How AI Models Actually Work
Deep technical explanations with code, diagrams, and mathematical foundations. Understand the architecture behind GPT-4, Claude, and other modern LLMs.
Agentic Design Patterns Part 2: Foundational Patterns with Working Code
Deep dive into prompt chaining, routing, parallelization, reflection, tool use, planning, and multi-agent collaboration. Real Python code you can run and modify.
How We Track AI Model Costs: Real Data, Not Marketing Claims
Behind the scenes of our cost analysis methodology. See how we track token usage, calculate real costs, and determine which AI models actually save you money.
Agentic Design Patterns: Complete Guide to Building AI Agents
Deep dive into the 21 essential design patterns for building autonomous AI agents. Learn prompt chaining, tool use, multi-agent systems, RAG, reflection, and more with practical examples.
ChatGPT Evolution: From GPT-3.5 to GPT-4 Turbo
How OpenAI's ChatGPT models have evolved across standardized benchmarks. Performance comparison on MMLU, GSM8K, and TruthfulQA showing real-world improvements from GPT-3.5 to GPT-4 Turbo.
GSM8K: Testing AI Mathematical Reasoning
How we measure whether AI can actually solve math problems - from word problems to multi-step algebra. Why most models still struggle with grade school math.
MMLU Benchmark: Measuring True AI Intelligence
A deep dive into the Massive Multitask Language Understanding benchmark - the gold standard for evaluating AI reasoning across 57 academic subjects
Open-Source AI Testing Tools
Our complete suite of benchmarking and evaluation tools for testing AI systems. Run the same tests we use, verify our results, and contribute improvements.
TruthfulQA: Can You Trust Your AI?
Testing whether AI models tell the truth or spread misinformation. How we measure hallucination resistance and factual accuracy across controversial topics.
LLM Hallucinations in Practice: A Claude Sonnet 4.5 Case Study
Real-world analysis of how even advanced LLMs can overcomplicate simple problems - and how prompt engineering helps
KV Cache and Memory Management
Deep dive into KV cache optimization - the key to fast and efficient LLM inference
Evaluating Long-Context Performance
How to test if LLMs actually use their 100K+ token context windows effectively
Position Encodings in Transformers Explained
How transformers understand word order - from sinusoidal to RoPE and ALiBi
LLM Inference Optimization: Speed & Cost Guide
How to make LLM inference faster and cheaper - quantization, batching, KV caching, and more
Training Large Language Models: Complete Guide
How LLMs are trained from scratch - pre-training, fine-tuning, RLHF, and everything in between
Attention Mechanisms Explained
Visual guide to how attention works in transformers - from basic self-attention to modern sparse patterns
How Long-Context Models Work: Technical Architecture
Deep dive into the technical innovations that enable models like Claude, Kimi, and GPT-4 to handle 100K+ token contexts
Transformer Architecture: Complete Visual Guide
How transformers work from input to output - the architecture behind GPT, BERT, and modern LLMs
More Coming Soon
We're continuously adding new technical deep-dives. Topics in the pipeline:
- Position Encodings Explained
- LLM Training & Fine-Tuning
- Inference Optimization Techniques
- RLHF & Alignment Methods
- Quantization & Compression
- Mixture of Experts (MoE)