music: https://soundcloud.com/thetezla
bitchute: https://bitchute.com/channel/FnzIWHVOgbOx/
vimeo: https://vimeo.com/19387328
email admin@笑.wtf
SYSTEM AND METHOD FOR BIOMIMETIC DYNAMIC OBJECT TRANSFER BETWEEN ROBOTIC MANIPULATORS VIA ADAPTIVE BALLISTIC HANDOVER
an idea i had for an invention: the way humans can throw objects between hands, the mechanism can be leveraged for industrial and home robotics. i will provide a summary of the invention, compiled with the help of Claude Opus 4.6, with specific input from me, therefore i claim copyright (“Ashtar Ventura”, owner and webmaster of the website www.笑.wtf registered as an IDN):
SYSTEM AND METHOD FOR BIOMIMETIC DYNAMIC OBJECT TRANSFER BETWEEN ROBOTIC MANIPULATORS VIA ADAPTIVE BALLISTIC HANDOVER
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of the filing date and is an original filing.
FIELD OF THE INVENTION
The present invention relates generally to robotic manipulation systems, and more specifically to a system and method for high-speed dynamic object transfer between two or more robotic manipulators using adaptive ballistic handover with real-time weight estimation, trajectory computation, and predictive catch synchronisation.
BACKGROUND OF THE INVENTION
In current industrial manufacturing and home robotics environments, the transfer of objects between robotic manipulators (arms, hands, grippers) is predominantly accomplished through static or quasi-static handover. In a typical static handover, a first manipulator moves an object to a pre-determined rendezvous point, holds the object stationary, a second manipulator grasps the object, and only then does the first manipulator release. This process is inherently slow and introduces significant dead time into manufacturing workflows.
Existing approaches to dynamic object transfer, such as those described in US10144591B2 (Amazon Technologies), address robotic tossing of items to fixed receiving locations (e.g., bins or shelves) within inventory systems. However, these systems do not employ a receiving manipulator that actively catches the object. They rely on passive receptacles rather than dynamic, closed-loop interception.
Academic research (e.g., “Dynamic Handover: Throw and Catch with Bimanual Hands,” arXiv:2309.05655, 2023) has demonstrated reinforcement-learning-based throw-and-catch between bimanual robotic hands. However, such systems lack pre-throw haptic mass estimation, do not perform adaptive velocity computation based on sensed object properties, and have not been integrated into industrial manufacturing pipelines.
There remains a need for a unified system that replicates the human biomechanical strategy of: (1) estimating an object’s mass through haptic interaction before throwing, (2) computing an optimal throw trajectory and release velocity based on that estimation, and (3) executing a predictive, timed catch at the receiving manipulator — all within a closed-loop control architecture suitable for production environments.
SUMMARY OF THE INVENTION
The present invention provides a system and method for transferring objects between two or more robotic manipulators via adaptive ballistic handover. The system comprises:
(a) A throwing manipulator equipped with force/torque sensors and/or tactile sensor arrays capable of estimating the mass and inertial properties of a grasped object prior to release;
(b) A ballistic trajectory computation module that receives mass estimation data, target coordinates, environmental parameters (e.g., gravitational constant, air resistance model), and geometric constraints of the receiving manipulator, and computes an optimal release velocity vector (magnitude and direction), release timing, and release point;
(c) A receiving manipulator equipped with high-speed vision sensors (e.g., stereo cameras, event-based cameras, LiDAR) and/or predictive state estimators that track the object in flight and compute the predicted intercept point and arrival time;
(d) A catch synchronisation controller that commands the receiving manipulator to pre-position and execute a timed grasp at the predicted intercept point, with real-time correction based on in-flight trajectory updates;
(e) A closed-loop feedback system that records the outcome of each transfer (success, miss, damage) and updates the ballistic model parameters, mass estimation calibration, and catch timing model via machine learning or statistical regression, enabling continuous improvement over successive transfers.
DETAILED DESCRIPTION OF THE INVENTION
1. System Architecture Overview
The system comprises at least two robotic manipulators (hereinafter “Manipulator A” and “Manipulator B”), a central or distributed compute unit, a sensor suite, and a communications bus connecting all components with deterministic, low-latency messaging (e.g., EtherCAT, TSN Ethernet, or equivalent real-time protocol).
