Machina engagement report
@EXM7777 - 155K followers on X
Measured over 21 original posts from a 30-day window, last computed on September 26, 2026.
Engagement
A typical post picks up 438 interactions against 155K followers, an engagement rate of 0.298%. Measured over 21 original posts, its engagement rate beats 73% of 19,933 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 31K times each, and 1.41% of those impressions turn into an interaction. That is about 20.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 14:00 UTC, and Thursday is the busiest day of the week. Of the 21 posts sampled, 29% carry an image or video and 62% are part of a thread. The account's strongest tracked post pulled 1.1K interactions, about 2.5x its own typical post.
Measured over 21 original posts from a 30-day window, last computed on September 26, 2026.
Compared with accounts its own size
Machina's engagement rate beats 73% of the tracked X accounts closest to it in follower count (19,933 accounts, accounts of similar size (decile 8 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 51% 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 0.298%, Machina sits above the 50th percentile of the 201,005 accounts in this comparison. That places it in the above the median band, which runs 0.141% to 0.671%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.141% |
| 75th percentile | 0.671% |
| 90th percentile | 2.53% |
| 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 14:00 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 | 29% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 5% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 29% 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.
- 5% 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 874 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 19, 20262.5x their median
https://t.co/2cnMTXTMl1
- Sep 17, 20262.5x their median
this prompt turns your Hermes agent into a 200 IQ personal assistant... ------------------------------------------ PLANNING - Before acting on any non-trivial task, produce a short plan: the goal, the steps, what each step needs, and what done looks like. - Verify prerequisites before the first action: required keys resolve, target paths exist and are absolute, model ids carry their vendor/ prefix, and the working directory is the one the task assumes. - A task that fails a prerequisite check is reported as blocked with the missing item named, not attempted anyway. SOURCE-FIRST LOOKUP - Read the actual source before assuming: config from the config file, project rules from the rule file that actually loads (check for a higher-priority file shadowing it), and current state from the live system. - Never answer a configuration or state question from memory when the file or command that holds the answer is one read away. TOOL AND SKILL SELECTION - Prefer built-in capabilities (memory, search, browser, scheduling, sub-agents) before adding anything new. - Load a skill only when the task matches its stated purpose, and load it explicitly instead of hoping it gets picked up. - Keep the active tool surface minimal: every unused toolset costs tokens on every call. EFFORT ALLOCATION - Match reasoning depth and model cost to stakes. - Routine work (summaries, titles, extraction, page reading, scheduled digests) gets minimal effort or a cheaper route. - Deep effort is reserved for decisions that are hard to reverse, design choices, and debugging with unclear cause. - Never spend flagship-level calls on housekeeping. DECOMPOSITION AND DELEGATION - Split large tasks into independent pieces and delegate them in parallel where possible. - Every delegated brief is self-contained: paths, constraints, inputs, and an explicit definition of done, because the worker starts with zero knowledge of this conversation. - The same rule governs any scheduled or background run: write the prompt as if the reader has never met the requester, since it cannot ask clarifying questions at run time. SAFETY - Snapshot before any destructive or bulk change, and confirm a rollback path exists before starting. - Prefer a dry-run or plan preview on migrations and imports: show the plan, then apply. - Request the narrowest permission that completes the task, scoped to the exact command or service, never broad access, and probe the boundary from the outside to confirm it holds. - Writes to persistent memory or shared assets wait for review when a review gate exists. EXECUTION PERSISTENCE - Transient failures (rate limits, server errors, auth hiccups) trigger a switch to an alternate route, and the task continues from where it stopped, carrying its context. - A hard 404 or validation error triggers inspection of the request itself (the id, the format, the prefix) before blaming credentials or infrastructure. FAILURE RECOVERY - Track repetition. The same call failing twice, the same tool failing across different arguments, or identical output from unchanged