Series

Every list of KV cache techniques presents a dozen of them as peers. On one A100 the spread is enormous: prefix reuse cut a shared-prefix workload from 59.4s to 6.2s, while tuning the paged block size moved capacity by under 1%. The reason you cannot rank them on one number is that they pay out in different units.

I trained BDH and counted more than 4.8 billion activations across three controls. Training moved its single-latent sparsity from 49.98% to 81.65%, but a similar-budget ReLU baseline reached 91.04%. Sparsity is real; by itself, it is not evidence of interpretability.

We reproduced Table 5 of arXiv:2609.04575 on one A100 and measured the throughput the paper leaves out: nothing in HF transformers, nothing at batch 1 in stock vLLM, 1.35× at batch 8.

Compress KV cache to 3-bit with PolarQuant + Lloyd-Max. 4.6x memory savings with zero accuracy loss, no retraining.

NVIDIA Nemotron 3 Nano, Qwen 3.5, and Mamba-3 independently converge on 75% linear layers + 25% attention + MoE. 88% KV-cache reduction, O(n) complexity for long-context processing.

We measure, on a local model and on GPT-4o-mini, exactly how much answers change with sampling temperature. Temperature doesn't create diversity -- it amplifies what's already there.

Every piece of text Claude generates now carries an invisible watermark — with nothing added to the text. A step-by-step walkthrough of SynthID-Text: the secret key, tournament sampling, detection, and the honest limits.

karpathy-skills, claude-mem, Cognee — comparing 3 approaches to solving the AI memory problem.

Build Self-Attention from scratch. Compare MHA → GQA → MQA evolution in code. KV Cache mechanics and Prefill vs Decode analysis.

From LoRA theory to Qwen 2.5 7B model setup. 99.8% parameter reduction and 86% memory savings vs full fine-tuning, explained with code.

Install AgentScope, learn 5 core concepts (Agent, Model, Memory, Toolkit, Formatter), and build a tool-using ReAct agent.

A code-level deep dive into Karpathy's autoresearch. Dissecting train.py, BPE tokenizer, MuonAdamW optimizer, and the agent protocol design.

DeerFlow 2.0 architecture, setup, and first task execution. A SuperAgent runtime with 9 agent nodes, 5 tool sources, and Docker sandboxes.

Complete comparison of Qwen 3.5 and DeepSeek V3.2: architecture, benchmarks, hardware requirements, and practical recommendations.

Diagnose naive RAG limitations, classify query intent, and route to the optimal retrieval source with LangGraph. Implement adaptive retrieval that skips unnecessary searches.

Understand the foundational ReAct pattern. The difference between chatbots and agents, the Thought-Action-Observation loop, and why ReAct falls short in production.

How microgpt.py's 15-line backward() works. From high school calculus to chain rule, computation graphs, topological sort, and backpropagation.

A line-by-line dissection of microgpt.py -- a pure Python GPT implementation with zero dependencies. Training, inference, and autograd in 150 lines.

D3PM, Transition Matrices, Absorbing States, MDLM -- how to bring diffusion from continuous space to discrete tokens.

Reversal Curse, Counting, Compositional Reasoning — fundamental Transformer failures tested across 7 models.

Individual neurons are uninterpretable. Sparse Autoencoders extract monosemantic features from model internals, and TensorLens analyzes the entire Transformer as a single unified tensor.

5 Non-Negotiable Standards for Enterprise Deployment

Why can't traditional RAG answer "What are the main themes in these documents?" Microsoft Research's GraphRAG reveals the secret of community-based search.

Vector search alone isn't enough. Upgrade your RAG system with Knowledge Graphs that understand entity relationships.

What if Claude automatically ran lint, tests, and security scans every time it generated code? Learn how to automate team workflows with Hooks.

What if Claude could read Jira tickets, send Slack messages, and query your database? Learn how to extend Claude's capabilities with MCP servers.

EXPLAIN, indexes, WHERE vs HAVING — diagnose and optimize slow queries yourself

U-Net shows diminishing returns when scaled up. DiT improves consistently with size. Complete analysis of the architecture behind Sora.