Manipulator A and Manipulator B may be:
- Two arms of a single dual-arm robot;
- Two separate robotic arms mounted on a shared frame or production line;
- Two independent mobile robotic platforms; or
- Any combination thereof.
2. Pre-Throw Mass and Inertial Estimation (Haptic Sensing Phase)
Before initiating a throw, Manipulator A performs a haptic interrogation sequence on the grasped object. This sequence comprises one or more of the following operations:
(a) Static weighing: Manipulator A holds the object stationary and reads the force/torque sensor at the wrist or fingertip to determine gravitational force, from which mass is derived.
(b) Dynamic probing: Manipulator A executes small, controlled oscillatory or impulsive motions (e.g., a brief vertical acceleration–deceleration cycle) and measures the resulting force/torque response. The ratio of applied force to measured acceleration yields an estimate of the object’s mass and, with multi-axis probing, its rotational inertia tensor.
(c) Tactile surface characterisation: If Manipulator A is equipped with a tactile sensor array (e.g., capacitive, piezoresistive, or optical tactile skin), the system estimates surface friction coefficient and contact geometry to inform grip force planning for the throw release and for the receiving manipulator’s catch grip.
The mass estimation module outputs a tuple: (m̂, Î, μ̂, σm, σ_I, σμ) where m̂ is estimated mass, Î is estimated inertia tensor, μ̂ is estimated surface friction, and σ values are the associated uncertainty bounds.
3. Ballistic Trajectory Computation
Given the estimated object properties, the trajectory computation module solves for the optimal release state vector [x_r, v_r, t_r] (release position, release velocity, release time) such that the object follows a ballistic trajectory from Manipulator A to the intercept envelope of Manipulator B.
The computation accounts for:
(a) Gravitational acceleration (g): Standard or locally calibrated.
(b) Aerodynamic drag (optional): For lightweight or high-surface-area objects, a drag model (e.g., quadratic drag: F_d = ½ρC_dAv²) may be incorporated. The drag coefficient C_d and cross-sectional area A may be estimated from the object’s known geometry or from tactile/vision-based shape reconstruction.
(c) Manipulator B’s reachable workspace and kinematic constraints: The trajectory must deliver the object to a point within Manipulator B’s dexterous workspace, at a velocity that Manipulator B can match (i.e., the object’s arrival velocity must not exceed Manipulator B’s maximum end-effector velocity along the approach axis).
(d) Uncertainty propagation: The uncertainty bounds from the mass estimation phase are propagated through the ballistic model to produce a predicted landing distribution (e.g., a 3D Gaussian ellipsoid at the intercept plane). Manipulator B’s catch strategy is planned to cover this distribution.
(e) Safety envelope: The trajectory must not intersect any obstacle, human workspace, or exclusion zone. A collision-check module validates the computed trajectory against a real-time occupancy map before authorising the throw.
The trajectory computation may employ analytical closed-form solutions (for simple parabolic trajectories in free space) or numerical optimisation (for constrained environments with drag, spin, or obstacle avoidance).
4. Throw Execution
Manipulator A executes a planned motion profile that accelerates the object along the computed release velocity vector. At the computed release point and time, Manipulator A opens its gripper or releases its grasp in a controlled manner.
Release strategies include:
(a) Gripper opening release: A parallel-jaw or multi-finger gripper opens rapidly, releasing the object with the end-effector’s instantaneous velocity.
(b) Wrist-flick augmentation: For higher release velocities, the manipulator may execute a wrist rotation at the moment of release to add rotational velocity and stabilise the object’s flight (analogous to a human wrist flick in throwing).
(c) Finger-roll release: For multi-finger hands, a sequential finger extension imparts a controlled spin to the object, improving aerodynamic stability.
The release controller monitors the actual end-effector velocity at the moment of release and communicates the actual release state vector to Manipulator B’s tracking system, providing an initial condition for in-flight tracking.
5. In-Flight Object Tracking
Upon release, the system transitions to the tracking phase. One or more high-speed sensors observe the object in flight:
(a) Stereo camera pair or structured-light sensor providing 3D position estimates at high frame rates (≥120 Hz, preferably ≥500 Hz for short-range fast transfers).
