Brian Corrigan l 🎮 Biz engagement report
@svperfecta - 357K followers on X
Measured over 8 original posts from a 30-day window, last computed on October 5, 2026.
Engagement
A typical post picks up 8 interactions against 357K followers, an engagement rate of 0.002%. Measured over 8 original posts, its engagement rate beats 17% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 12K times each, and 0.067% of those impressions turn into an interaction. That is about 3.36% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 8 posts sampled, 38% carry an image or video and 25% link out. The account's strongest tracked post pulled 36K interactions, about 4480x its own typical post.
Measured over 8 original posts from a 30-day window, last computed on October 5, 2026. Recurring tag: #valheim.
Compared with accounts its own size
Brian Corrigan l 🎮 Biz's engagement rate beats 17% of the tracked X accounts closest to it in follower count (20,444 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 4% 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 0.002%, Brian Corrigan l 🎮 Biz sits below the 10th percentile of the 204,880 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 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 13:00 UTC, and Tuesday 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 | 38% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 25% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 38% 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.
- 25% 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 420 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
- Aug 13, 20264480x their median
I have no idea why but Grok-4.6 just totally freaked out doing some maintenance tasks when it saw my low github id. https://t.co/TGcVPR0Anl
- May 16, 20242427x their median
RELEASE DAY After almost 10 years of hard work, tireless research, and a dive deep into the kernels of computer science, I finally realized a dream: running a high-level language on GPUs. And I'm giving it to the world! Bend compiles modern programming features, including: - Lambdas with full closure support - Unrestricted recursion and loops - Fast object allocations of all kinds - Folds, ADTs, continuations and much more To HVM2, a new runtime capable of spreading that workload across 1000's of cores, in a thread-safe, low-overhead fashion. As a result, we finally have a true high-level language that runs natively on GPUs! Here's a quick demo:
- Aug 15, 2026612x their median
I ditched Unreal for AI. Kaiju Engine is a completely AI written replacement. To prove it was ready for production, I tested it by recreating and porting my old game, Firefall, into Kaiju, purely from the Steam download, no source. And it worked. Real game code is messy, full of difficult details and compromises. If our engine could handle a commercially released game, it proves you can ship a game with it. We're developing 2 orders of magnitude faster (93x) by lines of code and features. We have fewer bugs, and iteration is super fast. Build times have gone from 30 mins (Unreal) to 36 seconds. Feature rate is through the roof, days instead of months. Our original engine for Firefall took 2-3 years and cost over 5M to build (adjusted). Our new engine was written in 5 months for a couple grand. We built only what we needed, with none of the Unreal bloat. And we added some features: - Modern meshlet based rendering with PBR. - Vis Buffer/Forward+ with clustered lighting. - Id Tech 5 style megatexture streaming. - Planetary sized renderer (entire solar systems possible). - Seamless flight from ground to orbit and space. - No loading screens. - Procedural planet generation with plate simulation. - Client/Server at all times. - Ozz for animation. Jolt for Physics. - Companion Blender plug-in for AI directed asset exports. - 140fps currently, 200-300 projected after optimizations. You can see Firefall's assets and levels ported over into our engine in the video. Texture resolution and pop-in are limitations of the original game (high rez CDN based textures were lost when game went offline, fog hid the original's pop-in). Our meshlet renderer will be able to do much better with LOD and already supports high rez textures. I used Grok/Codex/Claude to tag team the code. This is not just a boon for indie games, it's a real game changer for game preservation. The reaction to seeing this 10 year old discontinued game revived is very emotional for Firefall fans. But we're not just preserving Firefall, we're going beyond, creating an original game that is the spiritual successor, Em-8ER. Gliding, jumpjets... it's all coming back. Moving past Unreal let us move much faster, without the bloat, and with better performance. If you want to signup to follow news on the game, sign up is free at https://t.co/uVcBU0heLW
- Aug 17, 2026445x their median
