Governance: The "Human in the Loop" Fallacy
Humans cannot keep pace with AI outputs at scale. Here is why enterprise growth relies heavily on Constitutional AI, rather than just throwing more human reviewers at the problem.
Insights & Research
The Engine Room
From Silicon to Strategy. The latest thinking from the frontlines of building AI.
Latest Publication
The Open Source AI Tipping Point: Open Weights, Data Provenance, and What Still Locks In
Open-weight models from Meta, Mistral, and the Llama 4 ecosystem have shifted the AI debate from "open vs. closed" to a more nuanced question: what does open source actually mean when the training data remains invisible?
Read Full ArticleStrategy & Economics
View all Strategy insights →
The Democratization of Quant Finance: How Open-Source AI Is Reshaping Wall Street
Open-source AI-driven stock analysis tools are putting hedge-fund-grade quantitative analysis in every developer's terminal - and forcing traditional quant firms to rethink their moats.
AI Governance in the Era of Autonomous Systems
Traditional AI governance was built for human-in-the-loop systems where a person reviewed every decision. Autonomous agents remove the human from the loop entirely. Here is how governance frameworks...
The True Cost of Agentic Workflows
Hidden compute and API costs accumulate fast when deploying autonomous agent loops in production. A candid look at the real economics of agentic workloads.
AI Infrastructure
View all Infrastructure insights →
The CUDA Monopoly Breaks: Running Unmodified CUDA on AMD GPUs in 2026
SCALE and other CUDA-compatibility layers are cracking Nvidia's software moat, letting unmodified CUDA binaries run on AMD hardware. Here is what it means for AI inference costs and enterprise...
Serverless Inference: Conquering the 5-Second Cold Start
Serverless inference promises pay-per-request economics but the five-second cold start destroys the user experience. Here is what actually works: persistent model workers, speculative warmers, hybrid...
Data Gravity: Why Your Enterprise Data Dictates Your AI Infrastructure Choice
Your data location is no longer an afterthought. When every cloud provider promises the best AI infrastructure, the real tiebreaker is where your company's enterprise data already lives. We explore...
The Kubernetes for AI Paradigm
Native K8s orchestration is evolving to handle GPU scheduling, checkpointing, and live migration at the scale that AI demands.
Agentic AI
View all Agentic AI insights →
MCP Is Eating the AI Tooling Stack: Why Anthropic's Protocol Is the TCP/IP of Agentic AI
How the Model Context Protocol is becoming the universal interoperability layer for agentic AI, and why its donation to the Agentic AI Foundation marks a Kubernetes-level inflection point for...
Agent Correctness: Evaluating Tool Use Errors and Hallucinations
Text hallucinations get all the attention in LLM evaluation. But the more expensive failure mode in production agents is tool use: calling the wrong endpoints, inventing parameters, and executing...
LLMs are Terrible Backends: Forcing Strict JSON Output
When you use LLMs as API endpoints, their probabilistic nature breaks downstream systems. Here is how to enforce strict JSON output through grammar-constrained generation and structured outputs.
Handling Context Window Limits in Multi-Agent Loops
Architectural patterns for summarizing, pruning, and passing context between collaborative subagents without hitting OOM errors.
AI Engineering
View all AI Engineering insights →
Speculative Decoding: Breaking the Autoregressive Bottleneck
You do not need more GPU power to speed up LLM generation. You need a draft model. Speculative decoding uses small inexpensive models to propose multiple tokens at once, letting a large model verify...
Automated Agent Trajectory Evaluation
Building synthetic adversaries that grade and automatically improve agent execution paths. A hands-on framework for agent quality assurance.
Agent Correctness in Production: Moving Beyond Text Hallucination
Agent correctness in production: when text hallucinations are only half the problem. Structural errors, semantic drift, and the production monitoring gaps that kill autonomous agent systems.
Real-Time Video/Vision Pipelines for Multimodal AI
Architecting low-latency streaming pipelines for continuous multi-modal ingestion without bottlenecking I/O.
Looking for a specific technical architecture?
The archive is fully searchable. Use the rapid Pagefind component or hit Cmd/Ctrl + K anywhere on the site.