Lin Qiao engagement report
@lqiao - 230K followers on X
Measured over 2 original posts from a 30-day window, last computed on October 2, 2026.
Engagement
Early reading. We have captured 2 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 890 interactions against 230K followers, an engagement rate of 0.391%. Posts are seen about 137K times each, and 0.649% of those impressions turn into an interaction. That is about 59.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 18:00 UTC, and Wednesday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video and 50% link out. The account's strongest tracked post pulled 41K interactions, about 47x its own typical post. Only 2 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 2 original posts from a 30-day window, last computed on October 2, 2026.
Where this sits in the catalog
At 0.391%, Lin Qiao sits above the 50th percentile of the 157,515 accounts in this comparison. That places it in the above the median band, which runs 0.128% to 0.604%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.003% |
| 25th percentile | 0.022% |
| 50th percentile | 0.128% |
| 75th percentile | 0.604% |
| 90th percentile | 2.32% |
| 99th percentile | 83.3% |
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 18:00 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 50% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% 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 413 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 30, 202647x their median
Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Available now in beta. https://t.co/HKhLknxLJO
- Jun 24, 20263.6x their median
You can now try GLM 5.2 in Cursor! Excited to see more useful open models, thank you to Fireworks for partnering here. Results from our evals ↓ https://t.co/aUxnRtaRks
- Aug 25, 20261.7x their median
Excited about Ox Alpha, knowing which model it is. Will get it on Fireworks.
- Aug 4, 2026
Announcing the Artificial Analysis Endpoint Accuracy Index, measuring how much of an open weights model's accuracy each serverless API endpoint preserves. We are initiating coverage with GLM-5.2, gpt-oss-120b and DeepSeek V4 Pro, with Kimi K3 coming soon Providers trade off accuracy to optimize for speed and cost. They quantize weights, write custom kernels and tune their inference stacks, and sometimes they simply ship bugs. We are bringing the rigor of our Artificial Analysis Intelligence Index to measuring endpoints, so developers can pick providers on accuracy, not just price and speed We benchmark each serverless endpoint against our own self-hosted reference deployment of the official weights, where 100% represents matching the reference. An endpoint is at reference parity when its result falls within the 95% confidence interval of the reference. Coverage is live for GLM-5.2, gpt-oss-120b and DeepSeek V4 Pro, with Kimi K3 accuracy coverage launching soon Key elements of the Endpoint Accuracy Index: ➤ Three areas, equally weighted: tool calling (BFCL-500, 500 questions, 3 repeats), scientific reasoning (HLE-250, 250 questions, 10 repeats) and long context recall (AA-LCR-25, 25 questions, 10 repeats). Each subset separates endpoints on the serving choices that drive accuracy differences, with repeats sized for tight confidence intervals ➤ Reference deployment: we self-host the official weights at the lab's recommended precision, following the lab's serving recipe, and publish the complete commands for each reference ➤ Inference parameters: we run the model's highest supported reasoning mode and each endpoint's highest supported output length and context window ➤ Confidence intervals: the parity test accounts for uncertainty in both the endpoint's runs and the reference's runs ➤ Rotating coverage: models enter once sufficient number of providers serve them and exit when a newer version in the same family supersedes them. We benchmark new endpoints as providers launch them and refresh all listed endpoints periodically ➤ Point in time: each result carries the date it was measured, with multi-day benchmarks dated to their final day Key results for GLM-5.2 ➤ Output token limits restrict accuracy. Restrictive limits cut responses off before the model finishes reasoning, and the most restrictive endpoints score half the reference or less on HLE-250 Key results for gpt-oss-120b ➤ Tool call handling separates endpoints. Providers parse and format tool calls differently, and some endpoints score 22% on BFCL-500 against 37% for the reference ➤ Serving configuration changes what the model does at the same requested settings. Some endpoints produce far fewer reasoning tokens at the same configured level, and restricted context windows truncate long context tasks Key results for DeepSeek V4 Pro ➤ DeepSeek V4 Pro endpoints are more in line with the reference. Majority of the endpoints are at reference parity, and DeepSeek's own first-party endpoint scores slightly above the reference
- Sep 25, 2026
Today we’re announcing a $132M tender offer for Fireworks employees, led by Atreides. Building a company is an exciting and patient expedition. You make bets before the market agrees, and spend years building things that may only make sense to the people in the trenches. Today is meaningful because our team gets to realize some of the value they’ve created while continuing to build toward a future where every company can create its own specialized intelligence. Thank you to everyone who chose this expedition with me. We’re just 1% into the journey.
- Jun 3, 2026
We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier advisor. We tested a hybrid setup where GLM 5.1 served as the primary worker, routing tasks to Opus 4.7 as an advisor when needed. GLM invoked Opus sparingly, just 0.83 times per task on average. The hybrid setup beat Opus on both quality and cost: 18% all-pass vs 14%, at $368 vs $954 across the same 100 tasks. 2) Post-training can push open models to frontier-level legal performance. On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6's all-pass rate from 11% to 15%, beating Opus' 14%. But the cost gap was even more striking: $84 vs $954 across the same 100 tasks, or ~11x cheaper. We're excited to continue working with @FireworksAI_HQ on the next generation of open-source legal agents.
- May 27, 2026
We just hit a major milestone — @FireworksAI_HQ passed $800M annualized run rate and reached 4x revenue growth, apart from Cursor, in Q1. We invite curious and courageous minds to join us and define new frontiers of specialized intelligence!
- Jun 15, 2026
https://t.co/rMVRgBvwEI
- Sep 2, 2026
Excited for open model drops in Sept. 2 big ones. Very balanced.
- Aug 16, 2026
Don’t sell your data to Anthropic. I have so many convos with app founders — it’s your moat.
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 890 interactions against 230K followers, an engagement rate of 0.391%. Posts are seen about 137K times each, and 0.649% of those impressions turn into an interaction. That is about 59.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 18:00 UTC, and Wednesday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video and 50% link out. The account's strongest tracked post pulled 41K interactions, about 47x its own typical post. Only 2 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 Lin Qiao's engagement rate on X?
- Lin Qiao (@lqiao) has an engagement rate of 0.391%, based on the median interactions across 2 original posts from the last 30 days against 230,371 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.391%, Lin Qiao sits above the 50th percentile of the 157,515 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 @lqiao have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @lqiao post?
- Most posts go out around 18:00 UTC, and Wednesday is its busiest day, at roughly 0.3 posts per day across the measured window.