Which models can your machine actually run? Magnitude now answers that. The new model catalog: - Profiles your hardware automatically - Estimates tok/s for every model before you download - Recommends the best models for your machine Pick one and Magnitude handles the rest: - Downloads the model and quant from Hugging Face - Loads it into the built-in inference engine - Configures speculative decoding (MTP, DFlash, etc.) - Sets concurrency based on your memory Try it on your hardware: npm i -g @magnitudedev/cli
- Aug 10, 2026351x their median
Bernie Sanders has written a letter to Sam Altman, Dario Amodei, and Mark Zuckerberg urging them to immediately pause all AI development in the interest of humanity. And he warns if they do not take appropriate action now, the US Senate will. https://t.co/xwLB0FGmF9
- Sep 5, 2026277x their median
How did I miss that Flatpak is also anti-AI?! Generational fumble from so much of the old Linux world to be against the greatest gift to app development we've ever seen. So I guess we're also going to have to build a full app store now! OKAY! https://t.co/z7dlHy9Qp4 https://t.co/Jig6rkXGLp
- Sep 22, 2026182x their median
We spent the last few days testing JEV internally. We tried to reproduce it, break it, and improve it. 5 things we learned: 1. JEV’s core insight is simple - and probably right. If the answer space is bounded, don’t generate the answer token by token. Remove autoregressive decoding and you can remove 1–2 orders of magnitude of inference work. 2. RLCD isn’t the moat. Data might be. Laya already open-sourced the implementation + weights. The missing piece is the synthetic data recipe. And our experiments strongly point in the same direction. 3. “Open source already beat JEV” is a benchmark illusion. Same checkpoint: In-distribution: 0.769 OOD: 0.541 Change the distribution and the apparent breakthrough largely disappears. 4. Compute-optimal ≠learnability-optimal. We moved state outside the problem sequence to save compute. Result: -27 points. Likely because we saved FLOPs while throwing away useful MLM pretraining priors. Then we tested 6 more ideas in one night: anchor selection, continuity smoothing, asymmetric windows, bucketed temperature, two-stage retrieval, full RLCD. All failed or failed to generalize. Only one thing consistently worked: More data. 1,200 → 123,475 examples OOD: 0.4069 → 0.5498 5. The weirdest result: A decoder with zero additional training still beat every trained small encoder: 0.5671 vs 0.5628 / 0.5411 / 0.4069 Our takeaway: JEV’s breakthrough may not be a particular architecture or training algorithm. It may be a much simpler idea: When the answer space is bounded, search it. Don’t generate it. And right now, the biggest bottleneck to making that work broadly doesn’t look like architecture. It looks like data.
- Sep 13, 2026181x their median
Dario Amodei on Face the Nation this morning: CBS: Would you be willing to give up the technology to the government? Dario Amodei: To the to the right combination of governments. So the worry I have- CBS: You're willing to hand over this company? Dario Amodei: I want to be very clear about this. The the worry I have - I am concerned that one single government could abuse this technology just as easily as a single company could. But I think a combination of democratically elected governments - I don't know about handing over, but some kind of oversight, some kind of joint governance. Again, that would be the work of years, but I wonder if that's the direction we need to go in.
- Aug 25, 2026152x their median
BREAKING: Apple App Store sales have declined for the first time in a decade.
- Aug 16, 202653x their median
HR told me I couldn’t post a job for “Senior Technical Artist” because it might offend the elderly. My art lead and I thought it was a joke. It wasn’t. This is why we need an HR reset. Full video here: https://t.co/FyL2ymBd7J https://t.co/z8mb5KuQjv
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 8 interactions against 357K followers, an engagement rate of 0.002%. Measured over 8 original posts, its engagement rate beats 17% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 12K times each, and 0.067% of those impressions turn into an interaction. That is about 3.36% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 8 posts sampled, 38% carry an image or video and 25% link out. The account's strongest tracked post pulled 36K interactions, about 4480x its own typical post.
- What is Brian Corrigan l 🎮 Biz's engagement rate on X?
- Brian Corrigan l 🎮 Biz (@svperfecta) has an engagement rate of 0.002%, based on the median interactions across 8 original posts from the last 30 days against 356,611 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.002%, Brian Corrigan l 🎮 Biz sits below the 10th percentile of the 204,880 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 @svperfecta have real engagement?
- Its engagement rate beats 17% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the bottom quarter for its size 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 @svperfecta post?
- Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 1.47 posts per day across the measured window.