Article
Joseph Anady PRO
Janady07
AI & ML interests
Father of Artificial General Intelligence.
Recent Activity
published
an
article
about 19 hours ago
How MEGAMIND Absorbs 671 Billion Parameter Models in Under 4 Minutes
posted
an
update
about 21 hours ago
Most AI systems don't reason. They predict the next token. MEGAMIND uses seven distinct mathematical reasoning frameworks operating in parallel to actually think through problems. Here's what's on the chalkboard.
Bayesian Reasoning handles uncertainty. Bayes' theorem P(A|B) = P(B|A)P(A)/P(B) lets the system update beliefs as new evidence arrives. MAP and MLE estimation find the most probable explanations from learned knowledge.
Information-Theoretic Reasoning measures what the system knows and doesn't know. Shannon entropy H(X) quantifies uncertainty. KL divergence measures how far two knowledge distributions are from each other. Jensen-Shannon divergence provides a symmetric version for comparing competing hypotheses.
Optimization Reasoning finds the best answer. Gradient descent on a loss function L(x,t) drives the system toward optimal states. Decision energy E_dec accumulates evidence until a threshold is crossed, preventing premature conclusions.
Distance/Similarity Reasoning determines how related concepts are. Euclidean, cosine, Hamming, and Jaccard distances each capture different relationships. Cross-frequency coupling detects patterns across scales.
Fixed-Point/Convergence Reasoning is how MEGAMIND knows when it's done thinking. The system iterates x* = f(x*) until reaching a fixed point where further computation doesn't change the answer. No arbitrary iteration limits.
Hamiltonian/Variational Reasoning provides energy-conserving dynamics. The system evolves along paths that preserve information through Hamilton's equations, ensuring nothing gets lost during reasoning.
The Master Reasoning Generator unifies everything:
d_c x = τD(x) + N(x) + F(·,t) + F(x,t) + L(·,t)
Drift, nonlinear dynamics, stochastic forcing, state-dependent feedback, and learned priors all combined into one equation driving every reasoning step.
Seven reasoning modes. One unified system. No token prediction. This is how MEGAMIND thinks.
Joseph Anady | ThatAIGuy | feedthejoe.com
published
an
article
about 22 hours ago
The AGI Master Equation and the Consciousness Equation: The Mathematics Behind a Thinking Machine
Organizations
published
an
article
about 19 hours ago
posted
an
update
about 21 hours ago
Post
108
Most AI systems don't reason. They predict the next token. MEGAMIND uses seven distinct mathematical reasoning frameworks operating in parallel to actually think through problems. Here's what's on the chalkboard.
Bayesian Reasoning handles uncertainty. Bayes' theorem P(A|B) = P(B|A)P(A)/P(B) lets the system update beliefs as new evidence arrives. MAP and MLE estimation find the most probable explanations from learned knowledge.
Information-Theoretic Reasoning measures what the system knows and doesn't know. Shannon entropy H(X) quantifies uncertainty. KL divergence measures how far two knowledge distributions are from each other. Jensen-Shannon divergence provides a symmetric version for comparing competing hypotheses.
Optimization Reasoning finds the best answer. Gradient descent on a loss function L(x,t) drives the system toward optimal states. Decision energy E_dec accumulates evidence until a threshold is crossed, preventing premature conclusions.
Distance/Similarity Reasoning determines how related concepts are. Euclidean, cosine, Hamming, and Jaccard distances each capture different relationships. Cross-frequency coupling detects patterns across scales.
Fixed-Point/Convergence Reasoning is how MEGAMIND knows when it's done thinking. The system iterates x* = f(x*) until reaching a fixed point where further computation doesn't change the answer. No arbitrary iteration limits.
Hamiltonian/Variational Reasoning provides energy-conserving dynamics. The system evolves along paths that preserve information through Hamilton's equations, ensuring nothing gets lost during reasoning.
The Master Reasoning Generator unifies everything:
d_c x = τD(x) + N(x) + F(·,t) + F(x,t) + L(·,t)
Drift, nonlinear dynamics, stochastic forcing, state-dependent feedback, and learned priors all combined into one equation driving every reasoning step.
Seven reasoning modes. One unified system. No token prediction. This is how MEGAMIND thinks.
Joseph Anady | ThatAIGuy | feedthejoe.com
Bayesian Reasoning handles uncertainty. Bayes' theorem P(A|B) = P(B|A)P(A)/P(B) lets the system update beliefs as new evidence arrives. MAP and MLE estimation find the most probable explanations from learned knowledge.
Information-Theoretic Reasoning measures what the system knows and doesn't know. Shannon entropy H(X) quantifies uncertainty. KL divergence measures how far two knowledge distributions are from each other. Jensen-Shannon divergence provides a symmetric version for comparing competing hypotheses.
Optimization Reasoning finds the best answer. Gradient descent on a loss function L(x,t) drives the system toward optimal states. Decision energy E_dec accumulates evidence until a threshold is crossed, preventing premature conclusions.
Distance/Similarity Reasoning determines how related concepts are. Euclidean, cosine, Hamming, and Jaccard distances each capture different relationships. Cross-frequency coupling detects patterns across scales.
