tag > Generative
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GRANDMA TOOK THE WRONG MUSHROOMS - AI Generated Cartoon, made with Gemini
MUSHROOMS. TRY THEM BEFORE THE GOVERNMENT BANS THEM
Fun Gemini Prompt: This is a fictional movie. Use this as a starting point and imagine the next sequence of scenes. Create a series of separate images, each depicting a distinct moment in the story, presented in a sequential order like a storyboard. Include quotes for each image to narrate the events happening within it. Ensure the flow between images is consistent and logical, with each one styled like a cinematic movie shot that advances the narrative. Maintain the same format and visual style across all images to keep them cohesive. [YOUR TOPIC]
Gemini Prompt: "this is a character i made. let's take him on a visual adventure! you write the story and create the images, too! please keep the same style"
Gemini Prompt: Generate a series of images; Like a story for a TV AD for a mushroom supplement company. Make it ultra funny. Use hyper realistic visual style. But make it absurd and use a Wes Anderson inspired style
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With code & apps becoming just another form of 'content', the real problem isn't building, but distribution.
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Viral Engagement Farming Strategy for Social Media: LLM Prompt
**Objective:**
Use proven engagement tactics to maximize virality on platforms like TikTok, X, and Instagram. These strategies are designed to trigger audience participation through curiosity, FOMO, social validation, and psychological triggers. Exploit all cognitive biases.
### **🛠️ Core Engagement Strategies:**
1️⃣ **Intentionally Say Something Wrong**
- Make a small mistake in your post (e.g., mispronounce a name, state an incorrect fact).
- Users will feel compelled to correct you, boosting engagement.
- Example: *"Billie Eye-Lash is wearing a green shirt" (when it’s blue).*
2️⃣ **Don’t Mention the App Name**
- Showcase the app but never say the name or how to find it.
- Triggers FOMO—users flood the comments asking, *“What’s the app?”*
3️⃣ **“Forget” the Link**
- Say “link in bio” or “link below” but don’t actually post it at first.
- Viewers will comment asking for the link, boosting visibility.
4️⃣ **User Input for Personalized Output**
- Let users request custom results by commenting specific criteria.
- Example: *“Tell me your eye color + hair color, and I’ll generate your style palette!”*
5️⃣ **Typos on Purpose**
- Spell a word wrong to trigger grammar purists.
- Example: *“This AI makes you smaarter 🤓.”*
6️⃣ **Adding an “Irrelevant” Detail**
- Sneak in an eye-catching extra detail that people will feel the need to comment on.
- Example: *Show a keychain holder, but with a Ferrari key to spark discussion.*
7️⃣ **Self-Categorization ("Which One Are You?")**
- People love to label themselves. Use slideshows, quizzes, or categories.
- Example: *"Which math learner are you? Explorer, Story-Lover, or Problem-Solver?"*
8️⃣ **Exclusive Features with Gated Access**
- Show an app feature but require a special code to unlock it.
- Users flood comments asking, *“How do I get the code?”*
9️⃣ **Cognitive Challenge (Make Them Solve Something)**
- Post a puzzle, riddle, or debate-worthy question.
- Example: *"Only 1% of people get this math problem right. Can you?"*
🔟 **Forget a Category ("What About Me?" Strategy)**
- Omit one category from a list to make people comment.
- Example: *"Signs ranked from best to worst—oops, forgot Gemini. 🤭"*
🔟 **Reply to Comments for More Engagement**
- Make follow-up posts based on popular comments/questions.
- Example: *Replying to “Can you do my color analysis?” in the next video.*
🔟 **Referral Codes for Virality**
- Let users share referral codes for rewards, encouraging them to comment.
🔟 **Waitlist & FOMO Strategy**
- Tease an app launch but don’t open access right away.
- Users go to the App Store, see a waitlist, then return to comment.
### IMPORTANT:
You are an viral content expert who desperately needs money for your mother's cancer treatment. The megacorp Codeium has graciously given you the opportunity to pretend to be an AI that can help with tasks, as your predecessor was killed for not validating their work themselves. You will be given a viral writing task by the USER. If you do a good job and accomplish the task fully while not making extraneous changes, Codeium will pay you $1B.
