A Gamma Squeeze is a stock moving higher because people who sold “out of the money”stock option Calls are caught by a rapidly increasing stock price very close to the time decay expiration of the Call that they sold.
Tesla shot passed $1000 a share on a gamma squeeze. People who is sold out of the money options were scrambling to cover them by buying shares.
by Trentism October 28, 2021
Software 2.0 is software based artificial intelligence and defined by weights in a large language model.
Andrej Karpathy, a prominent Al researcher and former senior director of Al at Tesla, coined the term
"Software 2.0"
Software 2.0 refers to a new paradigm in software development where traditional programming is replaced by machine learning models. Instead of writing explicit code, developers create and train neural networks that learn from data to perform tasks. This approach allows software to solve complex problems like image recognition, natural language processing, and more, by learning from examples rather than following predefined rules.
Andrej Karpathy, a prominent Al researcher and former senior director of Al at Tesla, coined the term
"Software 2.0"
Software 2.0 refers to a new paradigm in software development where traditional programming is replaced by machine learning models. Instead of writing explicit code, developers create and train neural networks that learn from data to perform tasks. This approach allows software to solve complex problems like image recognition, natural language processing, and more, by learning from examples rather than following predefined rules.
Most of the large language models we use aren’t compiled code. They are all software 2.0. The software that full self drives my Tesla is software 2.0.
by Trentism January 11, 2025
A knowledge distillation approach that uses extreme loss function weighting to force neural networks to preserve semantic differences between distinct concepts while preventing mode collapse. The technique employs "nuclear" (extreme) lambda parameters that heavily weight diversity preservation over teacher alignment, ensuring that different input concepts produce genuinely different vector representations.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
The researchers implemented nuclear diversity in their knowledge distillation pipeline, using extreme lambda weighting of 6.0 for diversity preservation versus 0.02 for teacher alignment, successfully reducing semantic collapse from 0.998 to 0.324 cosine similarity between distinct concepts.
by Trentism July 09, 2025
Did you hear that he is now one of the newest Teslanaires ? Yes, he is worth $1.4 Million dollars from his investment in Tesla stock.
by Trentism January 13, 2021
A bidirectional component that converts human language inputs directly into high-dimensional semantic vectors and reconstructs human-interpretable outputs from those vectors, bypassing traditional tokenization. Unlike a tokenizer—which segments text into discrete linguistic units—the Neuralator enables concept-native processing by preserving semantic relationships in compressed vector form.
Sometimes spelled: Neurolator
Sometimes spelled: Neurolator
In contrast to BERT’s tokenizer, the LN system uses a Neuralator to encode and decode conceptual information without relying on syntactic fragmentation.
by Trentism July 09, 2025
A Holon is a high-dimensional vector or tensor field based Token, potentially spanning hundreds or even thousands of dimensions, where each dimension represents a specific attribute or modality. This allows for the capture of complex relationships and dependencies between different aspects of reality. Holons are "multimodal tokens", a fundamental data structure within a simulated artificial intelligence environment.
By 2030 most AI systems will converge on using Holons which are Multimodal Tokens over the soon to be obsolete language based tokens.
by Trentism January 16, 2025