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Sumanth engagement report

@Sumanth_077 - 77K followers on X

Measured over 4 original posts from a 30-day window, last computed on September 9, 2026.

Engagement

Per follower
0.284%
of 77K followers
Per impression
1.22%
18K views on a typical post
Reach
23.4%
of its followers see a post
Typical post
218
interactions (median)
Saved
1.43%
257 bookmarks on a typical post
Posting rate
1.2/day
active 63% of days
Peak time
14:00 UTC
Tuesday

Early reading. We have captured 4 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 218 interactions against 77K followers, an engagement rate of 0.284%. Posts are seen about 18K times each, and 1.22% of those impressions turn into an interaction. That is about 23.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.2 post a day over the last 30 days, with activity on roughly 63% of days. Most posts go out around 14:00 UTC, and Tuesday is the busiest day of the week. Of the 4 posts sampled, 50% carry an image or video, 50% are part of a thread and 50% link out. The account's strongest tracked post pulled 973 interactions, about 4.5x its own typical post. Only 4 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 4 original posts from a 30-day window, last computed on September 9, 2026.

Where this sits in the catalog

At 0.284%, Sumanth sits above the 50th percentile of the 66,536 accounts in this comparison. That places it in the above the median band, which runs 0.1% to 0.5%.

p100.002%
p250.016%
p50 (median)0.1%
p750.5%
p902.09%
p99119.7%
Engagement rate as a share of followers, across the 66,536 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 56,995 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 percentile0.002%
25th percentile0.016%
50th percentile0.1%
75th percentile0.5%
90th percentile2.09%
99th percentile119.7%

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 14:00 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.

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: 14:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 14:00 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: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Tuesday
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 video50% of posts+111%+108% to +115%34K
Outbound link50% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 50% 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.
  • 50% 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 972 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

  • Jun 6, 20264.5x their median

    Hands on AI Engineering! I open-sourced a collection of 50+ hands-on AI engineering tutorials. It features step-by-step projects and tutorials on: • AI Agents and Multi-agents • RAG (Agentic, Vision, and Local) • MCP AI Agents • OCR Apps • Voice AI Agents • & so much more 100% free and open source. 1k+ Github stars I've shared the link in the comments!

    78515332373K viewsView on X
  • Jul 15, 20262.2x their median

    Introducing factuality in the Arena: a new ranking of models according to a weighted combination of human preference and factuality. Model rankings are now viewable according to a weighted combination of human preference and factuality. Factuality is live in our Text and Search Arenas as a non-default toggle. We audit model responses by randomly sampling battles and extracting web-verifiable claims. We then verify these claims and compare the average correctness between model responses. To power these rankings, we’ve labeled over 2 million claims made by LLMs in real-world conversations, 1.3+ million from Text Arena, and 700k+ from Search Arena. Notable highlights with factuality enabled in the Text Arena: - Claude Fable 5 moves down slightly to spot #2 - GPT-5.5 saw the largest increase, moving up 13 spots into the #7 spot - Muse Spark dropped the most from #7 to #20 (-13pt) By labs, Meta saw the largest drop from #2 to #5, while Anthropic overall held the #1 spot. Looking at only open model providers, Xiaomi saw the largest improvement, jumping from #9 to #6. Learn more about the findings and methodology in this thread.

    400441218136K viewsView on X
  • Apr 8, 20261.9x their median

    https://t.co/zQeFLsziGf

    343584383K viewsView on X
  • Aug 25, 2026

    https://t.co/OJrhmzcTL1

    2624511565K viewsView on X
  • Jul 18, 2026

    https://t.co/9KPza8VWxZ

    2053892163K viewsView on X
  • Sep 8, 2026

    Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

    1953211116K viewsView on X
  • Aug 18, 2026

    https://t.co/XEZ0jpwACT

    153366220K viewsView on X
  • Jul 13, 2026

    https://t.co/lGawLq9VVA

    163254283K viewsView on X
  • Jul 27, 2026

    https://t.co/Qd8FqU2E5n

    69186462K viewsView on X
  • Jul 2, 2026

    https://t.co/8gHTbqLAhP

    60157259K 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.

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Reading these numbers

A typical post picks up 218 interactions against 77K followers, an engagement rate of 0.284%. Posts are seen about 18K times each, and 1.22% of those impressions turn into an interaction. That is about 23.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.2 post a day over the last 30 days, with activity on roughly 63% of days. Most posts go out around 14:00 UTC, and Tuesday is the busiest day of the week. Of the 4 posts sampled, 50% carry an image or video, 50% are part of a thread and 50% link out. The account's strongest tracked post pulled 973 interactions, about 4.5x its own typical post. Only 4 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is Sumanth's engagement rate on X?
Sumanth (@Sumanth_077) has an engagement rate of 0.284%, based on the median interactions across 4 original posts from the last 30 days against 76,938 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.284%, Sumanth sits above the 50th percentile of the 66,536 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 @Sumanth_077 have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @Sumanth_077 post?
Most posts go out around 14:00 UTC, and Tuesday is its busiest day, at roughly 1.2 posts per day across the measured window.

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