Farlane engagement report
@base10_ - 161K followers on X
Measured over 5 original posts from a 30-day window, last computed on October 26, 2026.
Engagement
Early reading. We have captured 5 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 104 interactions against 161K followers, an engagement rate of 0.065%. Posts are seen about 41K times each, and 0.257% of those impressions turn into an interaction. That is about 25.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 5 posts sampled, 100% carry an image or video, 40% are part of a thread and 60% link out. The account's strongest tracked post pulled 830 interactions, about 8.0x its own typical post. Only 5 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 5 original posts from a 30-day window, last computed on October 26, 2026. Recurring tag: #wizstarhalloween.
Where this sits in the catalog
At 0.065%, Farlane sits above the 25th percentile of the 206,113 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.143%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.143% |
| 75th percentile | 0.68% |
| 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 15:00 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 60% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% 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.
- 60% 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 582 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
- Oct 22, 20268.0x their median
Introducing Product Hunt for agents. The leaderboard of the most popular tools agents are using today. https://t.co/AebM9ZNHCh https://t.co/8RdvLt258Y
- Jun 30, 20265.9x their median
I think we're entering a new phase of AI video. For the first time, I generated a 4K product ad from a single image directly inside CapCut. No extra tools. No complicated workflow. Just image → video → edit → export. Here's what happened 👇 https://t.co/0ehfTiLFpG
- Sep 16, 20263.0x their median
Viktor helped us prevent $8.6K in low-return ad spend and save 2.5 hours per reporting cycle. I work on marketing operations, where keeping campaigns on track means constantly monitoring performance, fixing underperformers, and turning data into decisions. We were managing 12 campaigns across 12 markets with 13 metrics. The usual setup was spreadsheets, manual checks, and recurring reports. It worked, but someone still had to spend hours watching everything. So we gave Viktor access to the workflow. He reviewed our Google Ads data, flagged 8 of 12 campaigns that needed attention, and identified where the recurring work was going: monitoring and pacing, fixing underperformers, and reporting. The important part wasn’t another dashboard. Viktor turned the analysis into actionable work while keeping people in control of the final decisions. That meant we could spend less time working through the operational layer and more time making the decisions that actually mattered. That changed how I think about AI employees. The value isn’t another tool giving your team more information. It’s having someone continuously work through the work around that information. Chatbots answer. Copilots assist. Employees own outcomes. Hire @viktor_com for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #MarketingOps #AI Paid Partnership
- Sep 17, 20262.8x their median
Most AI agents can produce a good first draft. But the real test is what happens after that: Can you review the work, give the Agent feedback, have it revise the result, and actually get to something usable? I tested that workflow in Octo. 🧵 https://t.co/yP3LK6Yd1G
- Sep 17, 20262.7x their median
https://t.co/0rUcUPd9Xv
- Oct 22, 20262.5x their median
3 minutes 31 seconds to turn a Google Ads review into a decision brief examining $14,200 in underperforming campaign spend. Viktor's most useful finding wasn't about clicks. It was what happened after them. Those campaigns returned 2.53x ROAS, compared with 3.82x across non-brand campaigns overall. Their cost per click was only 5.1% higher than the non-brand average. Lower conversion rates and lower order values explained most of the performance gap. That changes where we should look for answers. Cutting bids alone won't address most of the gap. Landing pages, offers, product feeds and market fit deserve a closer look. Viktor also separated decisions ready for human review from those needing more information, instead of forcing recommendations when our targets weren't clear. The brief gave us four decisions ready for review, four needing more information, and four campaigns requiring no action. That's useful AI work: getting from raw data to a clearer next step, while people remain responsible for the decisions. Hire @viktor_com for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #MarketingOps Paid Partnership
- Sep 9, 20262.3x their median
I used to think the biggest value of AI was getting better answers. Then we started giving Viktor work. We asked him to review our marketing operations, find the tasks taking the most time, and turn his analysis into something our team could act on. Viktor identified roughly 12 to 19 hours of recurring marketing work every week: → Monitoring + budget pacing: 4 to 6 hrs/week → Fixing underperforming campaigns: 3 to 5 hrs/week → Manual reporting + data assembly: 2 to 3 hrs/week So we handed him part of that workload. We asked Viktor to build our weekly performance report. He reviewed 12 campaigns and produced: → $58,200 in ad spend → $253,570 in revenue → 4.36x ROAS → 3,171 conversions → 8 of 12 campaigns flagged for attention But he didn't just report the numbers. He turned the data into a prioritized list of actions, saving us 2.5 hours per report and helping prevent $8.6K in spend on low-return ads. That's what changed how I think about AI at work. Viktor wasn't answering questions. He took on a recurring workflow, worked through the data, and helped the team act on it. Most AI tools help your team figure out what to do. I think the bigger opportunity is giving AI ownership of the work itself. Would you let an AI employee handle a recurring workflow on your team? Hire @viktor_com for your team. $100 in credits included, no card. Full link in first comment. Paid Partnership
- Oct 24, 2026
FaceSearch: Every digital footprint tells a story. Uncover online identities, connect digital traces, and detect potential fraud risks with powerful identity intelligence. 🔎 Try FaceSearch: https://t.co/gNUZaDF34e https://t.co/EZujtDlIlb https://t.co/RrJcMaEuWB https://t.co/FvALqpq8jR
- Sep 4, 2026
🏢 More control with Querit Monitors Enterprise. ⚡ Custom Run QPS 📊 Up to 100 results/run 🌐 Up to 50 include/exclude sites 🎯 More Search controls What would you put on your first Monitor? Like this post + comment what you’d want to track 👇 We’ll send Enterprise test credits to everyone who joins in. Try it: https://t.co/rkVCPCMjk6 Discord: https://t.co/IvHO6hlmep
- Oct 21, 2026
I listed my Claude skill on Capafy, and it’s made over $289K in sales in the past 30 days! It’s called CloneCut. It helps you create your own version of a viral video you like, using your photos and ideas. The first version came together pretty quickly. Then I started posting videos on Instagram and TikTok to show people what it could do. Those videos helped me attract my first paying users. Many of them tried it once, then kept coming back and paying for more. Seeing views turn into actual sales showed me just how effective social media videos can be. The dashboard is now at $289,462.51. Thank you to everyone who’s tried CloneCut, shared it, or supported me along the way. Taking a moment to celebrate this one. Then back to making CloneCut even better. Try CloneCut on Capafy: https://t.co/Oeh273W3Of
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 104 interactions against 161K followers, an engagement rate of 0.065%. Posts are seen about 41K times each, and 0.257% of those impressions turn into an interaction. That is about 25.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 5 posts sampled, 100% carry an image or video, 40% are part of a thread and 60% link out. The account's strongest tracked post pulled 830 interactions, about 8.0x its own typical post. Only 5 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 Farlane's engagement rate on X?
- Farlane (@base10_) has an engagement rate of 0.065%, based on the median interactions across 5 original posts from the last 30 days against 160,643 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.065%, Farlane sits above the 25th percentile of the 206,113 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 @base10_ have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @base10_ post?
- Most posts go out around 15:00 UTC, and Thursday is its busiest day, at roughly 2.07 posts per day across the measured window.