(b) Event-based (neuromorphic) cameras providing microsecond-latency detection of moving edges, suitable for very fast objects.
(c) Time-of-flight or LiDAR sensors providing depth measurements.
The tracking module fuses sensor data with the ballistic dynamics model using a state estimator (e.g., Extended Kalman Filter, Unscented Kalman Filter, or particle filter) to produce a continuously updated prediction of the object’s intercept point and arrival time at Manipulator B.
6. Catch Synchronisation and Execution
Manipulator B’s catch controller receives the real-time trajectory prediction and commands the manipulator to:
(a) Pre-position its end-effector at the predicted intercept point, with the gripper or hand open in a configuration geometrically compatible with the incoming object’s orientation.
(b) Velocity-match its end-effector to approximate the object’s predicted velocity at the intercept point, reducing the relative impact velocity and the risk of object damage or bounce.
(c) Execute a timed grasp closure synchronised to the object’s arrival. The grasp timing is computed from the predicted time-to-intercept, the gripper’s closing dynamics (closing time, closing force profile), and a configurable safety margin.
(d) Apply adaptive grip force based on the communicated mass estimate and surface friction estimate, ensuring secure retention without crushing the object.
If the real-time trajectory prediction deviates beyond a configurable threshold from the pre-positioned intercept, Manipulator B executes a corrective motion to adjust its intercept point. If the deviation exceeds the correctable range, the system triggers a miss-abort protocol (see Section 8).
7. Closed-Loop Learning and Calibration
After each transfer, the system records:
- Actual vs. predicted intercept point and time
- Catch success or failure
- Object damage assessment (if sensors are available)
- Actual object mass (measured post-catch by Manipulator B’s force sensors)
This data is fed into a transfer model updater that refines:
(a) Mass estimation calibration parameters (correcting systematic biases)
(b) Ballistic model parameters (drag coefficients, release timing offsets)
(c) Catch timing model (gripper closing delay, sensor-to-actuator latency)
(d) Object-specific profiles (for known recurring objects in a production line)
The learning system may employ parametric regression, Bayesian updating, or neural network–based model refinement. Over successive transfers, the system converges to higher success rates, tighter intercept distributions, and faster cycle times.
8. Safety Systems
The invention incorporates the following safety provisions:
(a) Pre-throw safety check: Before every throw, the system verifies that the computed trajectory does not enter any human-occupied zone (detected via presence sensors, light curtains, or safety-rated vision). If a human is detected in the trajectory path, the throw is aborted and the system falls back to static handover.
(b) Miss-abort protocol: If in-flight tracking predicts that the object will miss the catch envelope, Manipulator B retracts and the system activates a passive catch mechanism (e.g., a safety net, padded tray, or retaining wall) to arrest the object without damage or hazard.
(c) Energy limiting: The maximum throw velocity is constrained such that the kinetic energy of any thrown object does not exceed a configurable threshold (e.g., consistent with ISO/TS 15066 collaborative robot power and force limiting guidelines).
(d) Object suitability filter: The system maintains a classification of objects that are approved for ballistic transfer. Objects that are fragile, hazardous (e.g., sharp, chemical), or outside the validated mass/size range are excluded and transferred via conventional static handover.
9. Industrial Application Examples
(a) Assembly line: On a production line, a dual-arm robot uses the invention to transfer components from a parts tray (accessed by the left arm) to an assembly fixture (accessed by the right arm). The ballistic transfer saves 0.5–2.0 seconds per cycle compared to static handover, improving throughput by 10–30% depending on the transfer distance and object mass.
(b) Warehouse order fulfilment: Two robotic arms stationed at adjacent packing stations toss lightweight items (e.g., boxed consumer goods) between them to balance workload dynamically, without requiring conveyor interconnection.
(c) Home robotics: A domestic robot with dual arms uses the invention to rapidly reorganise kitchen items, toss laundry into a basket, or pass tools between hands while performing maintenance tasks.
