Keeping Two Faces From Swapping Mid-Verse: Identity Preservation in AI Music Videos
The problem nobody warns you about You train a model to turn two photos into a short music video. The first three seconds look great. Then, around s…
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EmbeddingGemma 2: How Google's Open Multimodal Embedding Model Works
EmbeddingGemma 2: How Google's Open Multimodal Embedding Model Works If you've built a RAG pipeline or a semantic search system, you've almost certa…
Understanding Prism: Dynamic Sparse Attention for Joint Video-Audio Generation
Prism is a dynamic sparse-attention framework for training video-and-audio generation models at high resolution. It aims to reduce the cost of atten…
How AI Rap Duo Video Generators Work Under the Hood (and How to Pick One)
The "Hotel Lobby AI" trend is everywhere right now: two photos in, a twelve-second rap performance out, with two familiar faces sharing an orange st…
Ornith-1.0-9B vs. Qwen3.5 vs. Gemma4: A Local AI Battle Royale
Every few weeks a new model lands on Hugging Face with a specific claim: post-trained for agentic coding, tuned for tool use, optimized for terminal…
Why AI Lies Without Knowing It’s Lying | Victor Amit
Understanding Hallucinations, False Confidence, Statistical Prediction, and the Engineering Gap Between Plausibility and Truth A developer asks an a…
Julia 1: A 144M-Parameter Decision Model Trained for $104
A small AI lab in Brazil just released a model with an unusual claim. Not "beats GPT." Not "agentic superintelligence." The claim is this: a 144.3-m…
LoRA & DoRA: The Math, Memory, and Trade-offs
Every engineer who has ever tried to fine-tune a modern 27B or 70B parameter model knows the rude awakening of GPU memory arithmetic. You look at th…
SFT vs. RL: What Changes Inside the Model?
In modern generative AI post-training, two fundamental paradigms dominate: Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable R…
Prompt Engineering for Cinematic Video: How to Control Camera Trajectory and Subject Consistency Across Video Models
Most generative video pipelines fail predictably at the same threshold: the moment you ask for coordinated camera motion alongside character action,…
# Beyond Transformers: A Mathematical Critique of Cross-Lingual Diffusion Architectures
Beyond Transformers: A Mathematical Critique of Cross-Lingual Diffusion Architectures The current state of multilingual NLP is dominated by transfor…
From Data to Deployment: What an End-to-End Machine Learning Workflow Actually Looks Like
A practical guide to understanding what happens between a raw dataset and a machine learning model running inside a real application. A beginner's f…
Neural Networks Explained: From Inspiration to Implementation
Neural networks are one of the most important ideas behind modern AI. They power applications such as image recognition, speech assistants, recommen…
RL 2: The first physical and computational RL machines(1948–1954)
Blog 1 established the rules of learning from biology. This era asked the harder question: can we build a machine that actually does it? And so, in…
Modeling Data Distributions: The Core Idea Behind Generative Models
A generative model is not trying to memorize a dataset. Its real target is the probability structure that could have produced the observed data. Tha…
Every Greedy Metric Said the Model Was Improving. Then pass@64 Fell From 0.83 to 0.19
Every number we watched said the run was working. Correct-per-sample probability tripled. The greedy accuracy curve was climbing. By the numbers on…
The AI Industry Stack: A First-Principles Map from Foundation to Frontier (2026 Deep Dive)
The Number That Stopped Me September 2026: NVIDIA's market cap breaks $3.5 trillion -- the first technology company in human history to reach that n…
RL 1: Biological foundations and the "Law of Effect" (1898– 1949)
Before GPUs. Before neural networks. Before transistors were even a rumor. There was a hungry cat in a wooden crate, trying to get to a piece of fis…
Generative Modeling: From Data Distributions to Deep Generative Models
If you approach generative models as ""networks that create images,"" the field quickly turns into a collection of disconnected architectures. A mor…
My Model Had 100% Recall — Then I Realized It Was Predicting "Exoplanet" for Everything
A few months ago, I set out to build a model that detects exoplanets from stellar brightness data (light curves) collected by NASA's Kepler telescop…
The AI Revolution Isn’t Coming — It’s Already Here. Are You Ready to Build With It?
Artificial Intelligence isn't something developers are waiting for anymore. It's already changing how we write code, analyze data, search informatio…
QUASAR: How Saliency-Weighted Reconstruction Closes the Loss Floor Gap in LLM Quantization-Aware Training
QUASAR: How Saliency-Weighted Reconstruction Closes the Loss Floor Gap in LLM Quantization-Aware Training Quantization is one of the most practical…
GPT-5.6-Cyber: Daybreak Red, Exclusive Access Security
The Lock and Key: My First Brush with Daybreak Red's Gatekeepers The login screen was a study in minimalism. White space, a single text field, and t…
How Two-Stage Object Detectors Went From 47 Seconds to Real-Time-Adjacent
Object detection has two broad architectural families: one-stage detectors that predict boxes and classes directly in a single pass, and two-stage d…
Decoupling Physical Control and Reasoning: DeepMind's Gemini Robotics 2 Architecture
Why Decouple Reasoning from Motor Control General-purpose robots have to pull off two very different jobs at once. They need to read a cluttered, fu…
CoMem Explained — From Paper to Working Code in 10 Minutes
You've hit the wall: your long-context LLM pipeline eats 89 GB of VRAM for 128k tokens, your RAG system loses the thread of a long document, and eve…
First Commit of Machine Learning
Hey everyone! This side Lucky Jha, a final year engineering student, so like everyone does I also started writing blogs for some reason and almost e…
From Learning Machine Learning to Competing on Kaggle: My First End-to-End Playground Competition Journey
How I applied Exploratory Data Analysis, Feature Engineering, Pipelines, and Ensemble Models to solve a real-world machine learning problem—and the…
What Is PEFT? A Guide to Parameter-Efficient Fine-Tuning
A technical guide comparing LoRA, QLoRA, rsLoRA, AdaLoRA, DoRA, IA³, prompt tuning, and adapter deployment workflows. DEHA Research · July 16, 2026…
PyGo: A Deep Learning Framework Where Go Calls Python Calls C++
[Project] PyGo – embedding CPython inside a Go process to build a deep learning framework I've been working on something a bit unusual: a deep learn…