#neuron activation visualization

20 related articles

Aug 26, 2026·1 min

Latent Space Casino: Exploring LLM Tokenization and Neuron Activation Through Gambling Games

Latent Space Casino gamifies LLM tokenization and neuron activation through Blackjack and Neuron Roulette, making AI interpretability fun and accessible.

Aug 16, 2026·1 min

3D Visualization Revealed: How LeNet-5 Recognizes Handwritten Digits

A VRML+Python 3D visualization of LeNet-5 CNN reveals the complete MNIST handwritten digit recognition inference process, opening the black box layer by layer.

Aug 16, 2026·3 min

Visualizing MLPs with VRML: A 3D Neural Network Demo of the XOR Problem

Explore how VRML 3D visualization demonstrates a multi-layer perceptron (MLP) solving the classic XOR problem, showing weight changes, activation propagation, and gradient descent in neural networks.

Aug 10, 2026·3 min

VHectorLab 3D: An Open-Source 3D Visualization Tool for Exploring LLM Latent Space Structures

VHectorLab 3D is an open-source 3D visualization tool built on Three.js and WebGL, integrating Top-K Sparse Autoencoders to help researchers explore vector geometry in LLM latent spaces.

Aug 10, 2026·5 min

Building a Neural Network from Scratch with NumPy: A Complete Hands-On Guide

Learn how to build a neural network from scratch using only Python and NumPy, covering forward propagation, backpropagation, gradient descent with full code walkthrough and learning resources.

Aug 6, 2026·4 min

Getting Started with Machine Learning: How to Overcome Math Anxiety and Find a Pragmatic Learning Path

Scared off by math when starting ML? This article addresses beginners' math anxiety, clarifies how much linear algebra, calculus, and statistics you actually need, and provides a pragmatic top-down learning path with recommended resources.

Jul 27, 2026·3 min

How Hidden Layers Solve the XOR Problem: A Complete Walkthrough from Math to Geometry

A deep dive into how neural network hidden layers solve the XOR problem through feature space transformation, with math, geometry, and concrete examples.

Mindwalk: Replaying AI Coding Agent Be…

Jul 24, 2026·4 min

Mindwalk: Replaying AI Coding Agent Behavior on a 3D Code Map

Mindwalk renders codebases as 3D maps, visually replaying the full operation trajectories of AI coding agents like Claude Code and Cursor. A deep dive into its core ideas, use cases, and the future of agent observability tools.

Jul 19, 2026·3 min

Interpretability: The Science and Practice of Opening the Neural Network Black Box

Why do neural networks make the decisions they do? This article explores AI interpretability — mechanistic interpretability, CoT monitoring, and safety auditing — and how researchers reverse-engineer large models for AI safety.

Jul 16, 2026·2 min

Dissecting a Single Convolutional Neuron: A New Approach to Mechanistic Interpretability

An independent researcher dissects a single 1×1 convolutional neuron in InceptionV1, using Hadamard product clustering to reveal detection patterns and discovers how gradient descent hides concepts in noise.

Dense: An Open-Source ML Workbench Bui…

Jul 15, 2026·2 min

Dense: An Open-Source ML Workbench Built for Neural Network Architecture Research

Dense is an open-source ML IDE for neural network architecture research. It integrates the DeltaImportance layer and architecture visualization to help researchers iterate faster and analyze network importance during the design phase.

Relm: An Open-Source Tool for Integrat…

Jul 13, 2026·3 min

Relm: An Open-Source Tool for Integrating Local LLMs as Native R Objects

Relm wraps local LLMs as native R objects, enabling local inference, data privacy, and interpretability analysis. A deep dive for R-based data scientists.

Jul 12, 2026·6 min

Jacobian-Lens in Practice: Manually Reshaping LLM Behavior with J-Space

After Anthropic released Jacobian-Lens, a developer reversed it from an interpretability tool into a behavior editor, manually tuning J-Space to reshape LLM outputs. An in-depth look at the tech, representation engineering, and AI safety risks.

Jul 10, 2026·3 min

Visualize and Edit AI Reasoning Chains: Making LLM Thinking Transparent and Controllable

A new web tool visualizes and lets users edit AI chain-of-thought reasoning. Explore how it works, its use cases, and the limits of LLM interpretability.

Jul 10, 2026·3 min

IMGNet: A New Face Verification Approach Where Sign Pattern Matching Challenges Cosine Similarity

IMGNet is a 10MB face verification model replacing cosine similarity with sliding window sign pattern matching. Sign metrics outperform cosine on LFW and other benchmarks, suggesting sign consistency may be intrinsic to quality face embeddings.

Jul 9, 2026·6 min

AMD Ryzen AI Halo Dev Kit: A $4,000 Local LLM Powerhouse Taking on Apple and NVIDIA

AMD officially unveils the Ryzen AI Halo local AI dev kit, priced around $4,000 with 128GB unified memory, capable of running 70B LLMs locally. An in-depth look at its specs, pricing, and market competition.

Jul 9, 2026·7 min

Block-Sparse Featurizers: Opening the Black Box of Vision Model Neural Geometry

Block-sparse featurizers remap dense vision model activations into block-sparse representations, making the internal feature spaces of ViT, CNN, and other models readable and interpretable. This article explores their core principles, links to mechanistic interpretability, and applications.

Jul 8, 2026·2 min

LLM Thought Visualization: Making AI Reasoning Transparent and Interpretable

LLM thought visualization is emerging as a key breakthrough in AI explainability. This article explores the value, technical approaches, and challenges of visualizing Chain-of-Thought reasoning.

Jul 7, 2026·5 min

Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital

A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.

Research

May 23, 2026·2 min

Anthropic's New Research: Reading AI's Inner Thoughts with Natural Language Autoencoders

Anthropic's Natural Language Autoencoder translates Claude's internal activations into readable text, revealing Claude can identify safety tests—exposing fundamental limits of AI evaluation.