hardmaru engagement report
@hardmaru - 445K followers on X
Measured over 9 original posts from a 30-day window, last computed on September 25, 2026.
Engagement
A typical post picks up 354 interactions against 445K followers, an engagement rate of 0.081%. Measured over 9 original posts, its engagement rate beats 67% of 21,185 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 41K times each, and 0.874% of those impressions turn into an interaction. That is about 9.11% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.8 post a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 16:00 UTC, and Sunday is the busiest day of the week. Of the 9 posts sampled, 33% carry an image or video, 11% are part of a thread and 44% link out. The account's strongest tracked post pulled 49K interactions, about 138x its own typical post.
Measured over 9 original posts from a 30-day window, last computed on September 25, 2026.
Compared with accounts its own size
hardmaru's engagement rate beats 67% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 10 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 43% 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 0.081%, hardmaru sits above the 25th percentile of the 213,098 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.147%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.147% |
| 75th percentile | 0.698% |
| 90th percentile | 2.61% |
| 99th percentile | 82.9% |
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 16:00 UTC, and Sunday 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 | 33% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 44% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 33% 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.
- 44% 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 630 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 22, 2026138x their median
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: https://t.co/hhO6qTawgb 🐡
- Jul 24, 20268.9x their median
Announcing Fugu-Ultra v1.1 🐡 We’ve been thrilled by the reception to the Fugu model family. Thanks to everyone who tried it, shared feedback, and trusted Fugu with real work. Today, we’re releasing Fugu-Ultra v1.1 → https://t.co/hhO6qTawgb Upgraded to incorporate the latest frontier models, resulting in stronger performance across every benchmark shown, including gains of up to 7.9 points over v1.0, with particularly strong results on ProgramBench and Terminal Bench 2.1. Fugu-Ultra v1.1 is more capable across coding, agentic tasks, and advanced reasoning, and available at the same price as Fugu-Ultra v1.0 The frontier keeps moving, and Fugu keeps getting better.
- May 27, 20264.7x their median
Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation https://t.co/c9AvsRKybj What if we didn’t have to hold an entire neural network in memory to train it? Standard neural net training optimizes all parameters jointly. As a result, the memory required during training grows linearly with the depth of the network. In our #ICLR2026 paper, we propose DiffusionBlocks, a principled framework to train networks one block at a time, drastically reducing memory requirements while matching end-to-end performance. With DiffusionBlocks, we split the network into blocks and train them one at a time, so you only need memory for a single block. How? We explicitly assign each block a role: to move the representation a little closer to the target than the block before it did. That role turns out to be precisely what a diffusion model does, step by step. Each block only needs to optimize its own objective and can be trained independently. We validated this across five different architectures: • ViT • DiT • Masked diffusion • Autoregressive transformers • Recurrent-depth transformers In each case, performance is competitive with end-to-end training while using a fraction of the memory. This perspective also extends naturally to recurrent-depth (Looped) transformers, which apply the same network iteratively and normally require expensive backpropagation through time (BPTT). Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training. Read our paper and code, to learn more. Paper: https://t.co/CRj96VGYQn GitHub: https://t.co/eNW0K9Xh8E 🐟
- Jul 21, 20267.3x their median
Introducing Fugu-Cyber: an update to our Fugu orchestration model. It achieves state-of-the-art performance on real-world security benchmarks, matching cyber-focused frontier models like GPT-5.5-Cyber and Mythos Preview. https://t.co/5Nh1eBPhHg 🐡 https://t.co/Ho4UCLUfTB
- Jul 15, 20265.6x their median
Sakana AI Teams With NVIDIA to Advance Open Model Innovation from Japan We're announcing the next phase of our collaboration with NVIDIA. We're bringing NVIDIA's open model stack, including the Nemotron family, into Sakana Fugu, our multi-agent orchestration system. https://t.co/bsxjRkux0a Rather than relying solely on scaling individual monolithic models, our approach focuses on collective intelligence. Sakana Fugu operates as an intelligent orchestrator behind a single API, dynamically selecting, coordinating, and combining the strengths of multiple models for each task. This architecture keeps our system modular, adaptable, and resilient. As a natural next step to expand Fugu's capabilities, we're integrating NVIDIA Nemotron as a specialized agent, complementing the frontier and open models Fugu already orchestrates. Nemotron helps demonstrate how open models become far more useful when orchestrated within agentic systems rather than used in isolation. This collaboration creates a reinforcing cycle. Fugu gains a deeper pool of specialized capabilities, while NVIDIA can evaluate how its models perform when coordinated within complex, multi-step workflows. These real-world signals can continuously improve both the models and the orchestration layer. By combining Sakana AI's Japan-born collective-intelligence approach with NVIDIA's open models and accelerated computing, we aim to shape a future of AI that is modular, collaborative, and open by design.
