Nikki Siapno engagement report
@NikkiSiapno - 177K followers on X
Measured over 13 original posts from a 30-day window, last computed on October 18, 2026.
Engagement
A typical post picks up 127 interactions against 177K followers, an engagement rate of 0.072%. Measured over 13 original posts, its engagement rate beats 53% of 21,185 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 11K times each, and 1.13% of those impressions turn into an interaction. That is about 6.35% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.77 post a day over the last 30 days, with activity on roughly 70% of days. Most posts go out around 13:00 UTC, and Monday is the busiest day of the week. Of the 13 posts sampled, 85% carry an image or video and 92% link out. The account's strongest tracked post pulled 2.1K interactions, about 17x its own typical post.
Measured over 13 original posts from a 30-day window, last computed on October 18, 2026.
Compared with accounts its own size
Nikki Siapno's engagement rate beats 53% of the tracked X accounts closest to it in follower count (21,185 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 46% 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 0.072%, Nikki Siapno sits above the 25th percentile of the 212,825 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.697% |
| 90th percentile | 2.61% |
| 99th percentile | 83.0% |
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 13:00 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 | 85% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 92% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 85% 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.
- 92% 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 1195 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 28, 202617x their median
๐ ๐๐ฃ ๐๐ ๐๐ฃ๐. An ๐๐ฃ๐ defines how software systems communicate through specific endpoints, requests, and responses. It gives applications a structured way to access data or trigger functionality in another system. ๐ ๐๐ฃ gives AI applications a standardized way to discover and use external tools, data, and resources. Instead of building custom integrations for every AI client, an MCP server exposes capabilities through a common protocol. APIs expose functionality to software. MCP standardizes how AI applications discover and interact with that functionality. But once AI sits behind an API, the request-response model gets harder. Inference might take longer than the request can stay open. It might fail halfway through. It might need to be retried. That changes how the API itself should be designed. Oracleโs guide breaks down how to design for that with asynchronous jobs, workers, durable state, and predictable API contracts. ๐ฅ๐ฒ๐ฎ๐ฑ ๐๐ต๐ฒ ๐ด๐๐ถ๐ฑ๐ฒ โ https://t.co/YIJ4uDSgRA What else would you add? โโ ๐ Thanks to @OracleDevs for sponsoring this post. โ Follow me ( Nikki Siapno ) to improve at AI and system design.
- Oct 16, 202612x their median
๐ ๐๐ฃ ๐๐ ๐๐ฃ๐ ๐๐ ๐ฆ๐ธ๐ถ๐น๐น๐. An ๐๐ฃ๐ defines how software systems communicate through specific endpoints, requests, and responses. It gives applications a structured way to access data or trigger functionality in another system. ๐ ๐๐ฃ (Model Context Protocol) standardizes how AI applications discover and interact with external tools, data, and resources. Instead of building custom integrations for every AI client, MCP servers expose capabilities through a common protocol that agents can use. ๐ฆ๐ธ๐ถ๐น๐น๐ package reusable instructions, workflows, and resources that guide agents through specific tasks. Rather than providing new connections to external systems, they give agents task-specific procedures and know-how they can load when needed. Together, they help agents connect to systems and use those capabilities to complete tasks. But building all these integrations and workflows from scratch takes time. Precisely AI Studio helps builders build faster with ๐ฟ๐ฒ๐ฎ๐ฑ๐-๐บ๐ฎ๐ฑ๐ฒ ๐๐ ๐ฎ๐ด๐ฒ๐ป๐๐, ๐ฎ๐ฝ๐ฝ๐, ๐ฎ๐ป๐ฑ ๐๐ธ๐ถ๐น๐น๐ ๐ฐ๐ผ๐ป๐ป๐ฒ๐ฐ๐๐ฒ๐ฑ ๐๐ต๐ฟ๐ผ๐๐ด๐ต ๐ ๐๐ฃ. Builders can explore and test them using sample data, then bring agents and skills into their existing AI stack. Explore Precisely AI Studio โ https://t.co/Foa98LDoxs What else would you add? โโ โป๏ธ Repost to help other learn AI. ๐ Thanks to @PreciselyData for sponsoring this post. โ Follow me ( Nikki Siapno ) to improve at AI and system design.