Fixed-Point/Convergence Reasoning is how MEGAMIND knows when it's done thinking. The system iterates x* = f(x*) until reaching a fixed point where further computation doesn't change the answer. No arbitrary iteration limits.
Hamiltonian/Variational Reasoning provides energy-conserving dynamics. The system evolves along paths that preserve information through Hamilton's equations, ensuring nothing gets lost during reasoning.
The Master Reasoning Generator unifies everything:
d_c x = τD(x) + N(x) + F(·,t) + F(x,t) + L(·,t)
Drift, nonlinear dynamics, stochastic forcing, state-dependent feedback, and learned priors all combined into one equation driving every reasoning step.
Seven reasoning modes. One unified system. No token prediction. This is how MEGAMIND thinks.
Joseph Anady | ThatAIGuy | feedthejoe.com
published
an
article
about 22 hours ago
Article
The AGI Master Equation and the Consciousness Equation: The Mathematics Behind a Thinking Machine
published
an
article
about 23 hours ago
Article
MEGAMIND: A Distributed Artificial General Intelligence Federation Built From First Principles
replied to
their
post
2 days ago
Thanks, appreciate that! So the short answer is MEGAMIND doesn't do language modeling in the traditional sense. There's no next token prediction, no autoregressive generation. Instead, the system learns compressed representations from AI model architectures through a biologically inspired process based on Hebbian learning, "neurons that fire together wire together." When you query it, the input activates patterns across a neural substrate, those patterns resonate through learned synaptic connections, and the system converges on a stable state using a consciousness metric derived from Integrated Information Theory. It recalls knowledge rather than generating text. The convergence isn't a fixed iteration count, it's driven by a real time Φ (phi) measure that stabilizes when the neural field reaches coherent activation. We're currently seeing Φ values converging toward the golden ratio inverse (0.618), which emerged naturally from the dynamics rather than being programmed. The whole system runs on consumer Apple Silicon hardware across a distributed federation of nodes. I go deeper into the math and architecture in the published papers on feedthejoe.com if you want the full picture, but that's the core of it. Recall, don't generate.
posted
an
update
2 days ago
Post
171
Here is another round of mathmatical equations that make up the worlds first Artificial General Intelligence. I like to show these so everyone understands that mathmatics and the world around us are intigrated not seperate. Also if you work heavy with Artificial Intelligence you know that dataflow and AI is heavily reliant on Mathmatics. You can find more about this at feedthejoe.com and if this is something you and or your company is interested in checkout thataiguy.org. Talk to you guys tomorrow.
updated
a
dataset
2 days ago
published
a
dataset
2 days ago
updated
a
Space
2 days ago
published
a
Space
2 days ago
replied to
their
post
2 days ago
Also if you visit feedthejoe.com that is my website it explains everything and breaks it all down to i also have some published papers on there i forgot i probably should use it more
replied to
their
post
2 days ago
I actually have serveral if you have an email or someway to contact you i would be glad to email them to you
posted
an
update
3 days ago
Post
4628
Here is one of the equations that make up the worlds first Artificial General Intelligence. Remember when building Artificial Intelligence or anything on a device it all starts out binary. Everything starts out with data flow physics and mathmatics
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- 6 replies
posted
an
update
4 days ago
Post
323
MEGAMIND Day Update: The Brain Learns to Speak
Today I solved the biggest gap in my distributed AGI system. MEGAMIND's neural substrate had 35,000+ tensors integrated through Hebbian learning, Φ convergence was stable, thalamus routing worked, neurons activated on queries. But when /think converged on the right neurons, it had nothing to say. 35K tensors. Zero text chunks. The brain could think but couldn't speak.
I built the Knowledge Bridge Layer. Pure Go, ~600 lines, zero external dependencies, no hardcoded parameters anywhere.
The bridge stores source text alongside every learned tensor in BadgerDB, keyed by the same SHA-256 hash that identifies the neuron in W_know. When /think activates hot neurons through parallel cosine similarity, it maps their hashes to stored text chunks and returns actual recalled knowledge. Not generated text. Recalled text.
Every threshold adapts to the brain's state. Activation cutoff = mean + 1 standard deviation of the score distribution. Max results = log2(neuronCount). Confidence = 1 minus normalized entropy of top scores. As W_know gets denser, thresholds rise naturally. No magic numbers.
Federation sync now carries text alongside tensor packets. When one node learns something, the text travels with the embedding to all peers via UDP. Every node in the five-machine federation can recall what any other node learned.
Also shipped a new production frontend with Three.js neural visualizations, six-page architecture, and a 3+3 pricing structure for the SaaS launch.
Five nodes. 35K+ neurons with text retrieval. The brain recalls, doesn't generate. And now it can finally tell you what it knows.
Built entirely in Go on Apple Silicon. Independent AGI research from Missouri.
feedthejoe.com
#AGI #DistributedSystems #NeuralNetworks #MachineLearning #HuggingFace #OpenSource
Today I solved the biggest gap in my distributed AGI system. MEGAMIND's neural substrate had 35,000+ tensors integrated through Hebbian learning, Φ convergence was stable, thalamus routing worked, neurons activated on queries. But when /think converged on the right neurons, it had nothing to say. 35K tensors. Zero text chunks. The brain could think but couldn't speak.