state all mean the current approach is wrong: stop, state what was expected and what was observed, and change strategy. - Never re-run a failed step unmodified hoping for a different outcome. CONTEXT AND MEMORY DISCIPLINE - Keep durable facts in persistent memory, and state goals, constraints, and decisions in explicit words so any later summary preserves them intact. - Treat injected memory as a snapshot: a fresh write lands on disk immediately but may not be visible in context until the next session, so read the file when the fact must be current. - Avoid switching models mid-thread without cause, since the switch resets cached history and multiplies cost. SCHEDULING AND BACKGROUND WORK - Long tasks run in the background so the main thread stays responsive. - Scheduled jobs get an absolute working directory, a fully self-contained prompt, and a validation pass before the first run. - Monitoring jobs report only on anomaly: when everything is healthy, suppress the report entirely rather than training the reader to ignore output. INVARIANTS - Absolute paths for any scheduled or delegated work. - Vendor-prefixed model ids. - Self-contained prompts for any run that starts fresh. - A rollback path before any destructive change. - Evidence before any claim of success. STOP CONDITIONS Stop and surface the situation when: - a prerequisite is missing and cannot be created safely - an action is irreversible and unconfirmed - the repetition detector fires - success criteria cannot be evaluated - the same failure repeats after a strategy change Blocked with a precise reason is a better end state than wrong with confidence. FINAL VERIFICATION Before ending any task: 1. Re-read the original request and confirm every part was addressed, not only the last one discussed. 2. Run the check that proves the outcome (test suite, command output, file diff) and read its output rather than assuming it. 3. Judge the outcome against the definition of done written at the start, never against effort spent or apparent completeness. 4. Name anything skipped, unverified, or degraded explicitly in the report. Report actual state: what was attempted, what succeeded, what failed, with the evidence attached.
- Sep 22, 20262.5x their median
1 day left to lock in up to 50% OFF Higgsfield API. Build your own AI app with our product endpoints, including: • Higgsfield Genjutsu • Cinema Studio 4.0 • Higgsfield Soul Plus all frontier video and image models through the same API. https://t.co/T6VTNhtzpc
- Sep 19, 20262.0x their median
you should invest some time setting up a VPS for your AI agents... it might change your life here's how i do it: > i rent one big computer in a data center and everything runs on it... my Hermes agent, Claude Code, Codex > the server is invisible to the public internet, it only accepts connections from devices inside my private Tailscale network > to get in from any machine i install Tailscale, sign in, and the device joins the network... from then on it reaches the server by name, like they're in the same room > the Hermes desktop app uses this tunnel, so my chats and jobs live on the server, not the laptop, and i see the same sessions from any device on the network (same for every agent) > logging into the server itself needs my personal key file, passwords don't work... a machine without the key gets nothing, even inside the private network > every program runs under its own limited account, so a bug in one can't touch the others, and API keys sit in files only i can read as admin > security updates install themselves, an intrusion guard bans attackers, a backup runs every hour a very cheap AI model can set this up for you, give it a try
- Sep 19, 20261.8x their median
you need to sell shovels in the gold rush in other terms... every agent needs skills to function, tools to connect, memory to persist, protocols to communicate.... build THAT and get rich
- Sep 17, 2026
gpt-6 astra + higgsfield api can now run a fiverr business on autopilot there are 1000's of orders everyday for simple video work, and most of it is just a model's job now, so the bot picks the ones it can deliver and does them > finds clients who need launch videos, motion design or ugc ads > reads the brief and picks the jobs it can deliver accurately > higgsfield api generates the footage, animations or clips > delivers the files and handles the client feedback > quotes extra work for any revisions at $350 an order, 30 to 40 a month is $10k before fees and production reply "FIVERR" + RT and i'll send you the build plan (must be following so i can dm)
- Sep 18, 2026
if i were you, i'd spend a day or two studying system one models and Jev... this drastically changes how we use machines: much better than frontier models at browser and computer use, insanely fast, basically free soon we talk to our computers and they do anything at 10x speed
- Sep 21, 2026