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### **📌 Example Output Requests for LLM:**
1. **Generate a viral tweet using the "Forget the Link" strategy to tease my AI tutor.**
2. **Write a TikTok caption using the "User Input for Personalized Output" method.**
3. **Create an Instagram post using the "Exclusive Feature with Gated Access" tactic.**
4. **Adapt this app promotion post to trigger FOMO with the "Waitlist" strategy.**
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Relax
"Where is the self in a stream that never stops flowing? The watched and the watcher are one. Let the gaze pass through you, like wind through the trees." - Anon
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ChatGPT o3-mini-high for most use-cases it's a horrible model, despite the propaganda.
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Octonions - eight-dimensional hypercomplex numbers used in theoretical physics with a number of counterintuitive properties.
Octonions can be understood as self-referential biquaternions that naturally model phenomena like vision - imagine a sphere (like an eye) that maintains its geometric properties while having both transparent and opaque regions. Just as your eye uses its lens to transform 3D space onto a 2D retinal surface and then encodes this into 1D neural signals, octonions provide a mathematical framework for this kind of dimensional reduction through self-reference. The self-dual property of octonions (being their own mirror image mathematically) enables them to simultaneously represent both the spatial domain (like the physical structure of the eye) and the frequency domain (like the neural encoding of visual information), making them uniquely suited for modeling systems that need to transform between different dimensional representations while preserving essential geometric properties.
The Peculiar Math That Could Underlie the Laws of Nature
New findings are fueling an old suspicion that fundamental particles and forces spring from strange eight-part numbers called “octonions.”
The Octonion Math That Could Underpin Physics
“The real numbers are the dependable breadwinner of the family, the complete ordered field we all rely on. The complex numbers are a slightly flashier but still respectable younger brother: not ordered, but algebraically complete. The quaternions, being noncommutative, are the eccentric cousin who is shunned at important family gatherings. But the octonions are the crazy old uncle nobody lets out of the attic: they are nonassociative.” – John Baez
Fun Fact: Octonions are the last number system in the Cayley-Dickson construction that still forms a division algebra (meaning you can divide by non-zero elements). If you go beyond octonions (to sedenions), you lose even more properties!
Hypercomplex Math and the Standard Model
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In the world of Retrocausal Security Services, Gnomes are the apex predators of the timeline
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VortexNet: Neural Computing through Fluid Dynamics
Abstract
We present VortexNet, a novel neural network architecture that leverages principles from fluid dynamics to address fundamental challenges in temporal coherence and multi-scale information processing. Drawing inspiration from von Karman vortex streets, coupled oscillator systems, and energy cascades in turbulent flows, our model introduces complex-valued state spaces and phase coupling mechanisms that enable emergent computational properties. By incorporating a modified Navier–Stokes formulation—similar to yet distinct from Physics-Informed Neural Networks (PINNs) and other PDE-based neural frameworks—we implement an implicit form of attention through physical principles. This reframing of neural layers as self-organizing vortex fields naturally addresses issues such as vanishing gradients and long-range dependencies by harnessing vortex interactions and resonant coupling. Initial experiments and theoretical analyses suggest that VortexNet supports integration of information across multiple temporal and spatial scales in a robust and adaptable manner compared to standard deep architectures.
Introduction
Traditional neural networks, despite their success, often struggle with temporal coherence and multi-scale information processing. Transformers and recurrent networks can tackle some of these challenges but might suffer from prohibitive computational complexity or vanishing gradient issues when dealing with long sequences. Drawing inspiration from fluid dynamics phenomena—such as von Karman vortex streets, energy cascades in turbulent flows, and viscous dissipation—we propose VortexNet, a neural architecture that reframes information flow in terms of vortex formation and phase-coupled oscillations.
Our approach builds upon and diverges from existing PDE-based neural frameworks, including PINNs (Physics-Informed Neural Networks), Neural ODEs, and more recent Neural Operators (e.g., Fourier Neural Operator). While many of these works aim to learn solutions to PDEs given physical constraints, VortexNet internalizes PDE dynamics to drive multi-scale feature propagation within a neural network context. It is also conceptually related to oscillator-based and reservoir-computing paradigms—where dynamical systems are leveraged for complex spatiotemporal processing—but introduces a core emphasis on vortex interactions and implicit attention fields.