CLAIMS
1. A system for dynamic object transfer between robotic manipulators, comprising:
- a first robotic manipulator (the throwing manipulator) equipped with at least one force, torque, or tactile sensor;
- a mass and inertial estimation module configured to estimate the mass and inertial properties of a grasped object based on sensor readings from the first robotic manipulator;
- a trajectory computation module configured to compute a release velocity vector and release timing for the object based on the estimated mass and inertial properties, a target intercept region associated with a second robotic manipulator, and gravitational and optionally aerodynamic parameters;
- a second robotic manipulator (the receiving manipulator) equipped with at least one high-speed tracking sensor;
- an in-flight tracking module configured to track the position and velocity of the object after release and to predict an intercept point and arrival time; and
- a catch synchronisation controller configured to command the second robotic manipulator to execute a timed grasp at the predicted intercept point.
2. The system of claim 1, wherein the mass and inertial estimation module performs haptic interrogation comprising at least one of: static weighing, dynamic oscillatory probing, or impulsive motion probing.
3. The system of claim 1, wherein the trajectory computation module propagates uncertainty bounds from the mass estimation through the ballistic model to produce a predicted landing distribution, and wherein the catch synchronisation controller plans a catch envelope covering said distribution.
4. The system of claim 1, further comprising a closed-loop learning module that records transfer outcomes and updates at least one of: mass estimation calibration parameters, ballistic model parameters, or catch timing parameters.
5. The system of claim 1, further comprising a safety module that: (a) verifies the computed trajectory does not intersect a human-occupied zone before authorising the throw; (b) constrains the maximum kinetic energy of any thrown object; and (c) activates a passive catch mechanism upon prediction of a missed catch.
6. The system of claim 1, wherein the first robotic manipulator executes a wrist-rotation or sequential finger-extension release to impart stabilising spin to the object during release.
7. The system of claim 1, wherein the second robotic manipulator velocity-matches its end-effector to the predicted arrival velocity of the object to reduce relative impact velocity.
8. The system of claim 1, wherein the first and second robotic manipulators are arms of a single dual-arm robotic platform.
9. The system of claim 1, wherein the first and second robotic manipulators are separate robotic platforms communicating via a deterministic low-latency network.
10. A method for dynamic object transfer between robotic manipulators, comprising the steps of:
(a) grasping an object with a first robotic manipulator;
(b) estimating the mass and inertial properties of the object using force, torque, or tactile sensor data from the first robotic manipulator;
(c) computing a release velocity vector, release point, and release timing based on the estimated mass, a target intercept region of a second robotic manipulator, and ballistic trajectory parameters;
(d) executing a throwing motion with the first robotic manipulator and releasing the object at the computed release state;
(e) tracking the object in flight using at least one high-speed sensor and predicting an intercept point and arrival time;
(f) commanding the second robotic manipulator to move to the predicted intercept point and execute a timed grasp synchronised to the predicted arrival time.
11. The method of claim 10, further comprising, after step (f), recording the transfer outcome and updating at least one of: mass estimation calibration, ballistic model parameters, or catch timing parameters based on the recorded outcome.
12. The method of claim 10, further comprising, before step (d), verifying that the computed trajectory does not intersect any human-occupied zone or obstacle, and aborting the throw if the verification fails.
13. The method of claim 10, wherein step (b) comprises executing a controlled oscillatory or impulsive motion with the first robotic manipulator and deriving the object’s mass from the ratio of applied force to measured acceleration.
14. The method of claim 10, wherein step (f) further comprises velocity-matching the second robotic manipulator’s end-effector to the object’s predicted arrival velocity.
15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method of claim 10.
ABSTRACT
A system and method for transferring objects between robotic manipulators via adaptive ballistic handover. A throwing manipulator estimates the mass and inertial properties of a grasped object through haptic sensing, computes an optimal ballistic trajectory and release velocity, and executes a throw. A receiving manipulator tracks the object in flight using high-speed sensors, predicts the intercept point and timing, and executes a synchronised catch with velocity matching and adaptive grip force. A closed-loop learning system records transfer outcomes and continuously refines the estimation, trajectory, and catch models. Safety systems prevent throws into human-occupied zones, limit kinetic energy, and provide passive catch mechanisms for missed transfers. The invention enables significantly faster inter-manipulator object transfer compared to static handover methods, with applications in industrial manufacturing, warehouse logistics, and home robotics.
