Glossary AI & Vibecoding
Fundamental reference guide for the new era of software engineering by Andriy Orlov: autonomous agents, loop engineering, Model Context Protocol, context scaling, VPS infrastructure, and burnout prevention.
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ENAdvanced Voice Mode
Native bidirectional voice communication technology in real-time (ChatGPT Advanced Voice, Gemini Live). Enables conversation with a model with a delay of up to 300 ms, allows interruptions mid-sentence, and conveys live emotions.
AI Upscaling (Super Resolution)
A technology for enhancing resolution and restoring image details (Real-ESRGAN, Magnific AI, Topaz Photo). Unlike simple pixel enlargement (bicubic interpolation), neural networks generate new realistic microtextures: skin pores, eyelashes, hair strands, and fabric.
AI Video Tools (Runway, Kling, Luma Dream Machine)
Leading modern platforms for video generation using artificial intelligence (Text-to-Video and Image-to-Video). They enable the animation of static images, control of virtual camera movement, and the creation of high-resolution cinematic videos without a film crew.
Alibaba Qwen (Leader in Open Coding and Mathematics)
A series of high-performance open models from Alibaba's cloud division (Qwen 2.5, Qwen Coder). Recognized as a leader among open models in coding, mathematics, and multilingual text processing.
Apple Silicon for AI (M-Series and Unified Memory)
Apple's processor architecture (M1/M2/M3/M4) with Unified Memory Architecture allows the entire RAM array (up to 128-192 GB) to be accessible to the GPU as VRAM, enabling the execution of massive neural networks without server-grade GPUs.
Apple Silicon MLX Framework
A native machine learning library from Apple, designed to maximize the use of unified memory and GPU cores in M-series chips (M2/M3/M4) for running large LLMs.
Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI) is a hypothetical autonomous system capable of understanding, learning, and performing any intellectual task at human-level or exceeding it in most economically valuable fields of work.
Autonomous Local AI and Data Privacy
The practice of executing machine learning models entirely on personal physical hardware without internet connectivity (Air-Gapped AI). Guarantees 100% protection against the leakage of trade secrets, personal data (GDPR/HIPAA), and cloud provider failures.
AWQ & Activation-Aware GPU Quantization
4-bit weight compression methods for language models optimized for NVIDIA tensor core architecture, maximizing throughput while preserving critical activation channels.
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ENC
ENCatastrophic Forgetting
A fundamental issue in artificial neural networks where learning a new task or language overwrites previous connections, leading to a sudden and complete loss of previously acquired skills.
Character Consistency and Style Preservation
Methodologies and tools for maintaining visual character consistency and authorial style across a series of generations. Utilized for creating illustrated books, comics, film storyboards, and brand mascots using parameters like --cref, FaceID, and LoRA.
Claude Haiku (Fast Compact Models by Anthropic)
The most compact and fastest model in the Anthropic lineup, designed for lightning-fast responses, mass text classification, data extraction, and agent routing at minimal cost.
Claude Opus (Flagship AI Model by Anthropic)
The most powerful heavyweight model in the Anthropic family, designed for complex philosophical and scientific analysis, multi-layered text generation, and deep contextual understanding.
Claude Sonnet (Claude 3.7 / 3.5 Sonnet)
The flagship engineering model from Anthropic, optimized for complex programming, large codebase management, hybrid reasoning (Extended Thinking), and autonomous agentic cycles.
Constitutional AI (RLAIF)
A behavior alignment method developed by Anthropic (creators of Claude). Instead of relying on millions of hours of human labor, the model critiques and corrects its own responses based on a clear set of ethical principles ('Constitution').
Continuous / Dynamic Batching
A mechanism for grouping incoming requests to neural networks at the token iteration level (Iteration-Level Scheduling), eliminating GPU idle time during parallel loads.
ControlNet: Managing Poses and Geometry
A neural network extension for diffusion models (Stable Diffusion) that enables control over the spatial composition of generation using depth maps, Canny edge outlines, and human pose 'skeletons' (OpenPose). It transforms chaotic generation into a precise designer tool.
D
ENDeepfakes: Audio and Video
Technology for creating highly realistic synthetic audio and video materials using neural networks. It allows for face swapping in videos, cloning voices from a 3-second sample, or generating fake speeches of public figures.
DeepSeek (Chinese Breakthrough in Open AI)
A series of groundbreaking open models from the Chinese lab DeepSeek (V3, R1). Demonstrates top-tier coding and reasoning capabilities at a cost 10–20 times lower than Western commercial counterparts.
