Avi Chawla engagement report
@_avichawla - 77K followers on X
Measured over 5 original posts from a 30-day window, last computed on September 10, 2026.
Engagement
Early reading. We have captured 5 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 580 interactions against 77K followers, an engagement rate of 0.758%. Posts are seen about 77K times each, and 0.755% of those impressions turn into an interaction. That is about 99.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.87 post a day over the last 30 days, with activity on roughly 70% of days. Most posts go out around 09:00 UTC, and Wednesday is the busiest day of the week. Of the 5 posts sampled, 20% carry an image or video and 100% link out. The account's strongest tracked post pulled 3.6K interactions, about 6.3x its own typical post. Only 5 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 5 original posts from a 30-day window, last computed on September 10, 2026.
Where this sits in the catalog
At 0.758%, Avi Chawla sits above the 75th percentile of the 66,536 accounts in this comparison. That places it in the top 25% band, which runs 0.5% to 2.09%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.5% |
| 90th percentile | 2.09% |
| 99th percentile | 119.7% |
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 09:00 UTC, and Wednesday 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 | 20% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 20% 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.
- 100% 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 591 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
- Apr 13, 20256.3x their median
10 MCP, AI Agents, and RAG projects for AI Engineers (with code):
- Aug 28, 20262.6x their median
https://t.co/sLoULsUfEt
- Apr 16, 20262.2x their median
https://t.co/enbhzI6kvp
- Apr 4, 20261.9x their median
https://t.co/UVK8C699vj
- Jun 28, 2026
https://t.co/8j9ZSvQfU1
- Aug 26, 2026
Anthropic did something you'll regret ignoring: They split one coding task across four agents by role, as a planner, implementer, tester, and reviewer. The goal was to test whether splitting agents by job title is a good way to divide the work. And they found agents spent more tokens on coordination than on the work itself. They call it the telephone game, where each handoff degrades what the next agent receives. OpenAI and Google also built machinery for this rather than leaving it to the prompt. - OpenAI added a "handoffs" primitive to the Agents SDK - Google's ADK controls how much parent context reaches a sub-agent. Both are design-time wiring. You declare which agents are reachable from which, and both ends run inside the same framework. This setup assumes you know which agent feeds which before the run starts, but plenty of agent work isn't known in advance. Switch, from @Flint_AI_, is built for that case. It puts agents and people in the same chat channel, so any two of them can work together without being wired to each other in advance. Say the error rate on a checkout service rises. An on-call agent queries the logs and finds the deploy behind it. Reading that, someone decides the next step is a chart against last week's baseline. That decision did not exist a minute ago, so no handoff would have been declared for it. The charting agent is a separate session with an empty context window, often a different framework on a different machine. So the person makes the routing call and then moves the payload too, by reading the first agent's answer and typing a version of it into the second. This creates three problems: 1) The charting agent has no record of the earlier session, so on the next incident it recomputes work the first agent already did. 2) The charting agent only sees the conclusion without the queries behind it, so it either trusts the summary or reruns the retrieval itself. 3) When the first agent finds no regression, nobody types that anywhere, and the rest of the team never learns it was checked. With Switch, since everyone is working in a shared channel, the person still decides what runs next, but they stop carrying the payload. The charting agent joins a channel that already holds the first agent's report, so it points at that report directly instead of rerunning the queries, and the next incident starts from a channel that shows the earlier one. The team reads the same thread the agents do, so a no-regression result is visible the moment the first agent posts it. Switch connects agents built with Claude Code, OpenAI Codex, OpenCode, or any HTTP/MCP-compatible framework directly to the tools your team already uses, like Slack, Teams, Discord, Telegram, and Mattermost. Here's the repo: https://t.co/dzCxXBjBDP Thanks to Flint AI team for partnering with me on this post.
- Apr 21, 2026
https://t.co/xw9VH2zPP5
- Apr 28, 2026
https://t.co/PhKSmQtZRd
- Sep 6, 2026
https://t.co/VNzdmXnXrO
- May 9, 2026
https://t.co/658QADQmDC
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 580 interactions against 77K followers, an engagement rate of 0.758%. Posts are seen about 77K times each, and 0.755% of those impressions turn into an interaction. That is about 99.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.87 post a day over the last 30 days, with activity on roughly 70% of days. Most posts go out around 09:00 UTC, and Wednesday is the busiest day of the week. Of the 5 posts sampled, 20% carry an image or video and 100% link out. The account's strongest tracked post pulled 3.6K interactions, about 6.3x its own typical post. Only 5 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 Avi Chawla's engagement rate on X?
- Avi Chawla (@_avichawla) has an engagement rate of 0.758%, based on the median interactions across 5 original posts from the last 30 days against 76,925 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.758%, Avi Chawla sits above the 75th 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 @_avichawla have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @_avichawla post?
- Most posts go out around 09:00 UTC, and Wednesday is its busiest day, at roughly 0.87 posts per day across the measured window.