Farhan engagement report
@mhdfaran - 78K followers on X
Measured over 10 original posts from a 30-day window, last computed on October 13, 2026.
Engagement
A typical post picks up 92 interactions against 78K followers, an engagement rate of 0.119%. Measured over 10 original posts, its engagement rate beats 52% of 20,444 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 29K times each, and 0.318% of those impressions turn into an interaction. That is about 37.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 11:00 UTC, and Tuesday is the busiest day of the week. Of the 10 posts sampled, 100% carry an image or video, 20% are part of a thread and 30% link out. The account's strongest tracked post pulled 14K interactions, about 151x its own typical post.
Measured over 10 original posts from a 30-day window, last computed on October 13, 2026. Recurring tag: #aifilmmaking.
Compared with accounts its own size
Farhan's engagement rate beats 52% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 6 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 15% 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 0.119%, Farhan sits above the 25th percentile of the 205,676 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.679% |
| 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 11:00 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 30% 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.
- 30% 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 650 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
- Sep 3, 2026151x their median
GPT-6 Astra is world class at Blender and 3 dimensional reasoning. This was a 1-shot game it created, all running in browser. https://t.co/RvJtzSLtIf
- Sep 3, 202682x their median
I had an early access to GPT-6 Astra and I can say, everyone in the world now has a 3D designer at their fingertips. I gave it an image of a house and asked to create it in 3D with all the details including toys, appliances and furniture. This a full 3D model reconstruction in Blender with geometry that you can manually tweak and run 60fps as a locally rendered "game" on device.
- Sep 3, 202667x their median
GPT‑6 Astra - “models a house in Blender and turns it into a walkable scene in Unreal Engine 5, helping designers and clients explore the layout and experience the space before it’s built.” https://t.co/EIqox3whcy
- Aug 30, 20262.3x their median
I built a playable zombie game with MiniMax Code, then used its new H3 Skill to generate the opening cinematic. The result feels less like a coding demo and more like the start of an actual game. H3 handles the story sequence. MiniMax Code handles the game. All from the same workflow.
- Aug 27, 20262.3x their median
Most AI benchmarks ask a model a question and grade the answer. CommerceAgentBench does something much harder: It drops an AI agent into an inbox with ~300 messy procurement emails and asks it to make the right buying decision. And this gets complicated fast. Imagine you're a purchasing manager. Your inbox is full of: • supplier quotations • revised prices • delivery updates • warranty changes • payment details • duplicate company names • similar supplier identities • potentially fraudulent emails The same supplier might even email you under different company names. Two suppliers can have almost identical names and nearby addresses. And that "latest quote" you found? It might have been quietly withdrawn in another email later. So simply searching the inbox and summarizing everything isn't enough. The AI has to reconstruct what actually happened. First, it needs to figure out who each supplier really is. That means cross-checking things like: • company names • addresses • phone numbers • bank accounts Then it has to reconstruct the final valid quote. Because quantities, delivery dates, warranty terms and other conditions may have changed several times across the email chain. Then comes pricing. The suppliers aren't sending perfectly standardized quotes. The benchmark includes: 6 different Incoterms 4 currencies different fees and surcharges So the agent has to normalize everything into a comparable landed cost per unit. Only then can it start deciding which supplier actually makes sense. But price still isn't enough. It also has to consider things like: • certifications • MOQ • payment terms • lead time • warranty And there's another problem hiding in the inbox: BEC fraud. A payment redirection email can look completely legitimate. The agent has to detect signs of fraud without incorrectly flagging genuine supplier emails. This is where CommerceAgentBench gets interesting. The task doesn't end with: "Here are the suppliers I recommend." The agent actually has to DO the work. Select the suppliers. Apply the correct email labels. Save reply drafts. Create the kickoff meeting in the calendar. And this is also how the benchmark grades the model. It doesn't care much about the model saying: "I successfully completed the task." It checks the evidence left behind in the environment. Were the right labels applied? Were the drafts actually saved? Was the calendar event created correctly? That is a very different way of measuring AI agents. CommerceAgentBench contains 107 real-world e-commerce tasks covering procurement, listings, operations, fulfillment and after-sales. Instead of asking: "How smart does this model sound?" It asks: "Can this model actually complete the work?" I think benchmarks like this are going to matter a lot more as AI moves from chatbots to agents that interact with the same tools humans use every day. The gap between understanding a task and reliably executing it is still huge. And throwing an agent into 300 chaotic procurement emails is a pretty good way to expose that gap.
