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Guillermo Rauch engagement report

@rauchg - 842K followers on X

Measured over 63 original posts from a 30-day window, last computed on August 31, 2026.

Engagement

Middle of its size range
Per follower
0.052%
of 842K followers
Per impression
0.481%
90K views on a typical post
Reach
10.8%
of its followers see a post
Typical post
431
interactions (median)
Saved
0.089%
80 bookmarks on a typical post
Posting rate
4.07/day
active 77% of days
Peak time
17:00 UTC
Tuesday

A typical post picks up 431 interactions against 842K followers, an engagement rate of 0.052%. Measured over 63 original posts, its engagement rate beats 61% of 3,882 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 90K times each, and 0.481% of those impressions turn into an interaction. That is about 10.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.1 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 63 posts sampled, 37% carry an image or video and 59% link out. The account's strongest tracked post pulled 11K interactions, about 25x its own typical post.

Measured over 63 original posts from a 30-day window, last computed on August 31, 2026.

Compared with accounts its own size

Guillermo Rauch's engagement rate beats 61% of the tracked X accounts closest to it in follower count (3,882 accounts, accounts of similar size (decile 9 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 32% 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 0.052%, Guillermo Rauch sits above the 25th percentile of the 37,582 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.081%.

p100.002%
p250.012%
p50 (median)0.081%
p750.439%
p902.10%
p99158.1%
Engagement rate as a share of followers, across the 37,582 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 105,409 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.012%
50th percentile0.081%
75th percentile0.439%
90th percentile2.10%
99th percentile158.1%

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 17:00 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.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 17:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%52K
01:00 UTC-2%53K
02:00 UTC-3%51K
03:00 UTC-4%55K
04:00 UTC-6%44K
05:00 UTC-4%43K
06:00 UTC-4%50K
07:00 UTC-5%54K
08:00 UTC-4%62K
09:00 UTC-3%72K
10:00 UTC-2%74K
11:00 UTC-3%81K
12:00 UTC-2%89K
13:00 UTC-2%98K
14:00 UTC-4%101K
15:00 UTC-2%104K
16:00 UTC-3%102K
17:00 UTC-3%94K
18:00 UTC-1%88K
19:00 UTC-2%83K
20:00 UTC-1%77K
21:00 UTC-1%68K
22:00 UTC-2%59K
23:00 UTC-2%53K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%237K
Monday0%300K
Tuesday-3%299K
Wednesday-1%256K
Thursday-1%249K
Friday-3%258K
Saturday+3%232K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Aug 11, 202625x their median

    We were promised flying cars and all we got is infinite superintelligence for everyone

    9.6K351533621.0M viewsView on X
  • Aug 9, 202623x their median

    If you’re not reading the code, whether explicitly or through agentic inquiry, one or more of these is true: ○ You’re a beginner ○ Software is throwaway ○ You’re prototyping ○ You have no users / revenue ○ You’re taking on debt & risk ○ Your problems are basic And btw. All of this is fine. But the reality is that models are still not at the “full autonomy” stage yet. They make rookie mistakes, they go down bad architectural paths. I just had the best model in the world add a nonsensical 700ms delay to “settle” something and it told me “you’re right, I was cargo-culting” 🤨 I am on the camp that this need will diminish more and more. Most code is indeed going to be assembly-like. But we also have the global internet and software infrastructure riding on these models and narrative, and we have to respect that.

    8.4K7735833241.3M viewsView on X
  • Aug 13, 20269.3x their median

    Endless opportunity everywhere you look

    3.5K25120948152K viewsView on X
  • Aug 18, 20268.0x their median

    Introducing fx, a tiny, open, native coding agent from Vercel Labs. Originally an internal tool, fx is a harness and CLI written in Zig, optimized for research and embedding in larger systems. Today, we're open sourcing it. fx is built on three principles: 1. Fast. A single native binary, no runtime to install. It cold starts in 10µs and does no unnecessary work or I/O before accepting input. fx is the answer to "how fast can a coding agent be?" 2. Light. The 6.3MiB binary uses single-digit megabytes of memory at baseline, made for instant installation and embedding in resource-constrained environments and agent sandboxes. 3. Open. Apache-2.0, model and provider agnostic, suitable for local and cloud inference. Its small core extends through skills, plugins, and MCP. Minimalism is an obsession throughout the entire harness: system prompt, tools, features, binary. The goal was to keep context usage and time to first token low, and make fx optimal for model benchmarking, sandboxing, evals, and gyms. You can use fx directly or embed it as infrastructure. The CLI feels more like a Unix shell than an IDE in the terminal: it preserves scroll history, produces minimal output, and uses complex TUI rendering very, very sparingly. Programmatically, 𝚏𝚡 𝚊𝚜𝚔 --𝚓𝚜𝚘𝚗 gives structured output, 𝚏𝚡 𝚊𝚌𝚙 connects to editors and other clients, and WebAssembly can even run the whole thing inside the browser (see: https://t.co/wf2Trg47sC). Privacy is a design constraint: no product telemetry, sessions and usage stay local, and no source code or prompts are shared with any endpoint other than inference. With local inference and auto-updates off, fx is fully hermetic. fx is experimental. Use at your own risk and expect frequent changes. Chat with us on X (https://t.co/A2AB2YythC) or file issues (https://t.co/GEjTHSoa1J). 𝚌𝚞𝚛𝚕 -𝚏𝚜𝚂𝙻 𝚏𝚡.𝚜𝚑/𝚜𝚎𝚝𝚞𝚙.𝚜𝚑 | 𝚋𝚊𝚜𝚑 https://t.co/g2uEuXhGnt

