Sentient engagement report
@SentientAGI - 531K followers on X
Measured over 19 original posts from a 30-day window, last computed on August 27, 2026.
Engagement
A typical post picks up 44 interactions against 531K followers, an engagement rate of 0.008%. Measured over 19 original posts, its engagement rate beats 27% of 3,917 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 13K times each, and 0.328% of those impressions turn into an interaction. That is about 2.52% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 19 posts sampled, 84% carry an image or video, 11% are part of a thread and 26% link out. The account's strongest tracked post pulled 4.1K interactions, about 93x its own typical post.
Measured over 19 original posts from a 30-day window, last computed on August 27, 2026.
Compared with accounts its own size
Sentient's engagement rate beats 27% of the tracked X accounts closest to it in follower count (3,917 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 25% 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 0.008%, Sentient sits above the 10th percentile of the 37,996 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.012%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 154.7% |
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 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% | 53K |
| 01:00 UTC | -2% | 54K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 56K |
| 04:00 UTC | -6% | 45K |
| 05:00 UTC | -4% | 43K |
| 06:00 UTC | -4% | 50K |
| 07:00 UTC | -5% | 54K |
| 08:00 UTC | -4% | 63K |
| 09:00 UTC | -3% | 72K |
| 10:00 UTC | -2% | 75K |
| 11:00 UTC | -3% | 82K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 99K |
| 14:00 UTC | -3% | 102K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 103K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 89K |
| 19:00 UTC | -2% | 84K |
| 20:00 UTC | -1% | 78K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 54K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 239K |
| Monday | 0% | 303K |
| Tuesday | -3% | 305K |
| Wednesday | -1% | 259K |
| Thursday | -2% | 251K |
| Friday | -3% | 260K |
| Saturday | +3% | 234K |
Best tweets
- Aug 17, 202693x their median
First, they stole our data. Then, they sold it back to us. Now, they watermark it. Soon, they claim they own it all.
- Jul 9, 202621x their median
New asset now available to trade on Robinhood Crypto. $SENT (Sentient) https://t.co/3INOykIXWi
- Jul 9, 202619x their median
Apple looking at shrinking powerful AI models to run on iPhones, potentially cutting cloud computing costs and enhance user privacy: The Information *APPLE HELD TALKS WITH PRISMML ON USING ITS TECH: INFORMATION *PRISMML SHRUNK LARGE AI MODEL TO RUN ON AN IPHONE: INFORMATION
- Jun 25, 20266.0x their median
The most important technology of our time is being built in private, but we're funding the alternative: $42M for the people building AGI in the open. Whether it's the model, weights, code, data, or evals, AI should belong to the people it serves, run on hardware they own, and reach the cheapest phone on earth. Here’s a few of the use cases we want to back ↓
- Aug 4, 20264.0x their median
Agents can fetch data and nail the facts, but combining it all into real research is where they start to break. Sentient researchers Darshan Tank (@TankDarshan7) and Sidhant Rahi tackle this in "CryptoAnalystBench: Failures in Multi-Tool Long-Form LLM Analysis", accepted into KDD 2026 (@kdd_news) in Jeju, South Korea ↓
- Jul 2, 20263.7x their median
EvoSkill has now been cited by 56+ papers. From frontier AI labs at Microsoft Research (@MSFTResearch) and Tongyi Lab (@Ali_TongyiLab), to top universities such as National University of Singapore (@NUSingapore) and Columbia University (@Columbia), researchers around the world are building on our work that helped pioneer the field of self-evolving agents. Proof that open research travels fast.
- Jul 7, 20263.4x their median
Want to start building your own self-evolving agents? @0xhermes_ shows you how to set up EvoSkill from scratch using the same workflow that helped him place 2nd in the Arena ↓ https://t.co/nLpswvDaly
- Jul 23, 20263.2x their median
https://t.co/JpMMdiQHeO
- Jul 27, 20263.0x their median
Are agent skills always worth using? The answer is no? This paper provides some important insights to understand this more. (bookmark it) Paper summary: Adding procedural skills to an agent is usually scored by average task success. That number nets gains against damage and hides half of what happened. Setup: nearly 6,000 paired runs across two office automation benchmarks and three model harness stacks, comparing the same agent with and without skills. A regression is a task the agent solved without skills and then failed once skills were added. Regressions are large enough that the best performing skills separate themselves mainly through fewer regressions. Larger gains contribute much less. Three mechanisms drive it. Skill description osmosis, where a skill changes agent behavior just by sitting in context even when it is never invoked. Grounding displacement, where a prescribed procedure overrides how the agent reads its inputs. Verification displacement, where the procedure suppresses checks the agent would otherwise run on its own output. Trace analysis surfaces that procedural guidance is the stage least often responsible for failure, while grounding and verification dominate the errors that remain. Existing skills are almost entirely procedure. Paper: https://t.co/6dNPyN6ali Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
- Jul 13, 20263.0x their median
Our EvoSkill research paper has been accepted to Agent Skills '26 at @CAISconf, a workshop dedicated to the design, evaluation, and optimization of skills for LLM agents. Led by @salahalzubi401 and members of the @virginia_tech research team, EvoSkill is an open-source toolkit that takes a benchmark and a coding agent, and evolves it into a state-of-the-art specialist in minutes.
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 44 interactions against 531K followers, an engagement rate of 0.008%. Measured over 19 original posts, its engagement rate beats 27% of 3,917 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 13K times each, and 0.328% of those impressions turn into an interaction. That is about 2.52% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 19 posts sampled, 84% carry an image or video, 11% are part of a thread and 26% link out. The account's strongest tracked post pulled 4.1K interactions, about 93x its own typical post.
- What is Sentient's engagement rate on X?
- Sentient (@SentientAGI) has an engagement rate of 0.008%, based on the median interactions across 19 original posts from the last 30 days against 530,650 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.008%, Sentient sits above the 10th percentile of the 37,996 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 @SentientAGI have real engagement?
- Its engagement rate beats 27% of the tracked X accounts closest to it in follower count (3,917 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 @SentientAGI post?
- Most posts go out around 14:00 UTC, and Monday is its busiest day, at roughly 1.5 posts per day across the measured window.