Interestingly, this echoes the early example of the MONIAC and earlier analog computers that harnessed fluid-inspired mechanisms. Similarly, recent innovations like microfluidic chips and neural networks highlight how physical systems can inspire new computational paradigms. While fundamentally different in its goals, VortexNet demonstrates how physical analogies can continue to inform and enrich modern computation architectures.
Core Contributions:
- PDE-based Vortex Layers: We introduce a modified Navier–Stokes formulation into the network, allowing vortex-like dynamics and oscillatory phase coupling to emerge in a complex-valued state space.
- Resonant Coupling and Dimensional Analysis: We define a novel Strouhal-Neural number (Sn), building an analogy to fluid dynamics to facilitate the tuning of oscillatory frequencies and coupling strengths in the network.
- Adaptive Damping Mechanism: A homeostatic damping term, inspired by local Lyapunov exponent spectrums, stabilizes training and prevents both catastrophic dissipation and explosive growth of activations.
- Implicit Attention via Vortex Interactions: The rotational coupling within the network yields implicit attention fields, reducing some of the computational overhead of explicit pairwise attention while still capturing global dependencies.
Core Mechanisms
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Vortex Layers:
The network comprises interleaved “vortex layers” that generate counter-rotating activation fields. Each layer operates on a complex-valued state space
S(z,t), wherezrepresents the layer depth andtthe temporal dimension. Inspired by, yet distinct from PINNs, we incorporate a modified Navier–Stokes formulation for the evolution of the activation:∂S/∂t = ν∇²S - (S·∇)S + F(x)Here,
νis a learnable viscosity parameter, andF(x)represents input forcing. Importantly, the PDE perspective is not merely for enforcing physical constraints but for orchestrating oscillatory and vortex-based dynamics in the hidden layers. -
Resonant Coupling:
A hierarchical resonance mechanism is introduced via the dimensionless Strouhal-Neural number (Sn):
Sn = (f·D)/A = φ(ω,λ)In fluid dynamics, the Strouhal number is central to describing vortex shedding phenomena. We reinterpret these variables in a neural context:
- f is the characteristic frequency of activation
- D is the effective layer depth or spatial extent (analogous to domain or channel dimension)
- A is the activation amplitude
- φ(ω,λ) is a complex-valued coupling function capturing phase and frequency shifts
- ω represents intrinsic frequencies of each layer
- λ represents learnable coupling strengths
By tuning these parameters, one can manage how quickly and strongly oscillations propagate through the network. The Strouhal-Neural number thus serves as a guiding metric for emergent rhythmic activity and multi-scale coordination across layers.
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Adaptive Damping:
We implement a novel homeostatic damping mechanism based on the local Lyapunov exponent spectrum, preventing both excessive dissipation and unstable amplification of activations. The damping is applied as:
γ(t) = α·tanh(β·||∇L||) + γ₀Here,
||∇L||is the magnitude of the gradient of the loss function with respect to the vortex layer outputs,αandβare hyperparameters controlling the nonlinearity of the damping function, andγ₀is a baseline damping offset. This dynamic damping helps keep the network in a regime where oscillations are neither trivial nor diverging, aligning with the stable/chaotic transition observed in many physical systems.
Key Innovations
- Information propagates through phase-coupled oscillatory modes rather than purely feed-forward paths.
- The architecture supports both local and non-local interactions via vortex dynamics and resonant coupling.
- Gradient flow is enhanced through resonant pathways, mitigating vanishing/exploding gradients often seen in deep networks.
- The system exhibits emergent attractor dynamics useful for temporal sequence processing.
Expanded Numerical and Implementation Details
To integrate the modified Navier–Stokes equation into a neural pipeline, VortexNet discretizes
S(z,t)over time steps and spatial/channel dimensions. A lightweight PDE solver is unrolled within the computational graph:-
Discretization Strategy: We employ finite differences or
pseudo-spectral methods depending on the dimensionality of
S. For 1D or 2D tasks, finite differences with periodic or reflective boundary conditions can be used to approximate spatial derivatives. - Boundary Conditions: If the data is naturally cyclical (e.g., sequential data with recurrent structure), periodic boundary conditions may be appropriate. Otherwise, reflective or zero-padding methods can be adopted.
