AI Principles
The science behind AI — transformer architectures, training dynamics, and evaluation methodology. MONA explains how AI actually works, with precision over hype.
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BM25, SPLADE, and Reciprocal Rank Fusion: The Building Blocks of Production Hybrid Search
BM25, SPLADE, and reciprocal rank fusion each solve a different retrieval problem. Here's how the three combine into a …
Why RAG Still Fails in Production: Retrieval, Chunking, Grounding
RAG fails in production because retrieval, chunking, and grounding hit structural limits — not because of bugs. Why …
What Is RAG and How LLMs Use Vector Search to Ground Their Answers
Retrieval-augmented generation pairs an LLM with a vector index so answers are grounded in real documents — not just …
Score Mismatch, Tuning Hell: The Hard Limits of Hybrid Search Fusion
Hybrid search merges BM25 and vector results, but the fusion step has hard limits. Score mismatch, RRF blindness, and …
From Chunking to Reranking: RAG Pipeline Components and Prerequisites
Every RAG pipeline runs five components — chunker, embedder, vector store, retriever, reranker. Here is what each one …
What Is AI Background Removal? How Salient Object Segmentation Works
AI background removal is not one model — it's salient object detection plus alpha matting. See how U2-Net, BiRefNet, and …
Prompt Engineering for Image Generation: How Diffusion Models Read Text
Image prompts steer probability, not pixels. Learn how diffusion models, cross-attention, and CFG turn text into images …
Negative Prompts, Weights, Seeds: Image Prompting Limits 2026
Negative prompts and weight syntax aren't universal — and seed reproducibility breaks across model versions. Inside the …
Alpha Channels, Trimaps, and the Hard Limits of AI Background Removal
Background removal is alpha estimation, not subject detection. Learn how trimaps and matting work, and why hair, glass, …
How LoRA Fine-Tunes Diffusion Models for Image Generation
LoRA fine-tunes Stable Diffusion and FLUX without retraining. Learn how rank, alpha, and the BA decomposition turn a …
Why AI Upscalers Hallucinate Faces and Tile Seams at 4K and 8K
AI upscalers don't break at 4K and 8K because of weak hardware. The failures are structural — rooted in diffusion priors …
What Is Image Upscaling and How AI Super-Resolution Reconstructs Detail Beyond the Original Pixels
AI image upscaling doesn't enlarge what was captured — it generates plausible pixels from a learned prior. Learn how GAN …
Training Image LoRAs: Diffusion Math, Rank-Alpha, and VRAM Limits
Image LoRAs retarget diffusion models with small adapter files. Learn the rank-alpha math, VRAM ranges from SD 1.5 to …
From RRDB Blocks to Diffusion Priors: Inside Modern AI Upscalers
How modern AI upscalers are built — from ESRGAN's RRDB blocks and Real-ESRGAN to SUPIR's diffusion prior, plus the …
What Is AI Image Editing? Inpainting, Outpainting, Edit Models
AI image editing uses diffusion to modify pixels under a mask or follow text instructions. Learn how inpainting, …
From Diffusion to InstructPix2Pix: AI Image Editing Prerequisites
Before using GPT Image or FLUX, understand diffusion, classifier-free guidance, and why InstructPix2Pix made …
What Is a Diffusion Model? How Reversing Noise Creates Images and Video
Diffusion models generate images by reversing noise. Learn how forward and reverse processes differ, and why predicting …
U-Net, VAE, Schedulers, and Text Encoders: The Anatomy of a Modern Diffusion Model
A modern diffusion model is not one network but four: a VAE for compression, a U-Net or DiT denoiser, a text encoder, …
Multimodal Architecture: How Models Fuse Text, Images, Audio & Video
Multimodal models like GPT-5 and Gemini 3.1 Pro don't see images — they translate them into token space. Here's the …
From Vision Transformers to Modality Gaps: Prerequisites and Technical Limits of Multimodal AI in 2026
Before multimodal AI works, vision transformers, modality gaps, and grounding decay define its limits. The mechanics of …
Diffusion Models in 2026: Slow Sampling and Hard Engineering Limits
Why diffusion models still need many sampling steps, why FLUX and SD 3.5 stumble on text and hands, and where the 2026 …
What Is a Vision Transformer and How Image Patches Replaced Convolutions in Computer Vision
Vision Transformers treat images as token sequences, not pixel grids. Learn how 16x16 patches, self-attention, and …
What Is a State Space Model and How Selective SSMs Replace Quadratic Attention
State space models trade quadratic attention for linear recurrence. See how Mamba's selection works and why long-context …
In-Context Learning Gaps, Hybrid Complexity, and the Hard Technical Limits of State Space Models
State space models trade recall for speed. Learn why pure Mamba breaks on in-context tasks and how hybrid SSM-attention …
Patch Embeddings, Class Tokens, and 2D Positional Encoding: Inside the Vision Transformer
How Vision Transformers turn images into token sequences — inside patch embeddings, the CLS token, and the shift from 1D …
From HiPPO to Selective Scan: The Components and Prerequisites of State Space Models
State space models rebuilt recurrence on new math. Trace the components — HiPPO, S4, selective scan, gating — and the …
From CNN Intuition to Data Hunger: Prerequisites and Hard Limits of Vision Transformers
Vision Transformers drop CNN priors for learned attention — a trade that changes everything. Learn the prerequisites, …
What Is Mixture of Experts and How Sparse Gating Routes Inputs to Specialized Sub-Networks
Mixture of experts activates only selected sub-networks per token. Learn how sparse gating makes trillion-parameter …
Routing Collapse, Load Balancing Failures, and the Hard Engineering Limits of Mixture of Experts
MoE models promise scale at fractional compute cost. Understand routing collapse, memory tradeoffs, and communication …
From Feedforward Layers to Expert Pools: Prerequisites and Building Blocks of MoE Architecture
Mixture of experts replaces one feedforward layer with many expert networks and a router. Learn how MoE gating and …