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weights

Apr 24, 20261 min read

concept
tesla-ai
multiplication-to-addition
ai-patent

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2026-04-23: https://www.youtube.com/watch?v=GG9yOsPEGek Here is a summary of the video transcript formatted in Markdown. # Tesla’s AI Patent: The “Multiplication to Addition” Breakthrough Source: Dr. Know-it-all (crediting “Tesla Ming” for spotting the patent) Core Thesis: The fund (Tesla’s AI Patent: The “Multiplication to Addition” Breakthrough)

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INDEX
Using logs for fast multiplication
Using logs for fast multiplication
base-model-weights
consumer-grade-gpus
image-generation-control
lora-adapter
low-vram-optimization
model-artifacts
model-configuration
model-layers
model-weights
open-source-language-models
open-source-weights
open-weight-language-models
open-weights-models
Parameters
reasoning-models
specialized-ai-models
total-parameters
vram
AI & Agents
Adam Lucek - Flux model for Open AI generated image gen
Adam Lucek - quantisation of LLM
Best small LLM for local inference for instruction following
Difference between RAG and Agents for workflow
Fahd Mirza - fine tuning weights of OSS-20B
Fine Tuning RAG - Adam Lucek
How does 4bit quantisation work
Jeredblu running LLM locally
Julian Goldie SEO channel GLM 4.7
Prompt Engineering channel - new RAG multi modal approach
Reinforcement learning - locally
Using logs for fast multiplication
1-Bit LLMs: BitNet, Bonsai, and Efficient On-Device Deployment
1-Bit LLMs: BitNet, Bonsai, and Efficient On-Device Deployment
1-Bit LLMs BitNet Bonsai and Efficient On-Device Deployment
Llamacpp Local LLM Inference for Accessible Private AI
TurboQuant Reducing LLM Memory Footprint via KV Cache Compression
MiniMax M27 Open Source LLM Technical Overview and Deployment Summary
Demystifying AI Transformer Training on a 1979 PDP-11
LLM Inference: Engines, Memory Mapping, and Performance Optimization
DeepSeek V4: Next-Gen Open-Source LLM Performance and Efficiency Analysis
Google Gemma 4: Open-Weight AI for Local, Private Execution

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