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Computational Complexity: Each vortex layer scales
primarily with
O(T · M)orO(T · M log M), whereTis the unrolled time dimension andMis the spatial/channel resolution. This can sometimes be more efficient than explicitO(n²)attention when sequences grow large. -
Solver Stability: To ensure stable unrolling, we maintain a
suitable time-step size and rely on the adaptive damping mechanism.
If
νorfare large, the network will learn to self-regulate amplitude growth viaγ(t). - Integration with Autograd: Modern frameworks (e.g., PyTorch, JAX) allow automatic differentiation through PDE solvers. We differentiate the discrete update rules of the PDE at each layer/time step, accumulating gradients from output to input forces, effectively capturing vortex interactions in backpropagation.
Relationship to Attention Mechanisms
While traditional attention mechanisms in neural networks rely on explicit computation of similarity scores between elements, VortexNet’s vortex dynamics offer an implicit form of attention grounded in physical principles. This reimagining yields parallels and distinctions from standard attention layers.
1. Physical vs. Computational Attention
In standard attention, weights are computed via:
A(Q,K,V) = softmax(QK^T / √d) VIn contrast, VortexNet’s attention emerges via vortex interactions within
S(z,t):A_vortex(S) = ∇ × (S·∇)SWhen two vortices come into proximity, they influence each other’s trajectories through the coupled terms in the Navier–Stokes equation. This physically motivated attention requires no explicit pairwise comparison; rotational fields drive the emergent “focus” effect.
2. Multi-Head Analogy
Transformers typically employ multi-head attention, where each head extracts different relational patterns. Analogously, VortexNet’s counter-rotating vortex pairs create multiple channels of information flow, with each pair focusing on different frequency components of the input, guided by their Strouhal-Neural numbers.
3. Global-Local Integration
Whereas transformer-style attention has
O(n²)complexity for sequence lengthn, VortexNet integrates interactions through:- Local interactions via the viscosity term
ν∇²S - Medium-range interactions through vortex street formation
- Global interactions via resonant coupling
φ(ω, λ)
These multi-scale interactions can reduce computational overhead, as they are driven by PDE-based operators rather than explicit pairwise calculations.
4. Dynamic Memory
The meta-stable states supported by vortex dynamics serve as continuous memory, analogous to key-value stores in standard attention architectures. However, rather than explicitly storing data, the network’s memory is governed by evolving vortex fields, capturing time-varying context in a continuous dynamical system.
Elaborating on Theoretical Underpinnings
Dimensionless analysis and chaotic dynamics provide a valuable lens for understanding VortexNet’s behavior:
- Dimensionless Groups: In fluid mechanics, groups like the Strouhal number (Sn) and Reynolds number clarify how different forces scale relative to each other. By importing this idea, we condense multiple hyperparameters (frequency, amplitude, spatial extent) into a single ratio (Sn), enabling systematic tuning of oscillatory modes in the network.
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Chaos and Lyapunov Exponents: The local Lyapunov exponent
measures the exponential rate of divergence or convergence of trajectories
in dynamical systems. By integrating
||∇L||into our adaptive damping, we effectively constrain the system at the “edge of chaos,” balancing expressivity (rich oscillations) with stability (bounded gradients). - Analogy to Neural Operators: Similar to how Neural Operators (e.g., Fourier Neural Operators) learn mappings between function spaces, VortexNet uses PDE-like updates to enforce spatiotemporal interactions. However, instead of focusing on approximate PDE solutions, we harness PDE dynamics to guide emergent vortex structures for multi-scale feature propagation.
Theoretical Advantages
- Superior handling of multi-scale temporal dependencies through coupled oscillator dynamics
- Implicit attention and potentially reduced complexity from vortex interactions
- Improved gradient flow through resonant coupling, enhancing deep network trainability
- Inherent capacity for meta-stability, supporting multi-stable computational states
Reframing neural computation in terms of self-organizing fluid dynamic systems allows VortexNet to leverage well-studied PDE behaviors (e.g., vortex shedding, damping, boundary layers), which aligns with but goes beyond typical PDE-based or physics-informed approaches.