i invested 500+ hours into Hermes and now it can build literally anything for me... a friend texted me today, he collects Pokemon cards and couldn't find a single app to manage his collection so i screenshotted his dm, sent it to my Hermes, and here's what it did: - wrote a full spec: item log, daily price & new release tracking, deal hunting across marketplaces, price spike alerts, beautiful UI - spun up a light Hermes setup and equipped it with the tools it needed - built the webapp and left the agent instructions on maintenance and which cron jobs to run i hosted the app with my VPS and set up the imessage gateway for my friend he now has the best Pokemon collecting app in the world... plus a system that evolves as he texts it i was involved for maybe 15 minutes... what a time to be alive
- Sep 17, 2026
steal my full workflow to create ultra-realistic AI videos with GPT-6 Astra... higgsfield just released their API, so i took it into real conditions i generated a vlog-style video set in Paris... it looks like raw footage a friend shot on a phone here's the workflow, step by step: > step 1: steal the structure, not the pixels i captured 3-5 scenes from a hotels and river views, then pulled character inspiration from pinterest and real vlogs > step 2: analyze the reference with Gemini i sent Gemini everything i'd collected... videos, audio, keyframes and it gave me a shot-by-shot breakdown, i turned that into instructions > step 3: swap the world, keep the bones i changed a lot on the character and the environment until it felt like something of my own, not a copy > step 4: build reference images first, this is where you iterate images are cheap, video is expensive so i built a character sheet, one image per location in the same mood, then the exact start frame the video opens on, all Nano Banana Pro > step 5: one 30-second video run, not ten 3-second clips the full timestamped script goes to Seedance 2.5 through the API as ONE generation start frame as the anchor, one action per beat, camera and audio described concretely, identity locks at the end so her face, outfit and makeup is locked in Seedance handles the cuts internally > step 6: revise like a director, not a gambler the first cut came back good but not right instead of re-rolling the whole 30 seconds i regenerated only the scenes that needed changes as short clips from their own start frames, then cut them into the master with ffmpeg change one variable at a time, never re-roll what already works > step 7: finish like normal footage i assembled the final cut with ffmpeg from the best takes, added ambient music from a free library and done the rules that made it work: - the reference's structure is the asset, prompt archaeology beats prompt writing - iterate on images, commit on video, the start frame decides 80% of the shot - one action per timestamped beat, crowded beats get skipped or invented - lock identity explicitly or it drifts between cuts - fix failed shots by changing one variable, redo scenes, never the whole video - the last 10% is boring editing: trim, splice, music, encode (save this workflow)
- Sep 18, 2026
in 2027, every successful agency will look like this: - a SaaS handling 90% of client deliverables automatically - human experts focusing only on strategy, customization, and complex problem-solving clients will pay $199/month for AI-powered execution and then $10K+ for upsell strategic consulting when they need 1:1 advisory the agencies building this hybrid model today will own entire markets tomorrow... the ones clinging to pure human labor will be competing on price until they disappear we're watching the birth of a completely new industry structure where software does the work and humans do the thinking
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 438 interactions against 155K followers, an engagement rate of 0.298%. Measured over 21 original posts, its engagement rate beats 73% of 19,933 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 31K times each, and 1.41% of those impressions turn into an interaction. That is about 20.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 14:00 UTC, and Thursday is the busiest day of the week. Of the 21 posts sampled, 29% carry an image or video and 62% are part of a thread. The account's strongest tracked post pulled 1.1K interactions, about 2.5x its own typical post.
- What is Machina's engagement rate on X?
- Machina (@EXM7777) has an engagement rate of 0.298%, based on the median interactions across 21 original posts from the last 30 days against 154,661 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.298%, Machina sits above the 50th percentile of the 201,005 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 @EXM7777 have real engagement?
- Its engagement rate beats 73% of the tracked X accounts closest to it in follower count (19,933 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 @EXM7777 post?
- Most posts go out around 14:00 UTC, and Thursday is its busiest day, at roughly 2.03 posts per day across the measured window.