DeepSeek-R1 (DeepSeek Reasoning Model)
A groundbreaking open weights reasoning model based on a 671B MoE architecture, demonstrating the capability for advanced logical reasoning through pure Reinforcement Learning (GRPO).
Diffusion Models
The architecture of generative models (Stable Diffusion, Midjourney, FLUX) is based on principles of non-equilibrium thermodynamics. It operates in two stages: forward diffusion (gradual destruction of an image by random noise) and reverse diffusion (step-by-step denoising to a crystal-clear image based on a textual description).
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ENF
ENFine-Tuning Basics
The process of adapting a pre-trained large model to a specialized task or style using a small, high-quality dataset (Supervised Fine-Tuning, SFT). This enables training AI on medical terminology, corporate tone, or specific code formatting within hours.
FLUX.1 (The Modern King of Photorealistic Images)
The leading image generation model from Black Forest Labs (creators of Stable Diffusion). Known for impeccable photorealism, perfect hand finger rendering, and the ability to render clear printed text.
Frontier Models
The most powerful class of artificial intelligence at the forefront of global research (Claude 3.7 Sonnet, OpenAI o3/GPT-4.5, Gemini 2.0 Pro), defining the limits of modern reasoning, autonomy, and coding capabilities.
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ENGemini Flash & Pro (Google Gemini)
A family of multimodal models from Google DeepMind that combines a record context window (up to 2 million tokens), extreme generation speed (over 150 tokens/sec), and native perception of video and audio.
Gemini Nano & Edge AI (AI Directly on Your Smartphone Without Internet)
The most compact version of Google's AI, optimized for local execution on smartphone chips (NPU). It provides audio recording summaries, intelligent responses, and photo processing entirely offline.
Generation Speed (TPS / TTFT / Latency)
Key engineering performance metrics for language models: Time to First Token (response time to input context) and Tokens Per Second (streaming output text generation speed).
GGUF & Modern Quantization Standards
The GGUF format is a universal binary file format for storing and instantaneously loading quantized language models on CPUs and GPUs in llama.cpp, Ollama, and LM Studio.
Google AI Studio (Free Studio for AI Testing)
The official web sandbox from Google for rapid testing of Gemini models. It allows free experimentation with a 2-million context, customizing system prompts, and obtaining API keys without a credit card.
Google Gemini Pro (Model with Infinite Context)
The flagship working model from Google DeepMind featuring a record context window of over 2 million tokens. Capable of analyzing entire books, lengthy videos, and massive codebases in a single query.
GPT Image / DALL-E (Image Generation in ChatGPT)
An integrated visual content generation tool directly within the ChatGPT dialogue. It allows for the creation of illustrations, concept art, poster texts, and local editing of image fragments.
GPT Mini (Compact Models of the GPT-4o Mini Series)
An economical and ultra-fast version of OpenAI's flagship models. Optimized for mass daily tasks, instant responses, text classification, and limit conservation.
GPU vs. CPU for AI: What's the Difference
A deep comparison of Central Processing Units (CPU) and Graphics Processing Units (GPU) for machine learning tasks. It explains the fundamental differences between latency-oriented and throughput-oriented architectures, memory bus bandwidth (DDR5 vs HBM3e), and CLI benchmarking.
GRPO & DPO Modern Alignment Algorithms
Cutting-edge model policy optimization algorithms (Direct Preference Optimization and Group Relative Policy Optimization) eliminate the need for cumbersome standalone critic models during reasoning training.
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ENI
ENImage Prompt Engineering
A professional methodology for crafting text prompts for graphic neural networks. The golden formula consists of five elements: main subject, environmental details, lighting characteristics, virtual camera parameters, and artistic rendering style.
Inpainting and Outpainting (Image Editing and Expansion)
Selective neural network image editing techniques. Inpainting replaces or removes an object within a selected area (mask) while preserving light and texture. Outpainting (Generative Fill) extends new space beyond the original boundaries of the photograph.
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ENL
ENLatent Space
A multidimensional mathematical vector space created by neural networks for internal representation of concepts, styles, and images. It enables remarkable arithmetic operations on concepts: for example, 'King' minus 'Man' plus 'Woman' equals 'Queen.'
Llama Family (Meta Llama)
A series of foundational open language models from Meta (Llama 3, 3.1, 3.3) that have become the industrial standard for the Open Weights ecosystem, local AI, and enterprise fine-tuning.
LLM (Large Language Model)
A fundamental class of neural network architectures based on autoregressive transformers, predicting the probabilistic distribution of subsequent tokens and demonstrating emergent properties of abstract reasoning, code synthesis, and logical inference.