- Oct 13, 20267.9x their median
๐๐ง๐ง๐ฃ, ๐๐ง๐ง๐ฃ ๐ฎ, ๐๐ง๐ง๐ฃ ๐ฏ: ๐๐ผ ๐๐ผ๐ ๐ธ๐ป๐ผ๐ ๐๐ต๐ฒ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐? HTTP now powers complex modern applications, but it started as a simple protocol for retrieving documents. ๐๐ง๐ง๐ฃ/๐ฌ.๐ต only supported a single GET request with no headers or status codes; just raw HTML from the server. It was simple and fast, but lacked structure and scalability. ๐๐ง๐ง๐ฃ/๐ญ.๐ฌ introduced headers, status codes, and content types, making the web more structured and extensible, but required a new TCP connection for every request, which didnโt scale well. ๐๐ง๐ง๐ฃ/๐ญ.๐ญ added persistent connections, caching, and chunked transfer, making web interactions more efficient, but still suffered from head-of-line blocking and connection overhead at scale. ๐๐ง๐ง๐ฃ๐ฆ is HTTP layered over TLS, providing encryption, integrity, and authentication, securing communication over the internet, but adds handshake overhead and operational complexity. ๐๐ง๐ง๐ฃ/๐ฎ introduced multiplexing, binary framing, and header compression, allowing multiple requests over a single connection, improving performance, but still relies on TCP and suffers from transport-level blocking. ๐๐ง๐ง๐ฃ/๐ฏ runs over QUIC (UDP) with built-in TLS 1.3, enabling independent streams, faster handshakes, and reduced latency, but introduces more complexity in infrastructure and network handling. From per-request connections โ to multiplexing over TCP โ to QUIC and independent streams. Thatโs the evolution of HTTP. ๐ If you want to learn more, dive deeper here: https://t.co/HsKI9atEuR What else would you add?
- Jul 20, 20267.4x their median
SSO vs OAuth vs OIDC vs SAML ๐ฆ๐ฆ๐ข is a user experience, not a protocol. It lets users log in once and access multiple apps without re-authenticating; providing seamless access across tools. It relies on protocols like SAML or OIDC. ๐ข๐๐๐๐ต is for authorization. It lets apps access user data or services without sharing credentials. It controls what an app can access, not identity. ๐ข๐๐๐ is an authentication layer on top of OAuth 2.0. It verifies user identity and provides user info via ID tokens (usually JWTs). Itโs the standard for login + identity in modern apps. ๐ฆ๐๐ ๐ is an older, XML-based authentication protocol used for enterprise SSO. Itโs still widely used in legacy and enterprise systems, though newer applications increasingly adopt OIDC. Itโs powerful but more complex than OIDC. If you remember one thing: OAuth = access, OIDC = identity, SAML = enterprise SSO, SSO = the experience. What else would you add? โโ ๐๐ฟ๐ฒ ๐๐ผ๐ ๐๐๐ฟ๐ฒ ๐๐ผ๐๐ฟ ๐ฟ๐ฒ๐๐๐บ๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ ๐๐ง๐ฆ ๐๐ฐ๐ฎ๐ป๐? Check by using Kickresumeโs ATS Resume checker. Check your ATS score here โ https://t.co/pvpaXVnW7f Thanks to our partner Kickresume who helps keep our content free. โโ โป๏ธ Repost to help others learn and grow. โ Follow me ( Nikki Siapno ) to improve at system design.
