Future Forecasting Group
@TheOfficial_FFG · joined 11 Mar 2018
World's most successful commercial civilian remote viewing organization delivering forward intelligence on wealth, health, political shifts, and global events
35 251Followers
599Following
31 105Posts total
40.9KViews on collected posts
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He Was Working Blind — "The AI Becomes Us. We Become The AI."
He was given coordinates and nothing else. What he described was circuitry, implants, and a human race wired into its own technology https://t.co/iw1x13fKy1
1.1K views · 6 likes
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1:46
@TheOfficial_FFG @joerogan I think Tom meant well he’s just being a boomer that doesn’t understand what the tech is capable of pulling in
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Three targets isn't a study. I'm not claiming I've settled anything-- but between Daz's experiments and the ones shown here ... its not looking good for Ai right now...
See the full video 👇
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Tom — if this reaches you, I'm not calling you a liar. I think you saw something that looked like signal. I'd have thought so too.
Publish the transcripts. The target list. How many times you ran it.
Or come and do one with us, on camera. Our Human Remote Viewers are Pretty ht
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Here's the part that stayed with me.
Before I sent the first target, it predicted its own failure:
"I expect my output to contain plausible-sounding structure that is statistically likely for targets people choose for RV trials."
Then did exactly that. Three times. https://t.c
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Then Claude.
Three completely different targets: a man suspended in a glass box over the Thames, a wooden spiral staircase in a chapel, and a Japanese bullet train.
It described massive concrete infrastructure holding back water. Three times. https://t.co/pmpSEFnADp
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So: three targets I picked myself, none from any public pool, known to nobody else.
Same reference number to each AI and nothing else. No coaching. No second attempts.
Alexa refused outright on the first one — and offered to explain why remote viewing is pseudoscience. https://
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0:35
Three: he describes multiple attempts on the podcast.
We've been shown one result.
No transcripts. No target list. No count of how many times it ran.
Every viewer I work with has misses on record. That's what makes the hits mean anything. https://t.co/KkLQYZhbIm
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Two: he used free public target pools.
Those pools are web pages. The reference numbers, the images, and thousands of published sessions against them — all sitting on the open internet.
Which is the exact material these models were trained on. https://t.co/P2thmHoZzx
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One: the viewer was a live microphone.
He picked the target in his house and put it in a box — in a room with an always-on listening device.
He says so himself, thirty seconds later, about the same Alexa:
"…a bit, because she's listening." https://t.co/lDV4sArhwR
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0:18
Rogan's guest, Chris Williamson, asked the obvious question on air: "he needs to tell us how he did it."
Joe's answer was "well, he kind of did."
He didn't. So I went back through the tape.
Three problems, all in Campbell's own words. https://t.co/zZiGEtaRXP
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Daz had already run it. Eight AIs across two experiments.
They all failed the same way — every model reaches for something big, vertical and man-made.
That's what "interesting target" looks like when you're generating instead of perceiving.
Not a near miss. A fingerprint. http
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Then a few days later Rogan tells the story to Chris Williamson, and his producer pulls up a site that had already tested it.
That site is https://t.co/k4c8ERlIT5.
It belongs to Daz Smith @remoteviewed — 20+ years of documented sessions, and one of my colleagues. https://t.co
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First, credit where it's due.
Tom Campbell's premise is genuinely interesting: remote viewing isn't something you can talk your way through. Either you're getting information you have no ordinary way of having, or you aren't.
So if a machine could do it — that means something.
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@joerogan Rogan told millions of people that an Amazon Alexa did real remote viewing.
A spoon in a sealed box. It described the holes in the handle.
Nobody actually checked.
So I bought an Echo and ran it. Three blind targets. Here's what happened. 🧵 https://t.co/QLF4Dr0YkG
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0:19
This is a sketch one of our Remote Viewers drew while working blind on a new project .
He Describes something with pattern recognition tagging and storing information , something that reminds him of the matrix. What do you think the target could be ? https://t.co/iQJAnHp7Rl
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Your Robotaxi Takes 290 Photos Every Second. Where Does That Data Go?
A Waymo robotaxi carries 29 cameras and captures a snapshot from every one of them every 100 milliseconds. Edward Riordan's question isn't whether that data is being collected. It's who ends up buying it. http
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0:49
Join the Team Today: https://t.co/hxRdGvKT0u
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@TheOfficial_FFG they are the sin qua non of remote viewing.
None better, and worth every penny of the subscription.
I have been a paid member for 4 years...
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Future Forecasting Group (FFG) is the world's largest and most successful civilian led commercial remote viewing organization, maintaining the largest known archive of remote viewing sessions. With roots going back to around 2018, FFG runs multiple dedicated viewing teams that h
5.2K views · 33 likes
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Im Vergleich zu Konten gleicher Größe
21 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. übliche Reichweite für diese Größe, schwächere Reaktion als bei den meisten.
Medianaufrufe1 126dieses Konto991Median für 10K–100K
Reichweite, %3.19%dieses Konto3.67%Median für 10K–100K
Interaktion, %0.97%dieses Konto1.61%Median für 10K–100K
| Kennzahl | Dieses Konto | Median für 10K–100K | Verhältnis |
|---|---|---|---|
| Medianaufrufe pro Beitrag | 1 126 | 991 | 1.14× |
| Reichweite (Aufrufe ÷ Follower) | 3.19% | 3.67% | 0.87× |
| Interaktionsrate | 0.97% | 1.61% | 0.60× |
Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
1.5K17 Sep
1.3K
1.2K
1.1K
1K
951
865
785
722
680
946
1.4K
483
1.1K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
1.28%17 Sep
0.76%
0.68%
0.88%
0.86%
0.84%
1.39%
1.15%
0.97%
2.35%
1.27%
1.16%
1.04%
0.71%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.61%.
What the audience does
Likes69.0%323 in total
Reposts7.3%34 in total
Replies7.9%37 in total
Quotes1.3%6 in total
Bookmarks14.5%68 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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