Nebula Block engagement report
@nebulablockdata - 95K followers on X
Measured over 21 original posts from a 30-day window, last computed on August 25, 2026.
Engagement
A typical post picks up 0 interactions against 95K followers, an engagement rate of 0%. Measured over 21 original posts, its engagement rate beats 0% of 6,874 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 581 times each, and 0% of those impressions turn into an interaction. That is about 0.609% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.7 post a day over the last 30 days, with activity on roughly 63% of days. Most posts go out around 04:00 UTC, and Monday is the busiest day of the week. Of the 21 posts sampled, 95% carry an image or video and 81% link out. The account's strongest tracked post pulled 4 interactions. Recurring topics include #enterpriseai, #aiagents, #agenticai.
Measured over 21 original posts from a 30-day window, last computed on August 25, 2026. Recurring tags: #enterpriseai, #aiagents, #agenticai.
Compared with accounts its own size
Nebula Block's engagement rate beats 0% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 6 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 0% 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%, Nebula Block sits below the 10th percentile of the 66,393 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.016%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.5% |
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 04:00 UTC, and Monday 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 | 95% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 81% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 95% 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.
- 81% 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 587 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
- Aug 18, 2026
LLM vs. RAG vs. Agent Evals: What Are You Actually Measuring? 🧐 Not all AI systems should be evaluated the same way. - An LLM eval may focus on safety, instruction following, and accuracy. - A RAG system needs to prove retrieval quality and answer faithfulness. - A coding agent must be evaluated on tool calling, planning, retries, and whether it actually resolves the task. - A multi-agent system adds another layer: coordination, role adherence, hand-offs, and overall task success. The key takeaway: as AI systems become more autonomous, evaluation needs to move beyond “Is the answer correct?” to “Did the system complete the task reliably?” #AI #AIAgents #RAG #LLM #AgenticAI #AIEvaluation #EnterpriseAI #NebulaBlock
- Aug 7, 2026
AI agents shouldn’t just make decisions. They should know when to make them and when to ask for help. 👍 A reliable AI agent workflow needs more than automation. It needs confidence thresholds, human oversight, and action logging built into the decision loop. 🔹 High confidence (>85%) → Execute & log the action 🔹 Low confidence (<85%) → Draft the action & notify a human 🔹 Human approval → Approve or modify before execution This is what Human-in-the-Loop AI looks like in practice: letting agents move fast while keeping people in control of critical decisions. As AI agents move from answering questions to taking real-world actions, trust, transparency, and accountability become just as important as intelligence. #AIAgents #AgenticAI #HumanInTheLoop #AIInfrastructure #EnterpriseAI #ResponsibleAI #AI #Automation #NebulaBlock
- Jul 21, 2026
🚀 Qwen 3.8-Max-Preview is now live on Nebula Block! Experience Alibaba's latest preview model through a single OpenAI-compatible API. ⚡ Fast deployment 🧠 Advanced reasoning 💻 Coding & development 📚 Long-context tasks Start building today: https://t.co/pFmubJHhNq #Qwen #AlibabaCloud #LLM #AI #GenerativeAI #Developers #NebulaBlock
- Jul 18, 2026
Kimi K3 is now live on Nebula Block 🤝 Moonshot AI's flagship model with a 1M-token context window, built for long-horizon agentic coding and tool use, with native vision input and always-on reasoning Pricing 💲 $3.00/M input 💲 $15.00/M output 💾 $0.30/M cache read Find your at: https://t.co/UmCCL79ZZX
- Aug 13, 2026
🚨 Shadow AI: The Security Blind Spot You Can’t Ignore Simply banning AI tools in the enterprise doesn’t stop their use it just pushes employees to use unsanctioned, personal accounts. When sensitive data, source code, or customer records flow through untracked LLM API calls, the risks become real. In this latest post, NebulaBlock breaks down: 🔍 Why Shadow AI is exploding across enterprise workflows. 🛠️ 5 layers to detect rogue LLM API calls (from DNS logs to secret scanning). 🛡️ How to build a sustainable governance program that enables productivity without compromising security. 📌 Read the full article on Nebula Block: https://t.co/eFVlEVwRiI How is your team handling the balance between AI adoption and data security? Let’s connect in the comments! 👇 ---- #ShadowAI #EnterpriseAI #Cybersecurity #DataGovernance #NebulaBlock #LLM #API #CISO