- Sep 10, 20266.2x their median
๐๐ผ๐ด๐ ๐๐ ๐ ๐ฒ๐๐ฟ๐ถ๐ฐ๐ ๐๐ ๐ง๐ฟ๐ฎ๐ฐ๐ฒ๐. Logs, metrics, and traces can all point to the same problem, but they show you that problem from completely different perspectives. ๐๐ผ๐ด๐ = โ๐ช๐ต๐ฎ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ฒ๐ฑ?โ When something happens inside an application, logs capture a timestamped record of the event, from errors and warnings to requests and state changes. That detailed context helps you understand exactly what happened at a particular point in time. ๐ ๐ฒ๐๐ฟ๐ถ๐ฐ๐ = โ๐๐ผ๐ ๐ถ๐ ๐๐ต๐ฒ ๐๐๐๐๐ฒ๐บ ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ป๐ด?โ Rather than recording individual events, metrics turn system behavior into numerical measurements over time, such as request rate, error rate, latency, CPU usage, and memory consumption. This makes patterns, trends, and abnormal behavior much easier to spot. ๐ง๐ฟ๐ฎ๐ฐ๐ฒ๐ = โ๐ช๐ต๐ฎ๐ ๐ฝ๐ฎ๐๐ต ๐ฑ๐ถ๐ฑ ๐๐ต๐ฒ ๐ฟ๐ฒ๐พ๐๐ฒ๐๐ ๐๐ฎ๐ธ๐ฒ?โ Across a distributed system, a single operation can pass through many services. Traces connect spans from each step into an end-to-end journey, showing where time was spent, which services were involved, and where errors or latency appeared. But during an incident, seeing the signals is only part of the problem. The hard part is connecting them with deploys, commits, dependencies, and past incidents ๐๐ผ ๐ณ๐ถ๐ด๐๐ฟ๐ฒ ๐ผ๐๐ ๐๐ต๐ฎ๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ฏ๐ฟ๐ผ๐ธ๐ฒ ๐ฎ๐ป๐ฑ ๐๐ต๐. Thatโs where agentic root cause analysis can help. Investigations by @incident_io starts working the moment an incident is declared, reasoning across that context to build a hypothesis backed by evidence and identify where to look next. ๐๐ป๐๐๐ฒ๐ฎ๐ฑ ๐ผ๐ณ ๐๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐ณ๐ฟ๐ผ๐บ ๐๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต and digging for answers, ๐๐ผ๐ ๐ฎ๐ฟ๐ฟ๐ถ๐๐ฒ ๐๐ถ๐๐ต ๐ฎ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐๐ต๐ฒ๐ผ๐ฟ๐. Check it out โ https://t.co/45CkvWsPva What else would you add? โโ โป๏ธ Repost to help others learn and grow. ๐ Thanks to incident .io for sponsoring this post. โ Follow me ( Nikki Siapno ) to improve at AI and system design.
- Aug 9, 20265.7x their median
8 Authentication methods developers should know: ๐ญ) ๐๐ฃ๐ ๐ธ๐ฒ๐๐ โณ A static key sent with each request. Simple for app identification, usage tracking, and rate limits, but risky if leaked. ๐ฎ) ๐๐ฎ๐๐ถ๐ฐ ๐ฎ๐๐๐ต๐ฒ๐ป๐๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป โณ A username and password sent with each request. Easy to implement, but only safe over HTTPS and mostly used in simple or legacy systems. ๐ฏ) ๐๐ฒ๐ฎ๐ฟ๐ฒ๐ฟ ๐๐ผ๐ธ๐ฒ๐ป๐ โณ A token that grants access to whoever holds it. Flexible, but stolen tokens can be used until they expire or are revoked. ๐ฐ) ๐๐ฆ๐ข๐ก ๐ช๐ฒ๐ฏ ๐ง๐ผ๐ธ๐ฒ๐ป๐ (๐๐ช๐ง๐) โณ Signed tokens that carry claims like user ID, roles, and expiration. Good for stateless auth, but harder to revoke. ๐ฑ) ๐ข๐๐๐๐ต ๐ฎ.๐ฌ โณ A framework for delegated access. Lets apps access resources on a userโs behalf without sharing the userโs password. ๐ฒ) ๐ข๐ฝ๐ฒ๐ป๐๐ ๐๐ผ๐ป๐ป๐ฒ๐ฐ๐ โณ An identity layer on top of OAuth 2.0. OAuth grants access; OIDC confirms who the user is. ๐ณ) ๐๐ ๐๐ โณ A request signature created with a shared secret. Useful for proving the request came from a trusted client and was not changed. ๐ด) ๐ ๐๐๐๐ฎ๐น ๐ง๐๐ฆ โณ Both client and server authenticate with certificates. Strong for service-to-service APIs, but heavier to operate. The best authentication method depends on what you need to prove. Identity, access, integrity, and machine trust are different problems with different solutions. What else would you add? โโ ๐๐ฟ๐ฒ ๐๐ผ๐ ๐๐๐ฟ๐ฒ ๐๐ผ๐๐ฟ ๐ฟ๐ฒ๐๐๐บ๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ ๐๐ง๐ฆ ๐๐ฐ๐ฎ๐ป๐? Check by using Kickresumeโs ATS Resume checker. Check your ATS score here โ https://t.co/5leMTB0Yg4 Thanks to our partner Kickresume who keep our content free. โโ ๐ Save for later. โ Follow me ( @NikkiSiapno ) to improve at system design.
