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teej dv 🔭 engagement report

@teej_dv - 91K followers on X

Measured over 40 original posts from a 30-day window, last computed on September 25, 2026.

Engagement

Middle of its size range
Per follower
0.171%
of 91K followers
Per impression
1.56%
9.9K views on a typical post
Reach
10.9%
of its followers see a post
Typical post
154
interactions (median)
Saved
0.046%
4 bookmarks on a typical post
Posting rate
2.6/day
active 77% of days
Peak time
02:00 UTC
Thursday

A typical post picks up 154 interactions against 91K followers, an engagement rate of 0.171%. Measured over 40 original posts, its engagement rate beats 61% 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 9.9K times each, and 1.56% of those impressions turn into an interaction. That is about 10.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.6 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 02:00 UTC, and Thursday is the busiest day of the week. Of the 40 posts sampled, 55% carry an image or video, 15% are part of a thread and 15% link out. The account's strongest tracked post pulled 20K interactions, about 132x its own typical post.

Measured over 40 original posts from a 30-day window, last computed on September 25, 2026. Recurring tag: #ad.

Compared with accounts its own size

teej dv 🔭's engagement rate beats 61% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 7 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 53% 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 0.171%, teej dv 🔭 sits above the 50th percentile of the 212,925 accounts in this comparison. That places it in the above the median band, which runs 0.146% to 0.698%.

p100.004%
p250.025%
p50 (median)0.146%
p750.698%
p902.61%
p9983.0%
Engagement rate as a share of followers, across the 212,925 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,436 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.025%
50th percentile0.146%
75th percentile0.698%
90th percentile2.61%
99th percentile83.0%

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 02:00 UTC, and Thursday 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.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 02:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 02:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
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 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Thursday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Thursday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

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.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video55% of posts+111%+108% to +115%34K
Outbound link15% of posts-41%-42% to -40%32K
Typical length--3%-3% to -2%54K
  • 55% 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.
  • 15% 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 89 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below 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 14, 2026132x their median

    ChatGPT posted this on Instagram 😭 https://t.co/PaVoJMDhEl

    19K5582731241.0M viewsView on X
  • Aug 19, 202657x their median

    Who's gonna tell vibe coders about... > Rate Limiting > Caching > Load Balancing > Reverse Proxies > API Gateways > CI/CD > Docker > Kubernetes > Service Discovery > Circuit Breakers > Timeouts > Retries > Exponential Backoff > Idempotency > Message Queues > Pub/Sub > Event-Driven Architecture > Distributed Transactions > Saga Pattern > Dead Letter Queues > Cron Jobs > WebSockets > Long Polling > Server-Sent Events > Database Indexing > Query Optimization > N+1 Queries > Connection Pooling > Read Replicas > Sharding > Partitioning > Replication > Leader Election > CAP Theorem > Eventual Consistency > Optimistic Locking > Pessimistic Locking > Distributed Locks > Race Conditions > Deadlocks > Memory Leaks > Garbage Collection > Thread Safety > Backpressure > Autoscaling > Horizontal Scaling > Vertical Scaling > CDN > Edge Caching > Cache Invalidation > Feature Flags > Blue-Green Deployments > Canary Releases > Rolling Deployments > Rollbacks > Health Checks > Liveness & Readiness Probes > Monitoring > Logging > Distributed Tracing > Metrics > Alerting > SLOs > SLIs > Error Budgets > Observability > Secrets Management > IAM > OAuth > JWT Rotation > TLS > Encryption at Rest > Encryption in Transit > WAF > DDoS Protection > CORS > CSRF > SQL Injection > XSS > SSRF > Database Migrations > Schema Versioning > Disaster Recovery > Backups > Failover > Multi-Region Deployments > Chaos Engineering > Cost Optimization > Cold Starts > Serverless Limits > Latency > Throughput > P99 Latency > Tail Latency > Network Partitions > Clock Skew > DNS > TCP vs UDP > HTTP/2 & HTTP/3 > gRPC > Webhooks > API Versioning > Semantic Versioning > Infrastructure as Code > Terraform > Helm Charts > Build Caching > Dependency Hell > Production Incidents > On-call > Postmortems ...until the app gets its first million users.

    6.8K687944239762K viewsView on X
  • Jul 30, 202640x their median

    Opus 5 remade Pokemon in perfect 3D the starter scene is v disturbing https://t.co/lg8TmzsI0M

    5.4K2333041831.7M viewsView on X
  • Aug 21, 202614x their median

    if S3 is so great, why haven't that made S4 yet?

    1.9K601743393K viewsView on X
  • Aug 28, 202613x their median

    I built a @cursor_ai client that runs inside World of Warcraft as an addon so I can keep an eye on my agents and prompt them when i need. https://t.co/WVlj5Vh0FS

    1.7K5414950250K viewsView on X
  • Sep 12, 202613x their median

    "coding is solved" - salesforce https://t.co/U8aDuFi8DB

    1.8K316813450K viewsView on X
  • Sep 3, 202612x their median

    now take the models offline https://t.co/9GbZrmGSDT

    1.8K4725633K viewsView on X
  • Sep 3, 202612x their median

    Type Safety is a total joke I've been programming every day for 15 years and I can count on one hand the number of injuries I've got from Typing.

    1.7K8255893K viewsView on X
  • Jul 26, 20265.2x their median

    rofl added a terminal via libghostty to remarkable in one night. insane. ai actually just lets you do so much fun stuff https://t.co/Z6M3TrFzGq

    75612272225K viewsView on X
  • Aug 6, 20264.6x their median

    Hey @nikitabier if you're free at all, could you come on the @thestanduppod and talk about the challenges/successes/situations that were the most fun and exciting during your time at X from an engineering perspective? Thank you for your attention to this matter.

    6631325045K viewsView on X

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

#ad

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 154 interactions against 91K followers, an engagement rate of 0.171%. Measured over 40 original posts, its engagement rate beats 61% 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 9.9K times each, and 1.56% of those impressions turn into an interaction. That is about 10.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.6 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 02:00 UTC, and Thursday is the busiest day of the week. Of the 40 posts sampled, 55% carry an image or video, 15% are part of a thread and 15% link out. The account's strongest tracked post pulled 20K interactions, about 132x its own typical post.

What is teej dv 🔭's engagement rate on X?
teej dv 🔭 (@teej_dv) has an engagement rate of 0.171%, based on the median interactions across 40 original posts from the last 30 days against 91,184 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.171%, teej dv 🔭 sits above the 50th percentile of the 212,925 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 @teej_dv have real engagement?
Its engagement rate beats 61% 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 @teej_dv post?
Most posts go out around 02:00 UTC, and Thursday is its busiest day, at roughly 2.6 posts per day across the measured window.

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