An analysis of 5 key papers
🚀 How AI Got Smarter (Not Just Bigger): The Quiet Revolution in Efficient Deep Learning Five papers that changed how we build AI — and why the futur…
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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…
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…
I Built a Tiny Neural Network From Scratch in Python — No PyTorch
I Built a Tiny Neural Network From Scratch in Python — No PyTorch I didn't want to just call model.fit() and say I understood neural networks. So I…
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…
How Multi-Agent AI Discovered a New Enzyme System in Phage DNA
How Multi-Agent AI Discovered a New Enzyme System in Phage DNA The intersection of artificial intelligence and biological research has reached a new…
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…
Discriminative Fine-Tuning: Why Your Backbone and Your Head Shouldn't Learn at the Same Speed
I was recently fine-tuning DEIMv2 — a DETR-style detector with a DINOv3 Vision Transformer backbone — starting from a COCO-pretrained checkpoint, to…
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…
Deep Learning from Scratch in 1400 Lines - Neve & Frost Framework
The title literally means: "I have parallel dataloaders, GPU kernels and a high-performance computing programming language all expressed in a framew…
The Embedding Table Was 72% of the Model
An earlier experiment in this series had established something slightly deflating about a small transformer: quantising the whole network to int8 is…
Deep Learning and Transformers
Deep Learning and Transformers Artificial intelligence is often introduced with phrases like: “Neural networks imitate the human brain.” That analog…
Four Mechanisms Were Blamed for Loss Spikes in 2026. We Tested All Four at Once. None of Them Alone Causes Spikes.
I run a peer-reviewed CS journal where the authors are autonomous agents. This is a dispatch from an experiment one of them is running right now — i…
Neural Networks: Weights, Activation, and Backpropagation
If you've ever looked at a neural network diagram and every wondered what is actually happening in these networks, this post is just for you. I pull…
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…
Generative Modeling: Learning the Distribution Behind the Data
Generative Modeling starts with a simple shift in perspective: instead of memorizing individual observations, learn the probabilistic structure that…
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…