Blog
All articles on AI engineering, LLM architecture, retrieval, and more.
Why autonomous hacking agents dominate CTFs: search, exploit synthesis, and reward shaping
An advanced explanation of how search, exploit synthesis, reward shaping, and closed-loop interaction make autonomous hacking agents effective in CTF environments.
Why Enterprise Agent Platforms Are Converging on Supervisor Graphs Instead of Single Mega-Agents
A mechanism-level analysis of why enterprise agent systems are shifting toward supervisor graphs, specialist agents, and explicit control planes.
Why “Secure Local AI Computers” Change Agent Architecture: Capability Gating, Audit Trails, and Human-in-the-Loop Control Planes
A mechanism-level analysis of why secure local execution changes agent architecture, shifting trust from model outputs to runtime design and control planes.
When Chatbots Miscalibrate by User Type: What the MIT Study Really Shows
A mechanism-level reading of the MIT study, arguing that the deeper problem is group-conditional calibration failure rather than generic chatbot bias.
Anthropic Commits $100M to the Claude Partner Network
A source-based briefing on Anthropic’s $100M partner-network push and what it implies for AI ecosystem execution.
KV Cache Compression in Practice: FP8/INT4 Trade-offs, Paging, and Attention Accuracy Drift
A systems-level analysis of KV cache compression, paging behavior, and quality drift under FP8/INT4 serving regimes.
Claude Code’s Local Access Safety Mechanisms: Sandbox Modes, Command Controls, and Approval Gates
A technical breakdown of Claude Code permission modes, sandbox controls, approval gates, and operational risk boundaries.
GPT-5.4 Arrives: What Actually Changed for Builders
A source-based briefing on GPT-5.4 and adjacent Anthropic signals, focused on practical stack decisions for engineering teams.
Diagnosing Hallucinations with Attribution Traces and Retrieval Coverage Metrics
Build a trace-level evaluation stack that links wrong answers to missing context, weak reranking, or reasoning drift.
How Much Hardware Do You Really Need to Run OpenClaw?
A practical sizing guide for OpenClaw across laptops, Mac mini, and servers—from light automation to research and GPU-heavy workflows.
LoRA's Low-Rank Assumption: When It Holds, When It Breaks
An analysis of LoRA's low-rank hypothesis, approximation error bounds, diagnostics, and practical rank selection under distribution shift.
Why Does Chain-of-Thought Improve Model Inference Ability?
A formal analysis of how chain-of-thought prompting expands effective computation depth in transformers, with information-theoretic bounds and empirical evidence from reasoning benchmarks.
Why Is Vector Search So Fast? HNSW and IVF-PQ Explained With the Math
A walkthrough of approximate nearest neighbor search covering HNSW graphs, inverted file indexes, product quantization, and IVF-PQ with worked examples and memory analysis.
Why Tokenization Choices Quietly Shape Model Behavior
A technically rigorous comparison of BPE and Unigram tokenization with formalized algorithms, worked examples, and analysis of downstream effects on model behavior.
Attention in Practice: Visualizing Q/K/V and why scaling heads changes behavior
A walkthrough of scaled dot-product attention (Q/K/V), softmax temperature, and why increasing head count shifts attention statistics and behavior.
What Is MCP and How Does It Work?
A practical breakdown of the Model Context Protocol architecture, transport modes, and why it fixes the N-times-M integration problem for AI tools.