- Aug 10, 2026
Many workflows look simple on the surface but actually require complex multi-agent coordination behind the scenes. We break down 12 real-world examples from enterprise environments. 👇👇 🔗https://t.co/h4lgTDJqeb ---- #AIAgents #AgenticAI #AIInfrastructure #EnterpriseAI #ResponsibleAI #AI #Automation #NebulaBlock
- Aug 6, 2026
A great AI agent doesn't just complete tasks — it should feel like an actual member of the team. As AI agents become embedded in enterprise workflows, simply generating answers isn't enough. The best agents collaborate like trusted teammates by: ✅ Sharing context – They understand your team's history, terminology, and workflows without constant prompting. ✅ Being proactive – They identify opportunities, flag issues, and take action before someone asks. ✅ Knowing when to ask for help – They recognize uncertainty, hand decisions back to humans, and keep people in control. These principles transform AI from a tool you use into a teammate you can rely on. 📖 Read the full blog for practical design strategies and real-world examples. https://t.co/lO2Pw82NJo
- Jul 30, 2026
The illustration for Mark Zuckerberg's WSJ op-ed shows a head locked in an iron cage, with data streaming through the bars and turning into birds flying free. The message: intelligence always finds a way out. The real question isn't whether to stop it — it's who gets to open the cage, and how far. 🎯 The valid point Concentrated power — whether in a few AI labs or a global regulator — carries its own risks. History shows centralized control can slow innovation and hand too much power to a few gatekeepers. Making AI tools widely available also keeps markets competitive and stops any single company or country from setting all the rules. 🧬 The gaps Motive: Meta has a direct commercial stake in open models like Llama. That doesn't make the argument wrong, but it's worth reading with that in mind. Where's the line: Even Dario Amodei of Anthropic, a supporter of open models, distinguishes between models below a certain capability level and ones that cross into high-risk territory like biology or cybersecurity. Zuckerberg's piece says little about what happens if openness itself becomes the risk. On the other side, calls for global "slow down" agreements face a real problem too: hard to enforce, and countries who follow the rules may just fall behind while others don't. 🤝 The cage isn't neutral Framing control as a cage to escape already takes a side — it treats any restriction as suppression, not protection. Someone else could redraw the same image: not a prison, but a fence while we're still figuring out what we've built. ✅ Bottom line These aren't really four separate camps. Most sides agree some safety is needed — they disagree on where the line sits and who decides it. The real question isn't "open vs. closed," but whether we have the tools to tell a useful technology apart from an irreversible risk. Until that's clearer, everyone's just redrawing the cage to fit their own beliefs. How about you? What is your opinion?
- Jul 14, 2026
Government AI isn't just about model performance. Sovereign compute is becoming a critical part of public-sector AI deployments. 📍Here's why. https://t.co/GO8sPsxr2H #CanadianAI #Cloud #AI https://t.co/XeK7ccajNL
- Jul 8, 2026
Building AI shouldn't require 5 different cloud providers. Learn how to build a complete Canadian AI stack with:
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 0 interactions against 95K followers, an engagement rate of 0%. Measured over 21 original posts, its engagement rate beats 0% of 6,874 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 581 times each, and 0% of those impressions turn into an interaction. That is about 0.609% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.7 post a day over the last 30 days, with activity on roughly 63% of days. Most posts go out around 04:00 UTC, and Monday is the busiest day of the week. Of the 21 posts sampled, 95% carry an image or video and 81% link out. The account's strongest tracked post pulled 4 interactions. Recurring topics include #enterpriseai, #aiagents, #agenticai.
- What is Nebula Block's engagement rate on X?
- Nebula Block (@nebulablockdata) has an engagement rate of 0%, based on the median interactions across 21 original posts from the last 30 days against 95,368 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0%, Nebula Block sits below the 10th percentile of the 66,393 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 @nebulablockdata have real engagement?
- Its engagement rate beats 0% of the tracked X accounts closest to it in follower count (6,874 accounts), which puts it in the bottom 10% for its size 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 @nebulablockdata post?
- Most posts go out around 04:00 UTC, and Monday is its busiest day, at roughly 0.7 posts per day across the measured window.