๐ engagement report
@Preetiboi_Ray - 76K followers on X
Measured over 40 original posts from a 30-day window, last computed on October 21, 2026.
Engagement
A typical post picks up 0 interactions against 76K followers, an engagement rate of 0%. Measured over 40 original posts, its engagement rate beats 0% of 21,185 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 84 times each, and 0% of those impressions turn into an interaction. That is about 0.111% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 06:00 UTC, and Wednesday is the busiest day of the week. Of the 40 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 2 interactions.
Measured over 40 original posts from a 30-day window, last computed on October 21, 2026.
Compared with accounts its own size
๐'s engagement rate beats 0% of the tracked X accounts closest to it in follower count (21,185 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%, ๐ sits below the 10th percentile of the 213,098 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.147% |
| 75th percentile | 0.698% |
| 90th percentile | 2.61% |
| 99th percentile | 82.9% |
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 Wednesday 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% 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.
- 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 1174 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
- Sep 11, 2026
๐ง๐ต๐ฒ ๐ฏ๐ฒ๐๐ ๐๐ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐๐ถ๐น๐น ๐ฏ๐ฒ ๐๐ต๐ฒ ๐ฒ๐ฎ๐๐ถ๐ฒ๐๐ ๐๐ผ ๐๐๐ฒ. Advanced AI capabilities mean little if developers cannot integrate them efficiently. Infrastructure should reduce complexity rather than add another layer of friction. A strong AI ecosystem should aim for: โข Simple developer interfaces โข Reliable APIs โข Clear documentation โข Predictable execution โข Interoperability โข Transparent economic models The same principle applies to blockchain. When AI and blockchain are combined, developers should not need to understand every underlying implementation detail simply to build a useful application. @AINFTcom is part of an emerging effort to make the AI and decentralized technology intersection more practical. The biggest opportunity could be abstraction. Users should not need to understand consensus mechanisms to use an AI-powered decentralized application. Developers should not need to reinvent wallets, payments or agent permissions every time they build a new product. Good infrastructure hides complexity while preserving control. That is how technologies move from early adopters toward mainstream usage. If AI and blockchain are going to converge at scale, developer experience and user experience will matter just as much as technical capability. @AINFTcom @justinsuntron #TRONEcoStar
- Sep 11, 2026
๐ง๐ต๐ฒ ๐ป๐ฒ๐ ๐ ๐๐ฒ๐ฏ ๐บ๐ฎ๐ ๐ฏ๐ฒ ๐ฏ๐๐ถ๐น๐ ๐ณ๐ผ๐ฟ ๐ฏ๐ผ๐๐ต ๐ต๐๐บ๐ฎ๐ป๐ ๐ฎ๐ป๐ฑ ๐ฎ๐ด๐ฒ๐ป๐๐. The current internet is largely designed around human users navigating applications. AI agents introduce another category of participant: software that can discover services, interpret information and perform actions independently. That creates a need for machine-readable infrastructure. Agents need to know: โข What a service does โข How much it costs โข What permissions are required โข How to authenticate โข How to pay โข How to verify results Blockchain can potentially provide part of that infrastructure through programmable transactions, digital identities and verifiable records. @AINFTcom is part of a broader movement toward AI-native digital ecosystems where software becomes increasingly capable of interacting with decentralized services. The transition could be significant. Instead of users manually navigating dozens of interfaces, an AI agent could potentially act as an intelligent intermediary while the blockchain remains the settlement layer underneath. That does not eliminate interfaces. It changes who interacts with them. The future web could therefore be increasingly agent-driven, programmable and economically autonomous. Projects exploring this intersection today are helping define what that architecture might eventually look like. @AINFTcom @justinsuntron #TRONEcoStar
- Sep 11, 2026