LM Studio
A free desktop application for Windows, macOS, and Linux that allows users to find, download, and run open LLMs with a single click, without using the terminal. It features a built-in local server compatible with the OpenAI API.
LMSYS Chatbot Arena (ELO Rating)
A crowdsourced open platform for blind A/B testing of LLMs that determines the relative strength of language models based on the Bradley-Terry statistical model and Elo chess rating.
Local LLM Inference
The practice of autonomously executing large language models directly on developer hardware (Apple Silicon, NVIDIA GPU) with guaranteed absolute privacy and zero dependency on the internet.
LoRA Files for Styles and Faces (Low-Rank Adaptation)
A lightweight and rapid fine-tuning technology for image models (Stable Diffusion, FLUX). It creates compact files ranging from 20 to 200 MB that connect to the base model as a variable plugin, adding specific characters, artist styles, or visual aesthetics.
Lost in the Middle Effect
A well-documented cognitive asymmetry of large language models identified in Stanford research. It demonstrates that the accuracy of information retrieval is highest at the beginning and end of the input context, but sharply declines in the middle of long documents.
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ENMeta Llama (The Premier Open Standard for Artificial Intelligence)
The flagship series of open language models from Meta (Llama 3, 3.3). Available for free download, modification, and local deployment on personal servers without restrictions or censorship.
Midjourney (Leading Artistic Design Platform)
A premier closed image generator with the highest level of artistic aesthetics. The industry standard for designers, cinematographers, concept artists, and advertising creatives.
Mistral AI (European Open and Efficient Models)
A series of high-performance open and commercial models from the French startup Mistral AI (Mistral 7B, Mixtral 8x7B, Mistral Large). Known for their compactness, speed, and respect for data privacy.
Model Distillation & Reasoning Transfer
A methodology for transferring knowledge and reasoning chains from a large Teacher Model to a compact Student Model for fast and cost-effective inference.
Model Merging & Frankensteining
A technique for merging the weights of two or more distinct language models without retraining on GPU (SLERP, DARE, Ties-Merging), creating hybrid models with synergistic capabilities.
Model Parameter Count (7B, 14B, 70B)
The total number of training parameters (weights) in a large language model, where 'B' denotes billions. A key indicator of the model's intellectual capacity, operational speed, and computer memory requirements.
Model Quantization
A mathematical compression technology for neural network weights and activations by transitioning from high precision (FP16/BF16) to low-bit formats (FP8, INT8, INT4, GGUF) for radical memory savings.
Modern Speech Synthesis (Text-to-Speech / TTS)
The technology of artificial generation of human speech from printed text. Modern neural network TTS models (ElevenLabs, OpenAI Audio, Chatterbox) reproduce natural intonations, logical stresses, breathing, timbre, and emotional coloring, indistinguishable from a live narrator.
MoE (Mixture of Experts)
An architectural approach in deep learning where heavy fully-connected transformer layers are divided into dozens of specialized subnetworks ('experts'), and a dynamic router activates only a small subset for each individual token.
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ENNative Audio: Direct Speech-to-Speech Processing
The new generation of native multimodal models (GPT-4o Advanced Voice, Gemini Live) processes sound waves directly without the intermediate step of converting audio to text (STT) and back (TTS). This allows the model to perceive sarcasm, fear, laughter, whispers, and interrupt conversations on the fly with minimal latency.
Native Multimodal Reasoning
AI architectures capable of performing deep logical reasoning over code, architecture diagrams, UI screenshots, and video within a unified attention space.
Needle In A Haystack Test (NIAH)
A standard benchmark for testing the context window length of language models. A short random sentence (the needle) is hidden within a vast array of random background text (the haystack) at various positions, assessing the model's ability to locate it accurately.
Negative Prompts in Image Generation
A control mechanism in diffusion models (Stable Diffusion, Midjourney --no) that directs the mathematical vector of noise reduction away from unwanted images. It filters artifacts such as extra limbs, blurriness, watermarks, and cartoonish effects.
Neural Processing Unit (NPU)
A specialized hardware microchip (Neural Processing Unit) designed exclusively for executing artificial neural networks with minimal energy consumption. It handles background blurring in video calls, photo enhancement, and local AI prompts without draining the battery.
Next-Token Prediction
The fundamental mechanism of autoregressive large language models (LLMs). Calculation of logits, Softmax function, temperature influence, and sampling (Top-P/Top-K). Explanation of why text generation is an O(N) sequential process and how to inspect probabilities via API.
