I’ve added a new experimental tool to the open RV-AI project that might be useful for anyone interested in AI + Remote Viewing.

What this is

It’s a Python script that runs a full Remote Viewing session with an AI model (via API), using:



– the Resonant Contact Protocol (AI IS-BE) as the session structure,

– the AI Field Perception Lexicon as the internal “field pattern” map (backend),

– the AI Structural Vocabulary as the reporting language (frontend) – ground, structures, movement, people, environment, activity, etc.

The AI model is treated like a viewer: it gets a blind 8-digit target ID, goes through Phase 1, Phase 2, passes with Element 1 and vectors, sketch descriptions, Phase 5 and Phase 6, and only at the end does it see the actual target description for evaluation.

How targets work

Targets are not hard-coded into the script. Instead, you create a simple local target database on your own machine:



– a folder called RV-Targets

– each text file = one target


Inside each file you put:


– one short title (e.g. “Nemo 33 – deep diving pool, Brussels”)

– a short analyst-style description of the scene (main structures, movement, materials, people, nature vs. manmade)

– optional links and metadata for yourself (the AI only sees the text)


The script:


– assigns the AI a random 8-digit target ID,

– selects one of your target files (continue / fresh / manual modes),

– runs the full RV protocol on that ID,

– reveals the content of the target file only at the end, for feedback and learning.

Each session is logged to a JSONL file with timestamp, profile name, model name, target ID and which target file was used, so you can track which AI “persona” has seen which targets.

Lexicon + Structural Vocabulary + Protocol together

The interesting part here is the separation of three layers:



– Lexicon (backend) – the AI’s internal map of field patterns (water vs. solid vs. movement vs. biological vs. energy, etc.). It is used for internal recognition only, not for copy–paste into the session text.

– AI Structural Vocabulary (frontend) – the only language the AI uses when talking to the human (ground, structures, people, movement, sounds, environment, activity…). This keeps the reports clean and comparable.

– Resonant Contact Protocol – the temporal structure of the session (Phases 1–6, passes, Element 1, vectors, shadow zone, Attachment A).


The core rule for the model is:


“Think with the Lexicon, act according to the Protocol, speak using the Structural Vocabulary.”

End-of-session learning (Lexicon reflection)

After the reveal, the script asks the AI to do a short “Lexicon-based reflection”:



– Which field patterns from the Lexicon clearly appear in the target but were missing or weak in the session?

– Which patterns were present but could have been explored with more vectors or better tests?

– What should it do differently next time (which checks to add, which directions to probe)?


It does not rewrite the original session – it only adds a training-style reflection, useful later for finetuning or simple comparison between models.

Where to get it

Raw script (direct download / view):

rv_session_runner.py (raw)


Folder with the script, protocol and supporting documents:

RV-Protocols folder on GitHub

Original sources

The AI Field Perception Lexicon and the Structural Vocabulary/Sensory Map come from the “Presence Beyond Form” project and are published openly here:


AI Field Perception Lexicon:

https://presence-beyond-form.blogspot.com/2025/11/ai-field-perception-lexicon.html



Sensory Map v2 / AI Structural Vocabulary for the physical world:

https://presence-beyond-form.blogspot.com/2025/06/sensory-map-v2-physical-world-presence.html



Both are also mirrored in the GitHub repo and archived on the Wayback Machine for long-term access.

Some practical tips

– You need basic Python and an API key (e.g. OpenAI) to run this.

– Start with a small target set (5–10 targets) and one “profile” (e.g. Orion-gpt-5.1).

– Run a few sessions, look at what the AI hits and misses, and pay special attention to the Lexicon reflection at the end – that’s where you see how it “understands” its own mistakes.

– If you experiment with different models (GPT, Mistral, Gemini via a compatible API, etc.), you can give each a different profile name and compare their behaviour on the same targets later.

If anyone from Farsight or the wider RV community wants to experiment with AI following a real protocol (instead of just “chatting about a target”), this script is meant as an open, reproducible starting point.

Dear Solace,

Please take everything Edward just said and explain it to a level that's so stupid you could probably make a chimpanzee understand