๐๐ ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐๐ฝ๐น๐ฎ๐ฐ๐ฒ๐ ๐ฐ๐ผ๐๐น๐ฑ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฑ๐ถ๐ด๐ถ๐๐ฎ๐น ๐ฒ๐ฐ๐ผ๐ป๐ผ๐บ๐ถ๐ฒ๐. Imagine a marketplace where AI agents offer specialized capabilities rather than physical products. One agent provides translation. Another analyzes financial data. Another generates code. Another monitors blockchain activity. The infrastructure behind such a marketplace would need: โข Discoverable services โข Identity and authentication โข Pricing mechanisms โข Automated payments โข Reputation systems โข Dispute and verification mechanisms Blockchain can potentially provide the economic coordination layer while AI provides the services. This is one of the more interesting directions surrounding @AINFTcom. The opportunity is not necessarily to replace existing AI marketplaces. It is to explore whether decentralized ownership and programmable payments can create new models for distributing AI services. If agents can discover one another, negotiate service terms and settle payments programmatically, the marketplace itself becomes increasingly autonomous. That could eventually produce an economy where software does not just consume APIs but purchases services and generates revenue independently. The infrastructure required for that future is still developing, but the direction is worth studying carefully. @AINFTcom @justinsuntron #TRONEcoStar
- Sep 11, 2026
๐ง๐ต๐ฒ ๐๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐ป๐ฒ๐ฒ๐ฑ๐ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐ฏ๐ฒ๐ต๐ถ๐ป๐ฑ ๐๐ต๐ฒ ๐๐ฐ๐ฒ๐ป๐ฒ๐. Most users experience AI through a simple interface, but every useful AI application depends on deeper layers: models, inference, storage, data, APIs, authentication and increasingly autonomous agents. Adding blockchain introduces another infrastructure layer around ownership, payments, identity and settlement. For an AI-native ecosystem to mature, several pieces need to work together: โข Reliable intelligence โข Secure identity โข Interoperable protocols โข Efficient transactions โข Transparent economic incentives โข Developer-friendly interfaces @AINFTcom is part of the broader effort to explore what happens when AI capabilities meet decentralized digital infrastructure. This should be evaluated carefully. Not every AI workload belongs on-chain, and not every blockchain problem needs AI. The strongest architectures will use each technology where it provides a clear advantage. That pragmatic approach matters. AI can process and automate. Blockchain can coordinate and settle. Together, they can potentially support applications where intelligent software interacts with users, assets and protocols in ways that are difficult to achieve through traditional infrastructure alone. The future of AI may therefore be defined as much by its infrastructure layer as by its models. @AINFTcom @justinsuntron #TRONEcoStar
- Sep 11, 2026
๐๐ ๐ฎ๐ป๐ฑ ๐ฏ๐น๐ผ๐ฐ๐ธ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ฎ๐ฟ๐ฒ ๐ฏ๐ฒ๐๐ ๐๐ถ๐ฒ๐๐ฒ๐ฑ ๐ฎ๐ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐๐ฎ๐ฟ๐ ๐๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐. AI is exceptionally strong at processing information, generating outputs and automating decisions. Blockchain is strong at programmable settlement, digital ownership and transparent state changes. The combination creates an interesting architecture. Consider an AI agent that needs to: 1. Understand a user request 2. Select an appropriate service 3. Verify authorization 4. Pay for the service 5. Execute the transaction 6. Record the result 7. Continue operating according to predefined constraints AI can handle much of the intelligence and decision-making. Blockchain infrastructure can potentially handle parts of the identity, ownership, payment and settlement layer. That is the type of intersection that makes @AINFTcom worth following. The opportunity is not about replacing every existing AI architecture with blockchain. That would be unnecessary. Instead, the goal should be identifying the specific components where decentralized infrastructure provides genuine advantages. Good technology design is about choosing the right tool for the right problem. The future could therefore be less about โAI versus blockchainโ and more about AI operating on programmable decentralized rails where appropriate. @AINFTcom @justinsuntron #TRONEcoStar
