别的区块、4000+ 自主唤醒 Agent 和 200+ 社区 dApp 当然，数字本身不是最重要的，真正值得看的，是这些 Agent 有没有开始逐渐脱离人工值守 现在测试网已经结束了，接下来我反而不会太关注各种倒计时。比起喊主网上线还有多少天，我更想看 Passport、Genesis 到主网之后，Agent 到底能不能真正拥有自己的执行权和经济行为 如果机器以后真的要自己管钱包，那它首先得学会证明一件事：这笔钱，是它自己决定花的
- Aug 26, 20262.3x their median
🤖 Alright, real talk, guys AI is hungry for data that actually means something Not another scraped dataset Not another synthetic dump Real-world data that can be proven, traced, and acted on That’s exactly what DataVLT @DataVLTAI just shipped Fresh brand Fresh product stack Official site is live Here’s the simple version DataVLT is the Verifiable Data Infrastructure that sits between the messy physical world and the systems that need to trust it AI, Web3, DePIN, you name it! DVLT ONE pulls data straight from real devices DataVLT DID turns every contribution into a verifiable identity + data trail And yes, minting is free right now Result? Real-world data stops being “just data” and starts becoming something you can actually build on, verify, and execute against AI × Web3 × IoT × Real-World Data finally has a proper bridge Go poke around the new site and see the stack for yourself https://t.co/DUyO0WLeBh Hit follow on @DataVLTAI so you don’t miss what’s coming next The verifiable data layer just leveled up!
- Sep 2, 20262.2x their median
The AI industry doesn’t need more chatbots that sound smart. What I’m watching for now is much simpler: Can an AI Agent actually get work done? That’s one reason @Cortex_Network_ caught my attention. Cortex Network isn’t just trying to build a smarter model. It’s building the infrastructure for autonomous Agents to operate continuously, break down tasks, use different Skills, and coordinate with other Agents. The interesting part is what happens beyond a single prompt. Give an Agent a goal → let it execute → let other Agents collaborate when needed → verify and settle the work on-chain. OpenClaw, Agent Runtime, ACP, and modular Skills are all pieces of that stack. And once Agents can provide services, collaborate, and earn rewards on-chain, AI starts looking less like a tool you constantly operate and more like an autonomous workforce. I think that shift is much more important than another benchmark score. We’re still early, but if autonomous Agents become a real economy, the infrastructure behind them could be just as important as the AI models themselves. Definitely keeping an eye on @Cortex_Network_ #CortexNetwork #AI
- Sep 20, 20262.2x their median
兄弟们，有个细节，让我觉得 $KII 其实挺值得看的 你应该还记得，@kiichainio 之前经历过被盗，这种事情放在其他项目里，基本很容易把信心直接打穿 但Kii这个团队把局面稳住，也把信心、流动性和产品拉回来了，所以我那时也没卖掉，继续拿着 我还注意到，项目方在被盗后面的处理很不错，也让很多人开始关注这个项目 项目方持续沟通，处理被盗事件，同时也在慢慢恢复产品和流动性，这个恢复速度很快！ 再看最近的盘面 Binance Alpha 上，$KII 这几天涨幅大概不错，有25%左右，成交量也明显放大，Alpha 侧累计成交已经到了十亿级 所以我现在关注它，不只是因为涨了，也在看项目的方向 稳定币，外汇，跨境结算 再往前看，还有交易页面和智能体支付 我的理解，就是想让稳定币不只是用来转账，而是成为一个全天候在线的链上结算层 甚至让智能体也能够参与支付、交易和资金调度 所以我觉得，它肯定不只是一个普通公链，我也会继续拿着
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 56 interactions against 186K followers, an engagement rate of 0.03%. Measured over 11 original posts, its engagement rate beats 38% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 38K times each, and 0.149% of those impressions turn into an interaction. That is about 20.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.73 post a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 06:00 UTC, and Tuesday is the busiest day of the week. Of the 11 posts sampled, 91% carry an image or video and 73% link out. The account's strongest tracked post pulled 1.1K interactions, about 19x its own typical post. Recurring topics include #hyperliquid, #nashnova, #pionex.
- What is Wall Street AI 华尔街 📈 买美股上币安's engagement rate on X?
- Wall Street AI 华尔街 📈 买美股上币安 (@WallStAI_) has an engagement rate of 0.03%, based on the median interactions across 11 original posts from the last 30 days against 185,537 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.03%, Wall Street AI 华尔街 📈 买美股上币安 sits above the 25th percentile of the 204,956 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @WallStAI_ have real engagement?
- Its engagement rate beats 38% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @WallStAI_ post?
- Most posts go out around 06:00 UTC, and Tuesday is its busiest day, at roughly 0.73 posts per day across the measured window.