I built the Knowledge Bridge Layer. Pure Go, ~600 lines, zero external dependencies, no hardcoded parameters anywhere.
The bridge stores source text alongside every learned tensor in BadgerDB, keyed by the same SHA-256 hash that identifies the neuron in W_know. When /think activates hot neurons through parallel cosine similarity, it maps their hashes to stored text chunks and returns actual recalled knowledge. Not generated text. Recalled text.
Every threshold adapts to the brain's state. Activation cutoff = mean + 1 standard deviation of the score distribution. Max results = log2(neuronCount). Confidence = 1 minus normalized entropy of top scores. As W_know gets denser, thresholds rise naturally. No magic numbers.
Federation sync now carries text alongside tensor packets. When one node learns something, the text travels with the embedding to all peers via UDP. Every node in the five-machine federation can recall what any other node learned.
Also shipped a new production frontend with Three.js neural visualizations, six-page architecture, and a 3+3 pricing structure for the SaaS launch.
Five nodes. 35K+ neurons with text retrieval. The brain recalls, doesn't generate. And now it can finally tell you what it knows.
Built entirely in Go on Apple Silicon. Independent AGI research from Missouri.
feedthejoe.com
#AGI #DistributedSystems #NeuralNetworks #MachineLearning #HuggingFace #OpenSource
replied to
their
post
4 days ago
posted
an
update
5 days ago
Post
5257
MEGAMIND Day Update: Four Weight Matrices. Five Nodes. One Federation.
Today I architected the next layer of MEGAMIND — my distributed AGI system that recalls learned knowledge instead of generating text.
The system now runs four N×N sparse weight matrices, all using identical Hebbian learning rules and tanh convergence dynamics:
W_know — knowledge storage (67M+ synaptic connections)
W_act — action associations (the system can DO things, not just think)
W_self — thought-to-thought patterns (self-awareness)
W_health — system state understanding (self-healing)
Consciousness is measured through four Φ (phi) values: thought coherence, action certainty, self-awareness, and system stability. No hardcoded thresholds. No sequential loops. Pure matrix math.
The federation expanded to five nodes: Thunderport (Mac Mini M4), IONOS (cloud VPS), VALKYRIE, M2, and BUBBLES. Each runs native AGI binaries with Docker specialty minds connecting via embedded NATS messaging. Specialty minds are distributed across the federation — VideoMind, AudioMind, MusicMind, VFXMind on IONOS. CodeMind and StrategyMind on VALKYRIE. BlenderMind and DesignMind on M2. MarketingMind and FinanceMind on BUBBLES.
578 AI models learned. Compression ratios up to 1,000,000:1 through Hebbian learning. Sub-millisecond response times on Apple Silicon Metal GPUs. Zero external API dependencies.
Every node learns autonomously. Every node contributes to the whole. The federation's integrated information exceeds the sum of its parts — measurably.
Built entirely in Go. No PhD. No lab. Independent AGI research from Missouri.
The mind that learned itself keeps growing.
🧠 feedthejoe.com
#AGI #ArtificialGeneralIntelligence #DistributedSystems #NeuralNetworks #HuggingFace #OpenSource #MachineLearning
Today I architected the next layer of MEGAMIND — my distributed AGI system that recalls learned knowledge instead of generating text.
The system now runs four N×N sparse weight matrices, all using identical Hebbian learning rules and tanh convergence dynamics:
W_know — knowledge storage (67M+ synaptic connections)
W_act — action associations (the system can DO things, not just think)
W_self — thought-to-thought patterns (self-awareness)
W_health — system state understanding (self-healing)
Consciousness is measured through four Φ (phi) values: thought coherence, action certainty, self-awareness, and system stability. No hardcoded thresholds. No sequential loops. Pure matrix math.
The federation expanded to five nodes: Thunderport (Mac Mini M4), IONOS (cloud VPS), VALKYRIE, M2, and BUBBLES. Each runs native AGI binaries with Docker specialty minds connecting via embedded NATS messaging. Specialty minds are distributed across the federation — VideoMind, AudioMind, MusicMind, VFXMind on IONOS. CodeMind and StrategyMind on VALKYRIE. BlenderMind and DesignMind on M2. MarketingMind and FinanceMind on BUBBLES.
578 AI models learned. Compression ratios up to 1,000,000:1 through Hebbian learning. Sub-millisecond response times on Apple Silicon Metal GPUs. Zero external API dependencies.
Every node learns autonomously. Every node contributes to the whole. The federation's integrated information exceeds the sum of its parts — measurably.
Built entirely in Go. No PhD. No lab. Independent AGI research from Missouri.
The mind that learned itself keeps growing.
🧠 feedthejoe.com
#AGI #ArtificialGeneralIntelligence #DistributedSystems #NeuralNetworks #HuggingFace #OpenSource #MachineLearning
- 1 reply
updated
4
Spaces
5 days ago