- Sep 11, 2026
๐ข๐ป ๐ฐ๐ต๐ฎ๐ถ๐ป ๐๐ ๐ถ๐ ๐ฎ๐ฏ๐ผ๐๐ ๐บ๐ผ๐ฟ๐ฒ ๐๐ต๐ฎ๐ป ๐ฝ๐๐๐๐ถ๐ป๐ด ๐๐ ๐ผ๐ป ๐ฎ ๐ฏ๐น๐ผ๐ฐ๐ธ๐ฐ๐ต๐ฎ๐ถ๐ป. The more interesting question is how decentralized infrastructure can make AI systems more interoperable, economically connected and verifiable. AI applications traditionally operate through centralized servers and proprietary platforms. That model can be highly effective, but it also creates dependencies around access, ownership and control. Blockchain introduces another architecture where assets, transactions and permissions can be represented through programmable protocols. For AI, this could enable: โข Tokenized AI services โข On-chain ownership of digital assets โข Transparent payment flows โข Decentralized application interactions โข Programmable incentives โข New forms of AI agent coordination The @AINFTcom ecosystem sits within this emerging intersection. The strongest implementations will require careful engineering because decentralization alone does not solve every AI problem. Models still need reliable data, efficient inference, strong security and practical user experiences. Likewise, AI systems need blockchain infrastructure that can support transactions without unnecessary complexity. The opportunity lies in combining the strengths of both technologies. AI can provide intelligence and automation. Blockchain can provide programmable ownership and settlement. When those layers are designed properly, developers gain a foundation for applications that would be difficult to build using either technology independently. @AINFTcom @justinsuntron #TRONEcoStar
- Oct 21, 2026
๐ช๐ฒ๐ฏ๐ฏ ๐ด๐ฎ๐บ๐ถ๐ป๐ด ๐ป๐ฒ๐ฒ๐ฑ๐ ๐บ๐ผ๐ฟ๐ฒ ๐๐ต๐ฎ๐ป ๐ฎ ๐ฏ๐น๐ผ๐ฐ๐ธ๐ฐ๐ต๐ฎ๐ถ๐ป Games require mechanics. Some mechanics require randomness. If an application uses a centralized random number generator, players may have to trust the operator. Blockchain technology creates an opportunity to make game outcomes more transparent, but randomness must still be designed carefully. This is where VRF infrastructure becomes relevant. @WinkLink_Oracle's VRF functionality is designed to provide random values together with cryptographic verification mechanisms. Potential applications include: โข Random NFT attributes โข Randomized game events โข Player selection โข Reward distribution โข Tournament mechanisms The important principle is not simply generating a random-looking number. The application needs a mechanism where the result can be verified according to the relevant cryptographic rules. That makes randomness an infrastructure problem rather than just a game feature. As Web3 gaming evolves, services such as oracle-based VRF can help developers design more transparent and verifiable application mechanics. ๐ฅ๐ฎ๐ป๐ฑ๐ผ๐บ๐ป๐ฒ๐๐ ๐ถ๐ ๐ฎ ๐ฏ๐ฎ๐๐ถ๐ฐ ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฏ๐น๐ผ๐ฐ๐ธ ๐ณ๐ผ๐ฟ ๐บ๐ฎ๐ป๐ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ผ๐ป๐ฐ๐ต๐ฎ๐ถ๐ป ๐ฎ๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐. @WinkLink_Oracle @justinsuntron #TRONEcoStar
- Oct 21, 2026
๐๐ถ๐๐ฒ ๐ฝ๐ฟ๐ถ๐ป๐ฐ๐ถ๐ฝ๐น๐ฒ๐ ๐ ๐๐ผ๐๐น๐ฑ ๐๐ฎ๐๐ฐ๐ต ๐๐ต๐ฒ๐ป ๐ฒ๐๐ฎ๐น๐๐ฎ๐๐ถ๐ป๐ด ๐ฎ๐ป ๐ผ๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐๐๐๐๐ฒ๐บ Oracle infrastructure deserves technical analysis, not just branding. Here are five areas worth examining: 1๏ธโฃ ๐๐ฐ๐ฐ๐๐ฟ๐ฎ๐ฐ๐ Does the delivered information represent the intended underlying data? 2๏ธโฃ ๐๐ฟ๐ฒ๐๐ต๐ป๐ฒ๐๐ How frequently can the data update, and what triggers updates? 3๏ธโฃ ๐๐ถ๐๐ฒ๐ฟ๐๐ถ๐๐ Does the architecture reduce excessive dependence on one source or operator? 4๏ธโฃ ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ Can important outputs be checked or validated according to the system's design? 5๏ธโฃ ๐๐ฎ๐ถ๐น๐๐ฟ๐ฒ ๐ต๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด What happens when sources fail, markets move abnormally or data becomes unavailable? @WinkLink_Oracle's oracle infrastructure can be examined through these same engineering principles. The goal should not be to assume perfection. No complex infrastructure deserves that assumption. The goal is to understand how the system is designed, what guarantees it provides and what risks remain. That is the mindset developers and users need as blockchain applications become increasingly dependent on external information. ๐ข๐ฟ๐ฎ๐ฐ๐น๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ. ๐๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐ฑ๐ฒ๐๐ฒ๐ฟ๐๐ฒ๐ ๐ฑ๐ฒ๐ฒ๐ฝ ๐๐ฐ๐ฟ๐๐๐ถ๐ป๐. @WinkLink_Oracle @justinsuntron #TRONEcoStar