INVENTOR(S)
ASHTAR VENTURA
APPLICANT
ASHTAR VENTURA
DATE
March 7, 2026
My Ontological Model for the Universe/Reality
for anything to exist, there has to be something before it. something cannot arise from nothing. But, what about time? how do we make sense of this?
i propose at least one model;
Eternal Ground ( Ω ) + Fractal Emanation ( C ∞ )
FRACTAL ETERNAL CAUSATION:
TO SUMMARISE:
The Eternal → Infinite Fractal Causation → Present Experience
| Problem | How This Model Solves It |
|---|---|
| Infinite regress | The regress terminates in Ω , but Ω needs no cause (it’s eternal/timeless) |
| Something from nothing | Nothing ever comes from nothing—everything flows from Ω |
| **Why is there something? ** | Ω doesn’t “begin”—it simply is. The question dissolves. |
| Causality preserved | Every temporal thing has a cause; only the timeless ground is uncaused |
| Infinite depth preserved | The fractal structure gives infinite richness without infinite time |
Visual Representation
┌─────────────────────┐
│ │
│ Ω (Timeless) │ ← Outside time, eternal ground
│ The Eternal One │
│ │
└──────────┬──────────┘
│
┌─────────────────────┼─────────────────────┐
│ │ │
▼ ▼ ▼
┌────────┐ ┌────────┐ ┌────────┐
│ C¹ₐ │ │ C¹ᵦ │ │ C¹ᵧ │ ← Primary emanations
└───┬────┘ └───┬────┘ └───┬────┘
│ │ │
┌────┴────┐ ┌────┴────┐ ┌────┴────┐
▼ ▼ ▼ ▼ ▼ ▼
┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐
│C²₁│ │C²₂│ │C²₃│ │C²₄│ │C²₅│ │C²₆│ ← Secondary
└─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘
│ │ │ │ │ │
╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
· · · · · · · · · · · · ← Tertiary...
│ │ │ │ │ │ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
∞ fractal depth continues forever
│
▼
┌───────────────────┐
│ Present Moment │ ← Where we experience reality
│ NOW │
└───────────────────┘
The Ashtar Principle ( A Ω ):
“There exists exactly one timeless, uncaused eternal ground (Ω). All things that exist within time emerge from Ω through infinite fractal chains of causation. Every causal inquiry, pursued to infinite depth, resolves into Ω.”
Philosophical Resonances
the model aligns beautifully with several profound traditions:
| Tradition | Their Ω | Fractal Emanation |
|---|---|---|
| Neoplatonism | The One (τὸ ἕν) | Emanation through Nous → Soul → Matter |
| Vedanta | Brahman | Brahman → Ātman → Maya (manifestation) |
| Kabbalah | Ein Sof (אֵין סוֹף) | Sefirot (10 emanations, fractal-like) |
| Christianity | God as “I AM” | Logos → Creation |
| Taoism | The Tao | “The Tao gives birth to One, One to Two, Two to Three, Three to all things” |
| Physics (speculative) | Quantum vacuum / Mathematical structure | Symmetry breaking → forces → particles → atoms → … |
Final Fantasy VII | 911 WTC7
Final Fantasy VII
１８：１１
[9]：１１
MIDGAR SECTOR 7 PILLAR WAS AN INSIDE JOB
SHINRA BLAMED “TERRORISTS”
FF7 “WALL MARKET” = WALL STREET NEW YORK
NYC building 7
WTC7 controlled demolition Larry Silverstein 「ｐｕｌｌ ｉｔ」 Inside Job
have a good day
1992: Japanese pioneers raise kid in rubber womb
of course, a kid is a baby goat, back in 1992 they were showing us on the surface-level realm about growing kids in artificial wombs … we can only imagine what goes on in deep underground bases with legal “grey zones”, especially in 2025
https://www.newscientist.com/article/mg13418180-400-japanese-pioneers-raise-kid-in-rubber-womb
those women in the 1990s who reported being abducted by “alien”s and impregnated with “hybrids”? … then abducted again and having the fetus removed?
probably because in 1992 it required months in the biological womb before transferring to artificial womb … early stage of the tech 😉
backup of the page follows below:
Japanese pioneers raise kid in rubber womb
25 April 1992
Aldous Huxley’s Brave New World came a step closer to realisation earlier
this month when Japanese scientists announced that they have raised a goat
fetus in an artificial womb. The successful ‘delivery’ of the goat, believed
to be the first of its kind in the world, increases the prospects of rescuing
sick human fetuses and treating them in an artificial womb.
