HackerNoon | Learn Any Technology engagement report
@hackernoon - 95K followers on X
Measured over 23 original posts from a 30-day window, last computed on August 25, 2026.
Engagement
A typical post picks up 5 interactions against 95K followers, an engagement rate of 0.005%. Measured over 23 original posts, its engagement rate beats 12% of 4,347 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 1.7K times each, and 0.299% of those impressions turn into an interaction. That is about 1.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 23 posts sampled, 26% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 549 interactions, about 110x its own typical post. Recurring topics include #aifilmmaking, #aiimplementation, #businessintelligence.
Measured over 23 original posts from a 30-day window, last computed on August 25, 2026. Recurring tags: #aifilmmaking, #aiimplementation, #businessintelligence.
Compared with accounts its own size
HackerNoon | Learn Any Technology's engagement rate beats 12% of the tracked X accounts closest to it in follower count (4,347 accounts, accounts of similar size (decile 5 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 17% 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 0.005%, HackerNoon | Learn Any Technology sits above the 10th percentile of the 42,087 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.013%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.013% |
| 50th percentile | 0.084% |
| 75th percentile | 0.449% |
| 90th percentile | 2.07% |
| 99th percentile | 143.1% |
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 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% | 59K |
| 01:00 UTC | -2% | 60K |
| 02:00 UTC | -4% | 58K |
| 03:00 UTC | -4% | 62K |
| 04:00 UTC | -6% | 50K |
| 05:00 UTC | -5% | 49K |
| 06:00 UTC | -5% | 56K |
| 07:00 UTC | -5% | 61K |
| 08:00 UTC | -4% | 71K |
| 09:00 UTC | -4% | 82K |
| 10:00 UTC | -3% | 84K |
| 11:00 UTC | -3% | 91K |
| 12:00 UTC | -2% | 100K |
| 13:00 UTC | -2% | 110K |
| 14:00 UTC | -3% | 113K |
| 15:00 UTC | -2% | 117K |
| 16:00 UTC | -3% | 114K |
| 17:00 UTC | -3% | 106K |
| 18:00 UTC | -2% | 99K |
| 19:00 UTC | -2% | 93K |
| 20:00 UTC | -1% | 87K |
| 21:00 UTC | -1% | 77K |
| 22:00 UTC | -2% | 67K |
| 23:00 UTC | -1% | 60K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 257K |
| Monday | +1% | 329K |
| Tuesday | -2% | 355K |
| Wednesday | -4% | 323K |
| Thursday | -3% | 273K |
| Friday | -3% | 277K |
| Saturday | +3% | 250K |
Best tweets
- Aug 19, 2026110x their median
Why can yesterday's dashboard numbers change when nobody changed the data? @TimescaleDB explains how late-arriving records, invalidation, and refresh windows affect continuous aggregates—and how to keep historical results consistent: https://t.co/2u3nRYZfuQ
- Aug 21, 2026101x their median
What if Claude Code could check its own work after every edit? This five-line hook connects Claude Code to SonarQube's Agentic Analysis and feeds code-quality findings back into the development workflow. Read @SonarSource's breakdown: https://t.co/7eOflhmegh
- Aug 20, 202666x their median
AI coding tools generate a lot of output. The format you choose can have a surprisingly large impact on efficiency. Learn from @SonarSource how TOON and a simple Sonar CLI setting can reduce AI coding-agent usage compared with JSON output: https://t.co/yyZ55q1Fu1
- Aug 21, 202665x their median
AI agents don't just need a model—they need infrastructure to run reliably around the clock. Compare laptops, VPSs, and managed runtimes, and explore the security, deployment, and observability considerations behind always-on agents: https://t.co/D7ktM2Yc3m
- Aug 20, 202662x their median
AI tools are changing how legal teams work, but successful adoption depends on governance, oversight, and clear policies. This article explores practical considerations for using AI in legal workflows: https://t.co/u97RFZBALf
- Aug 20, 202649x their median
Managing compliance requirements across a supply chain can get complicated. See how SecurityMetrics' CMMC Link is designed to simplify compliance workflows and make requirements easier to manage: https://t.co/oyAAbgvsDz
- Jun 2, 202627x their median
The Decentralize AI Hackathon is live 💚 For builders, developers, and technical thinkers working on open AI infrastructure: → Enter with a project, prototype, or technical idea, publish your work as a @hackernoon blog post → $51,750+ prize pool and the GRAND PRIZE of the https://t.co/Inw4J5VJKx domain → Free compute credits for every eligible participant → Two rounds: June '26 through Feb '27 → Start at the concept stage. Keep building. Submit updates as your work evolves. Enter today 👉 https://t.co/Inw4J5VJKx Sponsored by @nosana_ai, @ArweaveEco, and @MEXC #DecentralizeAI
- Aug 24, 202626x their median
AI coding agents can move fast. Guardrails help keep them on track. Learn from @SonarSource how Claude Code hooks can enforce checks, block unwanted commands, and validate AI-generated code before it ships: https://t.co/HIs8OLzUn4
- Aug 19, 202613x their median
Security teams are dealing with more alerts than ever. The challenge is finding the signals that actually need attention. This case study by @anyrun_app explores how SOC teams can streamline incident investigation and respond to threats more efficiently: https://t.co/1jKfWVk87d
- Aug 25, 20266.0x their median
Learn how QA agents use knowledge graphs to understand your product, test by user goals, adapt to UI changes, and reduce test maintenance: https://t.co/PUPBUv5pOt
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 5 interactions against 95K followers, an engagement rate of 0.005%. Measured over 23 original posts, its engagement rate beats 12% of 4,347 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 1.7K times each, and 0.299% of those impressions turn into an interaction. That is about 1.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 23 posts sampled, 26% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 549 interactions, about 110x its own typical post. Recurring topics include #aifilmmaking, #aiimplementation, #businessintelligence.
- What is HackerNoon | Learn Any Technology's engagement rate on X?
- HackerNoon | Learn Any Technology (@hackernoon) has an engagement rate of 0.005%, based on the median interactions across 23 original posts from the last 30 days against 94,514 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.005%, HackerNoon | Learn Any Technology sits above the 10th percentile of the 42,087 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 @hackernoon have real engagement?
- Its engagement rate beats 12% of the tracked X accounts closest to it in follower count (4,347 accounts), which puts it in the bottom quarter 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 @hackernoon post?
- Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 2 posts per day across the measured window.