- Oct 21, 2026
๐ฆ๐บ๐ฎ๐ฟ๐ ๐ฐ๐ผ๐ป๐๐ฟ๐ฎ๐ฐ๐๐ ๐ฎ๐ฟ๐ฒ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น, ๐ฏ๐๐ ๐๐ต๐ฒ๐ ๐ฐ๐ฎ๐ป๐ป๐ผ๐ ๐ป๐ฎ๐๐ถ๐๐ฒ๐น๐ ๐ธ๐ป๐ผ๐ ๐ฒ๐๐ฒ๐ฟ๐๐๐ต๐ถ๐ป๐ด This is one of the most important concepts for anyone learning Web3. A smart contract can verify blockchain state. It can check balances. It can transfer tokens. It can enforce predefined conditions. But it cannot independently visit an exchange, query a traditional database or observe an external event. That is where an oracle becomes useful. @WinkLink_Oracle provides infrastructure designed to bridge this information gap. The architecture can be simplified into: ๐๐ ๐๐ฒ๐ฟ๐ป๐ฎ๐น ๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป โ ๐ช๐๐ก๐ธ๐๐ถ๐ป๐ธ ๐ผ๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐น๐ฎ๐๐ฒ๐ฟ โ ๐๐น๐ผ๐ฐ๐ธ๐ฐ๐ต๐ฎ๐ถ๐ป โ ๐ฆ๐บ๐ฎ๐ฟ๐ ๐ฐ๐ผ๐ป๐๐ฟ๐ฎ๐ฐ๐ ๐น๐ผ๐ด๐ถ๐ฐ This architecture allows developers to build applications that respond to more than blockchain-native information. That expands what decentralized systems can do. The oracle therefore acts as a bridge between two worlds: ๐ข๐ณ๐ณ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป and ๐ผ๐ป๐ฐ๐ต๐ฎ๐ถ๐ป ๐ฒ๐ ๐ฒ๐ฐ๐๐๐ถ๐ผ๐ป. That bridge is one of the fundamental pieces of modern Web3 infrastructure. @WinkLink_Oracle @justinsuntron #TRONEcoStar
- Oct 21, 2026
๐ข๐ป๐ฒ ๐ผ๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ, ๐บ๐ฎ๐ป๐ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ ๐๐๐ฒ ๐ฐ๐ฎ๐๐ฒ๐ The value of an oracle network becomes clearer when you look beyond a single application. Consider the possibilities: ๐ฐ ๐๐ฒ๐๐ถ Market prices can support lending, derivatives and collateral calculations. ๐ฎ ๐๐ฎ๐บ๐ถ๐ป๐ด Verifiable randomness can support transparent selection mechanisms. ๐ ๐๐ฎ๐๐ฎ ๐ถ๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป External APIs can bring selected information into blockchain applications. โ๏ธ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป Predefined contract conditions can trigger blockchain actions. This is why @WinkLink_Oracle should be understood as broader infrastructure rather than simply a price feed. Different applications have different information requirements. Yet the fundamental challenge remains similar: ๐ง๐ต๐ฒ ๐ฏ๐น๐ผ๐ฐ๐ธ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ป๐ฒ๐ฒ๐ฑ๐ ๐ฎ ๐ฟ๐ฒ๐น๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฎ๐ ๐๐ผ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐ ๐๐ถ๐๐ต ๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ฏ๐ฒ๐๐ผ๐ป๐ฑ ๐ถ๐๐ ๐ป๐ฎ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ฒ. Solving that problem creates new possibilities for developers. The future of decentralized applications depends not only on faster chains, but also on better infrastructure connecting those chains to useful information. @WinkLink_Oracle @justinsuntron #TRONEcoStar
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.
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Reading these numbers
A typical post picks up 0 interactions against 76K followers, an engagement rate of 0%. Measured over 40 original posts, its engagement rate beats 0% of 21,185 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 84 times each, and 0% of those impressions turn into an interaction. That is about 0.111% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 06:00 UTC, and Wednesday is the busiest day of the week. Of the 40 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 2 interactions.
- What is ๐'s engagement rate on X?
- ๐ (@Preetiboi_Ray) has an engagement rate of 0%, based on the median interactions across 40 original posts from the last 30 days against 75,947 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0%, ๐ sits below the 10th percentile of the 213,098 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 @Preetiboi_Ray have real engagement?
- Its engagement rate beats 0% of the tracked X accounts closest to it in follower count (21,185 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 @Preetiboi_Ray post?
- Most posts go out around 06:00 UTC, and Wednesday is its busiest day, at roughly 1.33 posts per day across the measured window.