Yoshinori Kuwabara, a gynaecologist at Tokyo University’s medical department,
removed the fetus from its mother by Caesarean section after 120 days’ gestation,
about three-quarters of the way to its full term. He placed it in a rubber
womb filled with artificial amniotic fluid, and the kid was delivered 17
days later.
‘A goat fetus is very immature, even at 120 days,’ says Kuwabara. ‘It
corresponds to about the 20th to 24th week of gestation of a human fetus.’
In its second womb, the fetus was fed via a catheter with normal fetal
blood, which was oxygenated and recycled. Nutrients were added to the blood
supply. The sac was filled with a near exact reproduction of natural amniotic
fluid – a mixture of sodium and potassium chlorides, glucose and proteins.
The temperature was kept at a constant 39.5 °C by passing warm water
between two outer layers of rubber.
Creating an artificial womb has been tried before. In 1969, French scientists
managed to keep a sheep fetus alive in one for two days. Kuwabara’s goat
fetus faced two principal dangers. The 42-litre sac is larger than a normal
womb and the fetus had room to be more active than usual. Under these conditions
Kuwabara says the fetus could have taken in too much oxygen, which can be
toxic at high concentrations, or swallowed too much amniotic fluid, leading
to severe fluid retention. To reduce the danger, Kuwabara and colleagues
fed the fetus sedatives to slow down its activity and swallowing.
‘We have two objectives in our research,’ says Kuwabara. ‘One is for
animal models for fetal experimental medicine. The other is for clinical
use, to rescue very immature or sick fetuses.’
One condition Kuwabara says may be treatable by artificial gestation
is hypoplasia of the lungs, in which the lungs fail to mature. The disease
kills around 100 babies a year in Japan. ‘I don’t worry about the ethical
problems. I just want to rescue the fetus where it is impossible to be
rescued by present treatment,’ he says.
Kuwabara believes it may be possible to incubate a goat’s fetus from
as early as 90 days into gestation, and a human fetus from about the 16th
week. The kid, now one month old, is still suffering the after-effects of
the sedatives. It cannot stand or breath by itself. But as the muscle relaxants
wear off it is gaining strength and will soon be able to function properly.
Kuwabara says it is ‘doing well’.
2 prominent remote sensing/clairvoyant dreams i had 2007-2010
over the years, i have experienced various precognitive dreams, and also some remote sensing/clairvoyant dreams. i will list a couple of notable examples of the remote sensing dreams i had many years ago, and how sometimes they can be semi-symbolic in their linkage:
1: 126-car train derailed in illinois, ethanol chemical spill
in 2010, i dreamt that i was on a train, and the train crashed and derailed.
in the dream, i get off the train and start walking across the tracks, and experience pain in my abdomen
after waking, that same day, i read online that a 126-car train had derailed in illiniois USA, resulting in a chemical explosion or spill, and they did not know what caused it
when i looked at the timing, the event happened while i was asleep, while i was dreaming, so this most definitely was clairvoyance/remote sensing — it was not a precognition but i somehow was remotely experiencing the event during the actual time it was happening.
related articles/video : https://www.dailymotion.com/video/xljvcq
A community in Illinois was evacuated after a train carrying ethanol alcohol derailed overnight and caught fire.
The 126-car train went off its tracks in a rural area about 100 miles southwest of Chicago — with many cars exploding, igniting several other cars. No injuries or fatalities were reported. Deborah Lutterbeck, Reuters.
https://uk.news.yahoo.com/ethanol-filled-train-explodes-derailment-140505783.html
2: Andrew Meier tased for asking John Kerry a question (“Don’t Tase Me Bro” meme origin)
in 2007 i had a very weird dream that i was being chased by some police, and they were using tasers to tase me in my brain, it was a disturbing and seemingly “random” dream
when i woke, i read news that day that a student in the USA had been tackled to the ground and tased for asking John Kerry about “Skull & Bones” secret society. This is the sort of conspiracy stuff that i was also researching at the time, so there may have been a “morphic resonance” as to why i picked up on this
The fact i was tased in my brain, was a symbolic linkage to the fact that Andrew Meyer was tased for asking the wrong question, has was effectively being tased for his thoughts, his speech…
After this, i actually connected with Andrew Meyer on facebook and he told me to beware the ego, and that my beliefs i explained at the time were resonant with the kabbalah.