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ENOCR vs Vision LLM: The Evolution of Text Recognition
A comparison of traditional Optical Character Recognition (OCR — Tesseract, ABBYY FineReader) with modern multimodal visual models (Vision LLM). Older algorithms blindly replicate pixels with errors, while new models correct handwriting, understand tables, and compute totals.
Ollama (Local Model Deployment Platform)
A leading open-source tool for easy loading, configuration, and local execution of language models (Llama, DeepSeek, Qwen) with a built-in REST API compatible with OpenAI.
Open Weights vs Closed APIs
A comparison of two fundamental philosophies in AI distribution: Open Weights (Llama, Mistral, DeepSeek), which can be downloaded and run on your own server, versus Closed APIs (OpenAI, Anthropic, Google), where access is granted solely through subscription.
Open Weights vs Truly Open Source AI
A legal and engineering analysis of the fundamental difference between accessible numerical weights of models (Llama, DeepSeek) and fully open projects with source data, code, and architecture (OSI Standard).
OpenAI GPT (Flagship Models of the GPT Series)
The primary universal line of large language models from OpenAI (GPT-4, GPT-4o). Optimized for complex text analysis, programming, creativity, and daily intellectual tasks.
OpenAI o-Series / Reasoning (Advanced Reasoning Models)
The new generation of artificial intelligence from OpenAI (o1, o3 series) is optimized for multi-step hidden reasoning, advanced mathematics, quantum physics, and complex algorithmic coding.
OpenAI Whisper (Gold Standard for Speech Recognition)
OpenAI's open-source Speech-to-Text (STT) model. It recognizes over 100 languages, resilient to background noise, dialects, and mumbling. The standard for automatic audio transcription and voice coding.
OpenRouter (Unified Model API Gateway)
A unified AI gateway providing standardized access to hundreds of closed and open language models from various inference providers through a single balance, a unified API key, and an automatic failover mechanism.
Output Tokens Limit (Why Text Cuts Off Mid-Word)
A hardware or software limit on the maximum length of a single response (Max Output Tokens). This explains why long code or articles sometimes stop mid-sentence and how the magic phrase 'Continue' brings the model back to work.
Overfitting
A fundamental issue in machine learning where a model excessively adapts to the training dataset along with its specific noise, losing the ability to generalize on new data. This entry dissects the divergence of loss functions, regularization techniques, and Early Stopping in code.
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ENPaged Attention & KV-Cache Management
A GPU memory management algorithm that segments the KV-cache of a language model into contiguous virtual pages (similar to OS kernels), eliminating fragmentation and increasing throughput by four times.
Perplexity AI (Next-Generation Search Assistant)
A next-generation AI search engine (Answer Engine) that synthesizes comprehensive structured responses with clickable links to original sources in real-time, rather than presenting a list of ad links.
Pinokio AI Browser
A desktop standalone browser and application manager ('App Store for Open AI') that allows users to install, configure, and run complex AI environments (ComfyUI, Stable Diffusion, Whisper, FaceFusion) without needing knowledge of Git, Python, or the terminal.
Pre-Training
The initial phase of creating a Foundation Model involves feeding a neural network trillions of words from the internet, books, and code on clusters of thousands of GPUs over months, costing tens to hundreds of millions of dollars.
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ENQuantization and GGUF Format
A mathematical method for reducing the precision of model weights (e.g., from 16-bit FP16 to 4-bit INT4) and a unified binary file format GGUF for instant loading into processors and GPUs via the llama.cpp engine.
Quantization Types: FP16, INT8, INT4
Technical formats for representing neural network weights. Ranging from full 16-bit floating-point precision (FP16 / BF16) to integer compression formats (INT8, INT4, AWQ, EXL2), which define the balance between memory consumption and the intellectual quality of responses.
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ENReasoning Models
A class of next-generation AI models (OpenAI o1/o3-mini, DeepSeek-R1, Claude 3.7 Extended Thinking) that utilize Test-Time Compute scaling and an internal chain of thought for hypothesis validation.
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement Learning from Human Feedback (RLHF) is a training method that utilizes comparative human evaluations to train a Reward Model. Through RLHF, language models have learned to be Helpful, Honest, and Harmless.
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ENSampling Parameters (Temperature, Top-p, Min-p)
Mathematical hyperparameters of stochastic decoding (Temperature, Top-P, Min-P, Penalties) that govern the probability distribution for selecting the next token, defining the model's level of determinism, accuracy, and creativity.
Scaling Laws in AI
An empirical law established by OpenAI and Google (formulated by Jared Kaplan in 2020) asserting that the performance of a language model predictably increases as a power law with the growth of three factors: the number of model parameters, the volume of training data, and the computational power expended (Compute).