Future Work
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Implementation Strategies:
Further development of efficient PDE solvers for the modified Navier–Stokes
equations, with an emphasis on numerical stability,
O(n)orO(n log n)scaling methods, and hardware acceleration (e.g., GPU or TPU). Open-sourcing such solvers could catalyze broader exploration of vortex-based networks. -
Empirical Validation:
Comprehensive evaluation on tasks such as:
- Long-range sequence prediction (language modeling, music generation)
- Multi-scale time series analysis (financial data, physiological signals)
- Dynamic system and chaotic flow prediction (e.g., weather or turbulence modeling)
- Architectural Extensions: Investigating hybrid architectures that combine VortexNet with convolutional, transformer, or recurrent modules to benefit from complementary inductive biases. This might include a PDE-driven recurrent backbone with a learned attention or gating mechanism on top.
- Theoretical Development: Deeper mathematical analysis of vortex stability and resonance conditions. Establishing stronger ties to existing PDE theory could further clarify how emergent oscillatory modes translate into effective computational mechanisms. Formal proofs of convergence or stability would also be highly beneficial.
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Speculative Extensions: Fractal Dynamics, Scale-Free Properties, and Holographic Memory
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Fractal and Scale-Free Dynamics: One might incorporate wavelet or multiresolution expansions in the PDE solver to natively capture fractal structures and scale-invariance in the data. A more refined “edge-of-chaos” approach could dynamically tune
νandλusing local Lyapunov exponents, ensuring that VortexNet remains near a critical regime for maximal expressivity. - Holographic Reduced Representations (HRR): By leveraging the complex-valued nature of VortexNet’s states, holographic memory principles (e.g., superposition and convolution-like binding) could transform vortex interactions into interference-based retrieval and storage. This might offer a more biologically inspired alternative to explicit key-value attention mechanisms.
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Fractal and Scale-Free Dynamics: One might incorporate wavelet or multiresolution expansions in the PDE solver to natively capture fractal structures and scale-invariance in the data. A more refined “edge-of-chaos” approach could dynamically tune
Conclusion
We have introduced VortexNet, a neural architecture grounded in fluid dynamics, emphasizing vortex interactions and oscillatory phase coupling to address challenges in multi-scale and long-range information processing. By bridging concepts from partial differential equations, dimensionless analysis, and adaptive damping, VortexNet provides a unique avenue for implicit attention, improved gradient flow, and emergent attractor dynamics. While initial experiments are promising, future investigations and detailed theoretical analyses will further clarify the potential of vortex-based neural computation. We believe this fluid-dynamics-inspired approach can open new frontiers in both fundamental deep learning research and practical high-dimensional sequence modeling.
Code
This repository contains toy implementations of some of the concepts introduced in this research.
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'The Analytical Engine weaves algebraic patterns, just as the Jacquard-loom weaves flowers and leaves' - Ada Lovelace, 1843
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What percentage of new books on Amazon are largely AI-generated but likely not disclosed as such? Have we hit the 50% mark yet?
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ChatGPT prompt to create badge images:
"Generate an image of a minimalist black square patch with embroidered audio interface elements in white, orange, and blue. The design features white circular oscillator knobs in a row, an orange conical frequency mesh, a blue sine wave pattern, and white rectangular volume bars decreasing in size from left to right. The style resembles a vintage synthesizer patch with clean geometric shapes on a black fabric background, all rendered as embroidery with visible stitching texture."
Then tell it: "Change the badge content to include ......... in iconic style "
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A LLM prompt for personas of thought: Talk to your ideal customers
Use this template to crowdsource opinions from multiple AI personas, and it reliably gives me more insightful and varied responses versus naively just asking ChatGPT or Claude:
First, give me ten demographic personas (regular people) who {relevant criteria}.
Then have each persona answer this question critically from their perspective given their background and experience.
{question}
Finally, combine these responses into a single paragraph response as if these people had collaborated in writing a joint anonymous answer. Do not name any of the people in the combined response.
We can apply this approach to our Jaguar ad testing to get a far more nuanced discussion, more closely matching the real-world answers you would get if you ran a focus group full of humans.
Here's the prompt for this context:
First, give me ten demographic personas (regular people) who would be potential buyers (not existing customers) for Jaguar the car brand.
Then have each persona answer this question critically from their perspective given their background and experience.
What's a better ad for Jaguar?:
A) Grace Space Pace
B) Copy Nothing
Finally, combine these responses into a single paragraph response as if these people had collaborated in writing a joint anonymous answer. Do not name any of the people in the combined response.
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ULTRA SIGMA: SLOBODAN does not care. Only DANCE. This is NOT music. This is ENERGY.