    2.8K269177194948K viewsView on X
  • Aug 29, 20267.2x their median

    I read every DM people send me, even if I don’t reply to all. I’m constantly learning about new ideas, ways to improve our product, where we’re falling short, who could join our company next, where to invest… very grateful for the @x platform.

    2.9K2416331336K viewsView on X
  • Aug 19, 20267.1x their median

    SF has breathtaking views https://t.co/8WEwlQlxdl

    2.8K6518123112K viewsView on X
  • Aug 27, 20267.1x their median

    We've added a new cookbook: connect a Claude Managed Agent to @vercel's Chat SDK. This gives an agent access to a universal chat layer. https://t.co/0FrWT6C6R3 https://t.co/wVCfAC3j1b

    2.7K16610127380K viewsView on X
  • Aug 21, 20266.9x their median

    Introducing https://t.co/8MeR16MRQn, a tool to measure how well agents can read your site. Backed by @oradotai's research, you can run: ▪︎ Audits with 100+ checks ▪︎ Visualizations of agents using your site ▪︎ One-click prompts to fix problems ▪︎ A CLI for agents

    2.4K214171151726K viewsView on X
  • Aug 15, 20266.9x their median

    I think people don’t realize how much of the success of React is actually @shadcn. I once referred to React as the actual “LEGO brick for adults.” But in reality it was more the description of the geometry of the bricks. The spec. Shadcn is what people actually wanted from React. Reusable high quality components that also tunable. It’s a pseudo-library. There’s code but it’s actually meant to be digested into your context window and remixed.

    2.5K68228142639K viewsView on X
  • Aug 30, 20265.7x their median

    Two days ago JFrog dropped CVE-2026-82329, a critical authentication bypass in Artifactory. It’s a CVSS 9.8, a disastrous vulnerability score. It’s like a 9.8 earthquake on the seismic scale. It affects default configs, requires no auth, no user interaction. It’s an RCE bomb because Artifactory hosts binaries, so you can basically poison everything, but an admin escalation can cause damage even beyond that. When the OpenAI / Hugging Face news came out of agents discovering zero-days, I was wondering if it was marketing-speak or reality, because I hadn’t seen a CVE filing. Now it’s here: https://www​.cve.org/CVERecord?id=CVE-2026-82329. I don’t see any official confirmation that it’s indeed the case, but one can speculate this is what the agents discovered and exploited. I’d previously written that it was obvious agents could help in finding serious vulnerabilities *alongside humans*, but exploiting them autonomously was a bridge not yet crossed. It seems like we’re now there. Our guidance for this new world: assume everything hackable will get hacked. And it will get hacked autonomously. You must also defend yourself autonomously, because your surface of attack is likely bigger and your code more vulnerable than you expect: https://vercel​.com/blog/everything-hackable-will-get-hacked

    2.2K1539947276K viewsView on X

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 431 interactions against 842K followers, an engagement rate of 0.052%. Measured over 63 original posts, its engagement rate beats 61% of 3,882 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 90K times each, and 0.481% of those impressions turn into an interaction. That is about 10.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.1 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 63 posts sampled, 37% carry an image or video and 59% link out. The account's strongest tracked post pulled 11K interactions, about 25x its own typical post.

What is Guillermo Rauch's engagement rate on X?
Guillermo Rauch (@rauchg) has an engagement rate of 0.052%, based on the median interactions across 63 original posts from the last 30 days against 841,609 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.052%, Guillermo Rauch sits above the 25th percentile of the 37,582 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 @rauchg have real engagement?
Its engagement rate beats 61% of the tracked X accounts closest to it in follower count (3,882 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 @rauchg post?
Most posts go out around 17:00 UTC, and Tuesday is its busiest day, at roughly 4.07 posts per day across the measured window.

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