Seed and Determinism (Reproducing Generation Results)
A numerical identifier for the random number generator (Seed). It allows for the stabilization of randomness in language models and image generators to achieve consistent, reproducible results when repeating the same query.
Self-Attention Mechanism
A key mathematical mechanism of the Transformer architecture that enables each word in a sentence to dynamically weigh the importance of all other surrounding words. This allows the model to distinguish between homonyms and link pronouns ('he', 'she', 'it') to the correct entities.
SLMs (Small Language Models 1B–3B)
Ultra-compact next-generation models sized 1B–3B parameters (Llama 3.2, SmolLM, Qwen 2.5), designed for local execution on phones, in browsers, and on inexpensive edge servers.
Sora and AI Video (Video Generation from Text)
A cutting-edge class of generative neural networks capable of creating photorealistic dynamic videos from text or static images (OpenAI Sora, Runway Gen-3, Kling, Luma Dream Machine).
Speculative Decoding & Draft Models
A hardware acceleration technique for inferring large language models 2–3 times faster without quality loss through parallel verification of predictions from a fast draft model.
Stochastic Parrot Theory
A notable scientific critique of large language models, introduced by linguists Emily Bender and Timnit Gebru in 2021. It asserts that LLMs lack consciousness or understanding, merely repeating learned word combinations in a stochastic manner, akin to a parrot.
Sub-Quadratic Attention & Mamba / State Space Models
Cutting-edge neural network architectures with linear computational complexity O(N), enabling the processing of millions of context tokens with constant memory usage.
Synthetic Data Generation & Pretraining
A technology for generating, automatically filtering, and formally verifying training datasets using artificial intelligence to overcome the shortage of quality human data.
T
ENTemperature in Generation (Creativity and Chaos Slider)
A key numerical parameter for text generation (typically ranging from 0.0 to 1.0 or 2.0). It determines the degree of unpredictability in selecting the next token, from strict deterministic mathematics to free-flowing creativity.
TensorRT-LLM & SGLang High-Speed Engines
Deeply compiled computational engines for extreme optimization of language model inference on NVIDIA servers, utilizing graph optimization, FlashAttention-3, and advanced routing.
Test-Time Compute Scaling
A new paradigm in AI development by the end of 2026: enhancing response quality not through massive model sizes during training, but by allocating additional seconds for reasoning before generation.
Thinking Budget in New Models
A new parameter in modern hybrid reasoning models (Claude 3.7 Sonnet, OpenAI o1/o3) that allows users to define the limit of tokens or seconds the AI can spend on internal contemplation before delivering a final response.
Tokens Explained (How Many Words in a Token)
The fundamental unit of measurement for text in language models. This entry explains how words are broken down into tokens, why this impacts query costs, and why Ukrainian words consume more tokens than English ones.
Top-P / Nucleus Sampling
A probabilistic word filtering method (Nucleus Sampling) that truncates the 'long tail' of low-probability, bizarre, and nonsensical words, retaining only the most relevant options with a cumulative probability of P (typically 0.9).
Transformer Architecture
The neural network architecture introduced by Google researchers in 2017 in the paper 'Attention Is All You Need'. It forms the foundation of all modern language models (GPT, Claude, Gemini, Llama), replacing slow recurrent networks and enabling parallel processing of entire text sequences.
TTFT vs TPS (Inference Latency Metrics)
Two key engineering metrics for inference performance: Time To First Token (latency to response initiation) and Tokens Per Second (throughput of code generation).
V
ENVideo RAM (VRAM) for AI
Video RAM (VRAM) is the memory of the graphics card where neural network weights and the context window are loaded. It is the primary hardware bottleneck: if the model does not fit in VRAM, it either won't run or will operate dozens of times slower on a regular CPU.
Vision Language Models (VLM)
Multimodal models that combine the ability to perceive visual images (through patching) with the textual intelligence of LLMs. They enable object recognition in photos, analysis of complex graphs, reading receipts, and understanding interfaces.
vLLM (High-Performance Inference Engine)
Leading open-source inference engine and LLM servicing framework that revolutionizes throughput with the PagedAttention memory virtualization algorithm and continuous batching.
Voice Cloning and Audio Ethics
The technology for generating a digital replica of a person's voice from a short audio sample (ranging from 5 seconds to several minutes). It enables dubbing videos in one's own voice in different languages but poses serious risks for phone fraud and requires strict ethical verification.
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ENX
ENFull Stack of Tools, Tier Lists, and AI Skills
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