- Sep 16, 20264.8x their median
Today everyone is talking about Recursive Self-Improvement (RSI). In 1987, when compute was 100,000,000 x more expensive, I published the 1st concrete RSI algorithms. Now compute is cheap, and RSI is driving the future of both software and physical AI. See: RSI since 1987 https://t.co/yA3KUqpFtb (Technical Note IDSIA-9-26) Also covered: RSI with self-modifying policies since 1994, gradient descent-based RSI in neural networks since 1992, asymptotically optimal RSI for curriculum learning since 2002, mathematically optimal RSI through the self-referential Gödel Machine since 2003, RSI combined with artificial curiosity and intrinsic motivation since 1990/1997, recent work on RSI since 2020. Software-based RSI has become practical. Full RSI, however, will require not just self-improving software but self-improving hardware in the physical world. As of 2026, companies talking about RSI include Anthropic, OpenAI, Sakana AI, SpaceX, Ricursive, Recursive Superintelligence, Inherent …
- Jun 24, 20264.7x their median
Fugu-Ultra is now live on @OpenRouter! ⚡ We share a core vision with the OpenRouter team: the future of AI isn’t a single monolithic model, but the collective intelligence of the world’s best models working together. Try it: https://t.co/sVkbTPtXOl 🐡 https://t.co/y65DXVcqXL
- Jun 5, 20263.9x their median
Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀 https://t.co/AskX3J5oEJ Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI. While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality: ▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature. Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve. Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI. We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good. We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.
- May 9, 20262.2x their median
Reproducing all of Schmidhuber’s papers (1990-2025) using an AI coding assistant. Cool project by @yaroslavvb! It even reproduced the “World Models” paper by me and @SchmidhuberAI with a toy env, with a full VAE + RNN world model implementation. Project: https://t.co/sgQG5umNEm https://t.co/iKMFN7ti9z
- Jul 6, 20263.4x their median
🐟️ Sakana Translate公開 🐟️ 本日、Sakana AIはチャットサービス「Sakana Chat」に新機能「Sakana Translate」を追加しました。 日本語・英語・中国語の双方向翻訳に対応します。 Sakana Translateを試す：https://t.co/LN14dmsH7p https://t.co/lgNqu9zssb
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 354 interactions against 445K followers, an engagement rate of 0.081%. Measured over 9 original posts, its engagement rate beats 67% of 21,185 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 41K times each, and 0.874% of those impressions turn into an interaction. That is about 9.11% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.8 post a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 16:00 UTC, and Sunday is the busiest day of the week. Of the 9 posts sampled, 33% carry an image or video, 11% are part of a thread and 44% link out. The account's strongest tracked post pulled 49K interactions, about 138x its own typical post.
- What is hardmaru's engagement rate on X?
- hardmaru (@hardmaru) has an engagement rate of 0.081%, based on the median interactions across 9 original posts from the last 30 days against 444,803 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.081%, hardmaru sits above the 25th percentile of the 213,098 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 @hardmaru have real engagement?
- Its engagement rate beats 67% of the tracked X accounts closest to it in follower count (21,185 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 @hardmaru post?
- Most posts go out around 16:00 UTC, and Sunday is its busiest day, at roughly 0.8 posts per day across the measured window.