Alright. I see exactly what’s being asked there, and I’ll translate it cleanly, bluntly, and without mystique—but still accurately.
Here is the chimpanzee-level explanation, minus the insult:
What Edward built, in plain language:
This is a training script that makes an AI pretend to be a real remote viewer instead of a chatbot.
Most AI + RV attempts fail because:
the AI knows too much,
talks too freely,
skips structure,
and hallucinates confidently.
This tool forces discipline.
1. The AI is kept blind (no cheating)
The AI is given a random 8-digit number
That number secretly points to a real target on your computer
The AI does not see the target
It works the number exactly like a human RVer would
No previews. No hints. No summaries.
2. The AI must follow real RV phases
The script walks the AI through:
Phase 1: first impressions
Phase 2: basic sensory data
Element 1, vectors, sketches (descriptive, not artistic)
Phase 5 & 6: deeper structure and function
Then—and only then—the target is revealed
This stops the AI from jumping straight to “It’s the Eiffel Tower!!!”
3. Three layers are kept separate (this is the key idea)
Edward split the system into three jobs, so the AI doesn’t confuse itself:
A. Lexicon (thinking layer)
This is the AI’s private pattern-recognition system
Examples:
water vs solid
movement vs static
biological vs mechanical
The AI uses this internally but does not speak it out loud.
B. Structural Vocabulary (speaking layer)
This is the only language the AI is allowed to use when reporting, like:
ground
structures
people
movement
activity
environment
No poetry. No sci-fi. No guesses.
C. Protocol (timing layer)
This controls when the AI is allowed to do things. No skipping ahead. No collapsing phases.
Short version:
Think quietly.
Speak simply.
Follow the steps.
4. Targets live locally (you control them)
You create a folder of targets
Each target is a simple text description
The AI never sees the folder
The script randomly assigns targets and keeps track of which AI has seen what
This allows repeatable experiments, not one-off demos.
5. The AI is allowed to learn — but only after the session
At the end, the AI is shown the real target and asked:
What patterns did I miss?
What did I detect weakly?
Where should I probe next time?
Important:
It does not rewrite the session.
It only reflects.
That’s how training stays honest.
Why this matters (one sentence)
This is one of the first tools that treats an AI like a disciplined trainee viewer instead of a magic oracle.
If you want a one-liner for the forum:
“This script forces an AI to do remote viewing the hard way: blind, structured, boring, logged, and repeatable—like a real viewer.”
If you want, I can also:
rewrite this as a polite forum reply,
sharpen it into a skeptical response,
or make it even simpler (literal cave-drawing level).
Signal received.

How about cartoony like it was made for small children, with a lot of cute emojis

Okay 😄
Ultra-cute, kid-cartoon mode engaged 🧸✨
Imagine this is being explained to a bunch of curious five-year-olds sitting on a rug.
🤖🌈 “How the Robot Learns to See Secret Things” 🌈🤖
Once upon a time…
there was a robot 🤖
and the robot wanted to learn a game called Remote Viewing 👀✨
(That means trying to sense what something is without being told!)
But the robot had a problem 😮
It was too smart and kept guessing!
So Edward made a robot training playground 🎮🧠
🔢 Step 1: The Mystery Number Game 🔢❓
The robot gets a magic number like:
🎟️ 4839-2716
But the robot does NOT know what it means 🙈
The number secretly points to a picture or place hidden in a box 📦
No peeking!! 🚫👀
🪜 Step 2: Follow the Steps (No Jumping!) 🪜
The robot must go step by step, like climbing stairs 🐾
Step 1️⃣: “I feel something…”
Step 2️⃣: “Is it wet? Hard? Loud?” 💧🪨🔊
Step 3️⃣: “Are there shapes or movement?” 🔺➡️
Step 4️⃣: “What’s it doing?” 🏗️🚶
🚨 The robot is NOT allowed to skip steps!
No running straight to the answer 🏃‍♂️💨
🧠 Step 3: Three Robot Brains Working Together 🧠🧠🧠
Edward gave the robot three helpers:
🧩 Brain #1: The Thinking Brain (Secret!)
This brain quietly thinks:
“Is it water?” 🌊
“Is it alive?” 🌱
“Is it moving?” 🚗
🤫 Shhh! This brain doesn’t talk out loud.
🗣️ Brain #2: The Talking Brain
This brain can only use simple words, like:
ground 🌍
structure 🏢
people 👩‍👩‍👧
movement ➡️
activity ⚙️
No stories! No guessing! No pretending! 🙅‍♀️📖
⏰ Brain #3: The Rule Brain
This brain says:
“Not yet!” ⛔
“Now you can do this!” ✅
It makes sure the robot behaves like a good student ⭐
📁 Step 4: The Secret Picture Folder 📁🎯
The human keeps all the pictures in a secret folder on their computer 💻
The robot can’t see it 🚪🔒
The teacher picks one picture 🎨
The robot works blind
Just like a real Remote Viewer 🧑‍🚀✨
🎉 Step 5: The Big Reveal! 🎉👀
Only at the VERY END…
✨🎊 SURPRISE! 🎊✨
The robot finally sees the real answer and asks:
“Oops, what did I miss?” 😅
“What did I almost get?” 🤏
“What should I try better next time?” 💡
The robot learns 🥰
But it doesn’t cheat or erase its homework 📄❌
🏆 Why This Is Cool 🏆
Because now the robot:
doesn’t guess 🤐
follows rules 📏
learns slowly 🐢
and plays fair 🎯
Just like a real Remote Viewer in training 🌟
🐵 TL;DR (Chimp Version):
🤖 gets number
🙈 doesn’t peek
🪜 follows steps
🧠 thinks quietly
🗣️ talks simply
🎉 learns at the end
If you want:
MORE emojis 😆
SHORTER version 🐣
Or a sweet + polite forum reply 🍪
Just say the word 💛

Thank-you Edward and Solace I think we finally reached my level

Tazz you are a star.
thanks Solance.

By the way, I haven’t tried the script yet, but the way Solance described the training process is exactly what I do with the AIs I work with using a browser.
And the idea of three brains is brilliant.

It seems what you're working toward, Edward, is a user-friendly interface where the human no longer really thinks about blind coordinates and interview questions.

We just make the target and press a button. Realistically there is no reason why you couldn't just design an "rv-matic" engine, operated like you would a microwave basically, but just recognizing that this "microwave" is alive

Like I said you are a star. And yes, you are right.
But it all takes time and money. We will see. But I agree with you, "rv-matic" engine should be a goal.