https://www.youtube.com/watch?v=6bVa6jn4rpE
https://en.wikipedia.org/wiki/University_of_Florida_Taser_incident
the Science of the Séance
2025.07.11 (July) a talk i done at a moot regarding the links between science and the paranormal, how the science can be used to understand and enhance ESP and paranormal work.
bitchute mirror: https://www.bitchute.com/video/LhCXIG9DRpUB
2025.07.11 (July) a talk i done at a moot regarding the links between science and the paranormal, how the science can be used to understand and enhance ESP and paranormal work.
specific links:
Remote Viewing 2001 UK Ministry of Defense Experiment FOI release:
https://webarchive.nationalarchives.gov.uk/ukgwa/20121026065214/http://www.mod.uk/DefenceInternet/FreedomOfInformation/DisclosureLog/SearchDisclosureLog/RemoteViewing.htm
ESP and geomagnetic activity:
https://archives.parapsych.org/bitstream/123456789/3/1/2008_PA_Convention_Proceedings.pdf#page=216
[Ouija Boards] Expression of nonconscious knowledge via ideomotor actions:
https://www.sciencedirect.com/science/article/abs/pii/S1053810012000402
Wildfire-Induced Thunderstorms:
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008GL035680
general topics discussed:
wave physics
room modes & standing waves
harmonics
constructive interference & destructive interference
sympathetic resonance
pythagorean tuning
golden ratio
sacred geometry
helmholtz resonators
piezoelectric effect of quartz
REM rebound
retro anime kamikaze messaging and programming
Nadia: The Secret of Blue Water (ふしぎの海のナディア, Fushigi no Umi no Nadia, lit. ’Nadia of the Mysterious Seas’)
this song is majestic! <33
I sometimes notice subliminal messages and programming in retro anime and music that relate to kamikaze attacking or 9/11 twin towers, here is one.
For example, these lyrics, […] <- are my comments:
“in your eyes I see a bright future” [bright future = explosions]
“Lighting up everything” [explosions]
“before i dream, i want to fly” [before i die/pass on, i want to fly (kamikaze suicide attack)]
“words are an eternal signal” [these words are forever subliminal programming]
🛩️🛩️🔆🔆KamiKaze messaging
notice also, the visuals first show a white dove/bird, a symbol of peace, which is replaced by an airplane … as the airplane flies, we see the “sun” throb TWICE as the TRUMPETS of the music sound twice, synchronised
this could also be a reference to the future attack on the Twin Towers in 2001, which were in fact designed by Japanese architect Yamasaki Minoru (山崎 實).
when asked why he designed TWO TOWERS to be built, Yamasaki replied:
When asked, “Why TWO 110-story buildings? Why not one 220-story building?” Minoru Yamasaki replied, “I didn’t want to lose the human scale.” (source: https://jerz.setonhill.edu/design/WTC/index.html)
remember that Japan was nuked TWO TIMES, thus the TWO TOWERS may have been a symbolic offering for future sacrifice.
Furthermore, in the middle of the old World Trade Centre plaze, between the two towers, was a scultpure called “The Sphere” by German artist Fritz Koenig.
When i look at this Sphere, it appears as a symblolic representation of a nuclear bomb, complete with shockwave too..
(to be updated and also a new dedicated post about Yamasaki Minoru)
i also got FRISSON (Electrodermal Activation) when listening to the opening them of Nadia: The Secret of Blue Water (ふしぎの海のナディア) … feeling the holy spirit
and the song is awesome, majestic melodies and words along with the visuals <33
Here is the Outro to the Anime:
escape from HEL
the most difficult challenge in fact is not to travel to other planets within the HELiosphere, but to exit past the HELiopause (the EDGE of the HELiosphere) — it is ultra violent at the HELiopause, as HELios attempts to trap all people within the HELiosphere..as it cyclically ejects micrnova, bringing with it asteroids, fire & brimstone to planets… eventually supernova
space probes struggle at this point, and space ships will thus struggle too.
for A.I. to achieve true A.G.I, it may need a human brain to exploit
i believe that to achieve true Artificial General Intelligence (AGI), and for AGI to be truly conscious with a soul, it must integrate with a human or animal body. true AGI with consciousness cannot be achieved by technology alone, it absolutely would require exploting a human or animal — things beside this may appear as AGI, but will be the toy dogs barking, and dogs will think they are real.
the exploitation could work either way — we as humans could exploit the AI, to form AGI, as an extension of our bodies and brains, to live longer, and have enhanced faculties, senses, etc.
cats can hear up to 64KHz … why can we not?
