jietang engagement report
@jietang - 78K followers on X
Measured over 3 original posts from a 30-day window, last computed on September 13, 2026.
Engagement
Early reading. We have captured 3 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 1.2K interactions against 78K followers, an engagement rate of 1.70%. Posts are seen about 143K times each, and 0.866% of those impressions turn into an interaction. That is about 182.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 02:00 UTC, and Thursday is the busiest day of the week. Of the 3 posts sampled, 67% carry an image or video and 33% link out. The account's strongest tracked post pulled 405K interactions, about 328x its own typical post. Only 3 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 3 original posts from a 30-day window, last computed on September 13, 2026.
Where this sits in the catalog
At 1.70%, jietang sits above the 75th percentile of the 205,676 accounts in this comparison. That places it in the top 25% band, which runs 0.679% to 2.56%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.679% |
| 90th percentile | 2.56% |
| 99th percentile | 82.4% |
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.
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 | 67% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 33% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 67% 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.
- 33% 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 189 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.
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 9, 2026328x their median
Tesla driving itself around LA https://t.co/NyM36R6J7a
- Apr 7, 202611x their median
Introducing GLM-5.1: The Next Level of Open Source - Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. - Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: https://t.co/hmyDe4Nel3 Weights: https://t.co/CuUjXcPKJD API: https://t.co/fz6reja4fb Coding Plan: https://t.co/Nk8Y98HNhU Coming to https://t.co/WCqWT0qCQb in the next few days.
- Jun 13, 20267.5x their median
GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing: science should be global. The path to AGI (Artificial General Intelligence) must never be enclosed by high walls. We have always believed that AGI should be the cornerstone for all of humanity to collaboratively explore the boundaries of intelligence and solve complex challenges, rather than a privilege monopolized by a few rules and subject to revocation at any moment. In the face of external blockades and restrictions, our attitude is one of radical openness. Frontier intelligence must remain open-source, accessible, and buildable, serving every dedicated developer. GLM-5.2 is Zhipu's most capable open-source model to date. It not only supports a truly usable 1M context window but also maintains a continuous lead in the independent completion of long-horizon tasks, providing solid foundational support for building complex agent applications. It also continues to be our main engine for creating the strongest domestic coding model. Tonight at 5:21—at this special moment—GLM-5.2 will officially be available to all GLM Coding Plan users (including Lite / Pro / Max). The API will also go live next week. A step closer to frontier intelligence for everyone. The future of AI is open, and it is for the people. ModelKey: GLM-5.2
- Jun 29, 20265.4x their median
Any new features we must have in the next version of glm?
- Jun 16, 20263.4x their median
We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.
- Aug 11, 20261.6x their median
ZCode now has 1 million users. As a thank-you to our community, we’ve reset usage limits for all GLM Coding Plan users. We’re also rolling out an update that helps turn long-horizon capabilities into completed work: - More intelligence in real engineering workflows - 98% cache hit rate, providing around 1.8x more usage Download: https://t.co/kDfUn4gXq6 Join the community: https://t.co/lpec2zKTGJ
- Jun 14, 20261.5x their median
GLM-5.2 is now fully available for GLM Coding Plan users. ZCode 3.0 is deeply optimized for GLM-5.2, bringing stronger Agent task execution, better long-context coding, and the new Goal feature for managing larger development objectives from planning to completion. Coding Plan subscribers get 150% usage quota inside ZCode. New users get 5 days free with 5M tokens per day. Download: https://t.co/HYwjwRO9bE
- Sep 8, 2026
Updated AA index... Now Fable 5.1 and GPT-astra have the same... https://t.co/G6cSEmAMus
- Jul 14, 2026
Our Single-rollout Asynchronous Optimization (SAO), is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks, such as SWE-Bench Verified, BeyondAIME, and IMOAnswerBench. https://t.co/oca1DvDvdB
- Sep 3, 2026
GLOBAL BUILD is live 🌍🔥 GLM-5.3-Flash, FREE in ZCode. 10 hours a day. Sep 3 – 18. Who gets it 👑 Coding Plan members — free every day, all 15 days. Our VIPs go first. 🥚 New users — 100M free tokens on sign-up. One-time, valid until the window closes When it opens 🇺🇸 8 AM PT · 11 AM ET 🇬🇧 4 PM London 🇨🇳 11 PM Beijing How to claim 1️⃣ Open ZCode → tap the card in the bottom-left corner 👀 2️⃣ Not there? Restart the app 😏 https://t.co/9y0HPmcBxz
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 1.2K interactions against 78K followers, an engagement rate of 1.70%. Posts are seen about 143K times each, and 0.866% of those impressions turn into an interaction. That is about 182.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 02:00 UTC, and Thursday is the busiest day of the week. Of the 3 posts sampled, 67% carry an image or video and 33% link out. The account's strongest tracked post pulled 405K interactions, about 328x its own typical post. Only 3 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 jietang's engagement rate on X?
- jietang (@jietang) has an engagement rate of 1.70%, based on the median interactions across 3 original posts from the last 30 days against 78,163 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 1.70%, jietang sits above the 75th percentile of the 205,676 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 @jietang have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @jietang post?
- Most posts go out around 02:00 UTC, and Thursday is its busiest day, at roughly 0.4 posts per day across the measured window.