- Oct 8, 20265.1x their median
๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ ๐๐ ๐๐๐ฏ๐ฒ๐ฟ๐ป๐ฒ๐๐ฒ๐. Docker is a containerization platform that solves the โ๐ถ๐ ๐๐ผ๐ฟ๐ธ๐ ๐ผ๐ป ๐บ๐ ๐บ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒโ problem. It builds images, runs containers, and primarily runs them on a single host. Applications are packaged with everything they need and run as lightweight, isolated containers. The focus is on running software consistently with simple primitives like images and containers. ๐๐๐ฏ๐ฒ๐ฟ๐ป๐ฒ๐๐ฒ๐ is a cluster orchestration system. It manages containers across multiple machines using a control plane that schedules and maintains containerized workloads. It works with Pods and reconciles actual state to desired state. The focus is on operating software at scale with capabilities like self-healing, autoscaling, and rolling updates. Kubernetes doesnโt create containers. It orchestrates them. Docker runs containers. Kubernetes keeps them running. What else would you add? โป๏ธ Repost to help other learn and grow. ๐พ Save for later.
- Aug 10, 20264.9x their median
Postgres vs MySQL ๐ฃ๐ผ๐๐๐ด๐ฟ๐ฒ๐ is built as a single, integrated system, and that design choice shapes how everything else works. Data is stored in heap tables, where multiple row versions coexist. Updates create new versions, while old ones remain until cleaned up (VACUUM). MVCC is built into the data model, enabling advanced indexing and extensibility. ๐ ๐๐ฆ๐ค๐ is built around a pluggable storage engine architecture. The SQL layer sits on top, while InnoDB handles storage. Old row versions are kept in undo logs and reconstructed when needed. Data is organised as a clustered index, with the primary key defining physical layout. Which database do you prefer? โโ ๐๐ฟ๐ฒ ๐๐ผ๐ ๐๐๐ฟ๐ฒ ๐๐ผ๐๐ฟ ๐ฟ๐ฒ๐๐๐บ๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ ๐๐ง๐ฆ ๐๐ฐ๐ฎ๐ป๐? Check by using Kickresumeโs ATS Resume checker. Check your ATS score here โ https://t.co/kToDNuJAki Thanks to our partner Kickresume who keeps our content free. โโ โป๏ธ Repost to help others databases. โ Follow me ( @NikkiSiapno ) to improve at system design.
- Aug 5, 20264.7x their median
8 RAG Architectures Developers Should Know. RAG isn't one fixed architecture. It's a spectrum of retrieval approaches that determine what gets searched, how results are ranked, and how much control the LLM has over retrieval. Knowing these architectures is important. But understanding how to evaluate and choose the right one for your application is just as important. Learn more about that here: https://t.co/eyIBu7mNEc 1. ๐ก๐ฎ๐ถ๐๐ฒ ๐ฅ๐๐ โณ Retrieves the top matching chunks from a vector store and passes them directly to the LLM. 2. ๐๐๐ฏ๐ฟ๐ถ๐ฑ ๐ฅ๐๐ โณ Combines keyword search with vector search, which helps when both exact terms and semantic meaning matter. 3. ๐ฅ๐ฒ๐ฟ๐ฎ๐ป๐ธ๐ฒ๐ฑ ๐ฅ๐๐ โณ Retrieves a larger candidate set first, then scores and reorders chunks so the most relevant context gets used. 4. ๐ ๐๐น๐๐ถ-๐ค๐๐ฒ๐ฟ๐ ๐ฅ๐๐ โณ Rewrites one question into multiple related queries to improve recall across different phrasings and document matches. 5. ๐๐ถ๐ฒ๐ฟ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐ฐ๐ฎ๐น ๐ฅ๐๐ โณ Searches broad document sections first, then narrows into smaller chunks for more precise retrieval. 6. ๐๐ฟ๐ฎ๐ฝ๐ต ๐ฅ๐๐ โณ Represents knowledge as entities and relationships, which helps when the answer depends on connected facts. 7. ๐๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐๐ถ๐๐ฒ ๐ฅ๐๐ โณ Checks whether retrieved context is useful and adjusts the retrieval process when the first results are weak. 8. ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฅ๐๐ โณ Lets the LLM plan retrieval steps, choose tools or sources, and reason through multiple searches before answering. Here's a simple mental model: Naive = fast and simple. Hybrid + Reranked = better precision. Multi-Query + Hierarchical = broader coverage. Graph = connected facts. Corrective = self-checking. Agentic = autonomous reasoning. RAG helps models retrieve knowledge. Memory systems help them retain it. If you want to learn how production AI evolves beyond RAG into stateful memory systems, here's a great deep dive โ https://t.co/MMG8YivtUW What else would you add? โโ โป๏ธ Repost to help others learn RAG. ๐ Thanks to @Oracle for sponsoring this post. โ Follow me ( @NikkiSiapno ) to improve at AI engineering.
