NVIDIA AI Developer engagement report
@NVIDIAAIDev - 112K followers on X
Measured over 1 original post from a 30-day window, last computed on October 10, 2026.
Engagement
Early reading. We have captured 1 original post 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 22 interactions against 112K followers, an engagement rate of 0.02%. Posts are seen about 3.7K times each, and 0.59% of those impressions turn into an interaction. That is about 3.33% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% 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 1 posts sampled, 100% link out. The account's strongest tracked post pulled 63K interactions, about 2872x its own typical post. Only 1 original post 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 1 original post from a 30-day window, last computed on October 10, 2026.
Where this sits in the catalog
At 0.02%, NVIDIA AI Developer sits above the 10th percentile of the 212,741 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.697% |
| 90th percentile | 2.61% |
| 99th percentile | 82.8% |
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 | 0% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | -8% | -9% to -7% | 41K |
- 0% 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 47 characters, which falls in the under 80 characters band. Across the catalog, posts under 80 characters run 8% below 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 23, 20262872x their median
Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex. https://t.co/rPLTk99ZH5
- Apr 17, 2026495x their median
I'm lucky enough to have a great doctor and access to excellent Bay Area medical care. I've taken lots of standard screening tests over the years and have tried lots of "health tech" devices and tools. With all this said, by far the most useful preventative medical advice that I've ever received has come from unleashing coding agents on my genome, having them investigate my specific mutations, and having them recommend specific follow-on tests and treatments. Population averages are population averages, but we ourselves are not averages. For example, it turns out that I probably have a 30x(!) higher-than-average predisposition to melanoma. Fortunately, there are both specific supplements that help counteract the particular mutations I have, and of course I can significantly dial up my screening frequency. So, this is very useful to know. I don't know exactly how much the analysis cost, but probably less than $100. Sequencing my genome cost a few hundred dollars. (One often sees papers and articles claiming that models aren't very good at medical reasoning. These analyses are usually based on employing several-year-old models, which is a kind of ludicrous malpractice. It is true that you still have to carefully monitor the agents' reasoning, and they do on occasion jump to conclusions or skip steps, requiring some nudging and re-steering. But, overall, they are almost literally infinitely better for this kind of work than what one can otherwise obtain today.) There are still lots of questions about how this will diffuse and get adopted, but it seems very clear that medical practice is about to improve enormously. Exciting times!
- Apr 12, 2026311x their median
We're delighted to announce that MiniMax M2.7 is now officially open source. With SOTA performance in SWE-Pro (56.22%) and Terminal Bench 2 (57.0%). You can find it on Hugging Face now. Enjoy!🤗 huggingface：https://t.co/ApWrahIl3o Blog: https://t.co/gAxeFsNdW4 MiniMax API: https://t.co/1dgbMx0Q7K
- Apr 21, 2026218x their median
Here is a manga made by ChatGPT Images 2.0 of @gabeeegoooh and me looking for more GPUs: https://t.co/ek95JfUN5V
- Apr 17, 2026182x their median
I trained a 12M parameter LLM on my own ML framework using a Rust backend and CUDA kernels for flash attention, AdamW, and more. Wrote the full transformer architecture, and BPE tokenizer from scratch. The framework features: - Custom CUDA kernels (Flash Attention, fused LayerNorm, fused GELU) for 3x increased throughput - Automatic WebGPU fallback for non-NVIDIA devices - TypeScript API with Rust compute backend - One npm install to get started, prebuilt binaries for every platform Try out the model for yourself: https://t.co/TB2itlmCVT Built with @_reesechong. Check out the repos and blog if you want to learn more. Shoutout to @modal for the compute credits allowing me to train on 2 A100 GPUs without going broke cc @sundeep @GavinSherry
- Apr 15, 2026159x their median
Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: https://t.co/ROR7miJeCU 📄 Read the paper: https://t.co/1osU9EGjGD
- Apr 29, 202681x their median
If you’re seeing this, you should be following @NVIDIAAI. Our developer team has moved next door to @NVIDIAAI. Join us there to stay up to date on the latest products, models, deep dives, and more. Thanks to everyone who’s been with us here. We hope to see soon 💚 https://t.co/2j1VAACIoy
- Apr 8, 202637x their median
What do you want to see me running on these 4x DGX Sparks today? https://t.co/VVTX1xvqEt
- Apr 17, 202635x their median
Here's your weekend project. Build a fully local, sandboxed AI assistant. Step-by-step tutorial to build your always-on agent: 🦞 on @openclaw ✅ with NVIDIA NemoClaw ✨ using NVIDIA DGX Spark Get started: https://t.co/NMw40hnH4b https://t.co/4Xol9TzMmk
- Apr 15, 202629x their median
🦞 Set up NVIDIA NeMoClaw with MiniMax M2.7 (from @MiniMax_AI) using secure, out-of-the-box integrations. ⚡Configure your environment and run search queries in minutes. 🎥 Full tutorial available here 👇 Or on YouTube 👉 https://t.co/7LpzyqYSjs https://t.co/oqsGTGRBuf
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 22 interactions against 112K followers, an engagement rate of 0.02%. Posts are seen about 3.7K times each, and 0.59% of those impressions turn into an interaction. That is about 3.33% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% 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 1 posts sampled, 100% link out. The account's strongest tracked post pulled 63K interactions, about 2872x its own typical post. Only 1 original post 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 NVIDIA AI Developer's engagement rate on X?
- NVIDIA AI Developer (@NVIDIAAIDev) has an engagement rate of 0.02%, based on the median interactions across 1 original posts from the last 30 days against 111,928 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.02%, NVIDIA AI Developer sits above the 10th percentile of the 212,741 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 @NVIDIAAIDev have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @NVIDIAAIDev post?
- Most posts go out around 18:00 UTC, and Wednesday is its busiest day, at roughly 0.03 posts per day across the measured window.