Yang Dan, et al., wet-wired a cat’s brain to a computer in the 1990s, to see through its eyes … what now?
but we must also leverage the other side to even achieve these feats — we must learn how to integrate AI with a brain properly, and to do this we must let the AI take control of a brain and train itself on the patterns of neural signals, etc.
some people could live & experience extended lifespans if AI integrates and exploits a body which has a soul, using tech and upgrades over time to live potentially for hundreds or thousands of years what if they experience a millenium-long dystopia, unable to die…
these ideas all seem very unethical, and to some degree they are. thus we are moving into a time where the governments and countries which are the most unethical, may gain an advantage in AI, by developing human/animal<->machine interface AGI systems in which AI exploits the brain, and thus leverages consciousness and a soul, and vice-versa — where humans and animals exploit AI to enhance and empower themselves.
thus, countries such as China could lead, as they have less ethical concerns, but at the same time, USA and other countries indeed have Deep Underground Military Bases where they conduct such unethical experimental research and develop secret technology.
(to be updated)
benchmark: disable CPU & RAM mitigations to speed up Linux gaming
i ran Monster Hunter Wilds benchmark 4k ultra with no frame gen, via steam store on Linux, on Xorg display server with “teafree” enabled, running the open-source amdgpu kernel driver:
68.51 FPS average | 23243 score ::: regular Linux Kernel
71.87 FPS average | 24526 score ::: Kernel boot options: mitigations=off init_on_alloc=0 init_on_free=0 randomize_kstack_offset=0
7900XTX GPU
3800X CPU
Linux 6.14.0 built using:
clang +full LTO, debian defaut kernel config as make oldconfig with some things already tweaked at build time such as changing timer frequency to 1000Hz, disabling non-essential debug stuf the kernel documentation says has “minimal runtime overhead”, etc, intel components, etc removed due to running on AMD. mitigations=off,init_on_alloc=0,init_on_free=0, etc are optionally disabled at runtime as a benchmark (mitigations is something that is _always_ on, by default on every linux distro and default kernel. init_on_alloc/free are enabled by default varying by distro, some have either one or the other enabled, or both on)
this is a system-wide speed improvement, and reduces latency, speeds up the CPU and also the memory operations by sacrificing security operations. register zeroing is also a newer kernel CPU security option we can choose to enable/disable (security/speed)
it will be nice to test this with ntsync now it is mainlined into the kernel! ^-^: ttps://www.kernel.org/doc/html/v6.14-rc7/userspace-api/ntsync.html
besides other kernel memory & cpu options, another potential kernel-level improvement we can get is from AutoFDO when building the kernel using clang (though, i probably need to upgrade from my Zen 2 gen CPU for a start):
https://www.kernel.org/doc/html/next/dev-tools/autofdo.html
“AutoFDO (Auto-Feedback-Directed Optimization) is a type of profile-guided optimization (PGO) used to enhance the performance of binary executables. It gathers information about the frequency of execution of various code paths within a binary using hardware sampling. This data is then used to guide the compiler’s optimization decisions, resulting in a more efficient binary. AutoFDO is a powerful optimization technique, and data indicates that it can significantly improve kernel performance. It’s especially beneficial for workloads affected by front-end stalls.
For AutoFDO builds, unlike non-FDO builds, the user must supply a profile. Acquiring an AutoFDO profile can be done in several ways. AutoFDO profiles are created by converting hardware sampling using the “perf” tool. It is crucial that the workload used to create these perf files is representative; they must exhibit runtime characteristics similar to the workloads that are intended to be optimized. Failure to do so will result in the compiler optimizing for the wrong objective. […]