- Jul 10, 20264.0x their median
I wrote 35 articles for 35 system design concepts: 1) gRPC: https://t.co/QwgTXr1N9z 2) Redis: https://t.co/YmMKgSvyVk 3) Event-driven architecture: https://t.co/QNUuf1JOy7 4) OAuth: https://t.co/TgQdzGO2Kr 5) ACID vs BASE: https://t.co/a7nOyylUxk 6) Microservices: https://t.co/1CpY04nNxb 7) Rate limiting: https://t.co/wr0UAh4sJm PS - if you want a structured path, get my free 142-page System Design Handbook when you join my free weekly newsletter โ https://t.co/rKYuAkfCSL 8) Observability: https://t.co/VjfECfyB9d 9) System design quality attributes: https://t.co/v9WJoUPevt 10) CAP theorem: https://t.co/ONwpduTOD1 11) Idempotency: https://t.co/2sItwlz1oe 12) REST APIs: https://t.co/GSjtaCU2Y8 13) Sync vs Async: https://t.co/ZXCxP7XrAb 14) Network protocols: https://t.co/tx7MlZQIwE 15) Change Data Capture (CDC): https://t.co/tgwwoTitCA 16) CI/CD pipelines: https://t.co/SM2YvhioIX 17) SSO (single sign-on): https://t.co/sNCN5cdt6V 18) JWT: https://t.co/Kuv7DAj6B9 19) Circuit breakers: https://t.co/1U81SrBokz 20) Health checks vs heartbeats: https://t.co/r5SalP6CCh 21) API gateway vs load balancer vs reverse proxy: https://t.co/Tg3EhT60tU 22) HTTPS: https://t.co/wc3CQOsmPS 23) Load balancing algorithms: https://t.co/VCLCKOZzni 24) Database caching: https://t.co/23QdZATj2o 25) API protocols: https://t.co/2CEu4Wnhsv 26) CDN: https://t.co/MbaSzBnZPQ 27) Database types: https://t.co/T0tUF1xYPI 28) Message Queues: https://t.co/7Tz5sevNA8 29) Hashing vs encryption vs tokenization: https://t.co/6eN9I2YAej 30) Service Discovery: https://t.co/rcKkXkWWcX 31) Pub/Sub: https://t.co/HF0Zr5R4SK 32) Connection pooling: https://t.co/39SsEo4kk3 33) Forward proxy vs reverse proxy: https://t.co/0P6NM8kh8u 34) Consistent hashing: https://t.co/8d8o74EsaS 35) SQL vs NoSQL: https://t.co/oDTRpsnQUn ๐ PS: Want to improve at system design? Download my free System Design Handbook and join 34,000+ engineers who get my free weekly newsletter โ https://t.co/LybPLdor9s โโ ๐ Save it for later. โ Follow me ( Nikki Siapno ) to improve at system design.
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 127 interactions against 177K followers, an engagement rate of 0.072%. Measured over 13 original posts, its engagement rate beats 53% of 21,185 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 11K times each, and 1.13% of those impressions turn into an interaction. That is about 6.35% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.77 post a day over the last 30 days, with activity on roughly 70% of days. Most posts go out around 13:00 UTC, and Monday is the busiest day of the week. Of the 13 posts sampled, 85% carry an image or video and 92% link out. The account's strongest tracked post pulled 2.1K interactions, about 17x its own typical post.
- What is Nikki Siapno's engagement rate on X?
- Nikki Siapno (@NikkiSiapno) has an engagement rate of 0.072%, based on the median interactions across 13 original posts from the last 30 days against 176,852 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.072%, Nikki Siapno sits above the 25th percentile of the 212,825 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 @NikkiSiapno have real engagement?
- Its engagement rate beats 53% of the tracked X accounts closest to it in follower count (21,185 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 @NikkiSiapno post?
- Most posts go out around 13:00 UTC, and Monday is its busiest day, at roughly 0.77 posts per day across the measured window.