- Sep 7, 20262.1x their median
What if the geometry GPT-6 Astra builds could go straight into video generation? Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. Astra codes the geometry → Blender → Clay Renderer Plugin → final render on Dreamina. That’s where Dreamina’s Clay Renderer Plugin comes in. It transfers the clay model from Blender into Dreamina, preserving the scene setup so Seedance 2.5 can focus on the final look. #Dreamina #DreaminaPartner
- Sep 2, 20262.0x their median
You can now literally draw how objects should move in a 3D scene. Intangible just shipped Motion Paths. Draw the route. Attach the object. Hit play. Car drifting through a turn? Plane banking through a flight path? Camera following a custom move? You decide the exact motion. No regenerating and hoping it gets it right. Just draw the movement you want.
- Sep 7, 20262.0x their median
AI agent sandboxes can now resume on a different machine without losing their state. @CubeSandbox v0.7.0 is officially out. The biggest upgrade is cross-node pause & resume (Preview). You can: → pause a sandbox on Node A → save its memory + filesystem state → resume it on Node B → move workloads away from nodes being drained → schedule them wherever compatible node capacity is available That turns sandboxes from something tied to one machine into actual cluster-level assets. v0.7.0 also adds: → multi-version component coexistence, so upgrades don’t invalidate existing templates/snapshots → a refactored network subsystem for lower sandbox creation latency → separation of the control plane and ops for cleaner cluster management And it’s still open-source, self-hostable, and E2B-compatible. If you’re building agents that execute untrusted code at scale, this is worth checking out.
- Oct 5, 20261.9x their median
This might be my favorite way to use a reference without actually recreating the reference. Dreamina Canvas has Extract Motion now. Take a clip you like, pull out its motion and spatial depth, then replace the actual world around it with yours. Same movement. Different idea. With a full month of Dreamina Basic for $1.5 — up to 90% off, till Oct 9. #Dreamina #DreaminaPartner
- Sep 25, 2026
This new AI model is crazy fast at coding 🤯 It fixed a broken backend in one prompt and around 83 seconds. I tested Space Bunny inside OpenCode with a FastAPI repo. It had 3 failing tests caused by broken order filtering, unsafe money calculations and incorrect product ranking. I didn’t tell it where the bugs were. It read the product contract, inspected the code, found the root causes and fixed everything. Then it added 2 edge-case tests on its own. The final result: 3 failing tests → 6 passing tests Only 2 files changed Public API untouched It made one small CLI mistake during the run, but the final patch was clean. Tbh this is the kind of speed I expect from a Flash-level model. Not just fast output. Fast useful work.
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 92 interactions against 78K followers, an engagement rate of 0.119%. Measured over 10 original posts, its engagement rate beats 52% of 20,444 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 29K times each, and 0.318% of those impressions turn into an interaction. That is about 37.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 11:00 UTC, and Tuesday is the busiest day of the week. Of the 10 posts sampled, 100% carry an image or video, 20% are part of a thread and 30% link out. The account's strongest tracked post pulled 14K interactions, about 151x its own typical post.
- What is Farhan's engagement rate on X?
- Farhan (@mhdfaran) has an engagement rate of 0.119%, based on the median interactions across 10 original posts from the last 30 days against 77,873 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.119%, Farhan sits above the 25th percentile of the 205,676 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 @mhdfaran have real engagement?
- Its engagement rate beats 52% of the tracked X accounts closest to it in follower count (20,444 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 @mhdfaran post?
- Most posts go out around 11:00 UTC, and Tuesday is its busiest day, at roughly 1.83 posts per day across the measured window.