Distillation in 2026: The Hidden Arms Race
All frontier labs have converged on distillation as their primary post-training method. The real innovation is no longer in pre-training but in post-training pipeline design. Here is why distillation...
Insights & Research
The Engine Room
From Silicon to Strategy. The latest thinking from the frontlines of building AI.
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The Agent Billing Crisis: When Autonomous Agents Break Your Cloud Cost Model
Agents with cloud credentials execute thousands of API calls per minute. Standard billing guardrails built for human interaction break entirely. Here is how to architect cost governance at machine speed.
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Open Source AI Is the Infrastructure Play Boards Finally Get
Boards are waking up to what engineering teams have known for eighteen months: open-source AI models drive better margins than proprietary APIs. This is not about ideology. It is about unit economics...
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.
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 Infrastructure
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AirLLM and the GPU Accessibility Trap
AirLLM runs 70B models on a 4GB GPU. The trick is not the sharding; it is that enterprise inference workloads break the edge cost model. Here is why GPU accessibility is not the same thing as cost...
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...
Agentic AI
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Codebase-Aware Agentic AI: Why MCP + Persistent Indexing Is the Next Layer
code-review-graph at 20K stars. AI coding agents are moving past generic code search. The next layer is persistent structural indexing through MCP — how agents go from helpful junior to autonomous...
Browser Agents Are The Unsexy Infrastructure Win of 2026
Everyone is writing about agent frameworks and protocol standards. Nobody is talking about the real breakthrough: agents that can actually use a web browser. Eight thousand GitHub stars in weeks...
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...
AI Engineering
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Structured Agent Memory vs Vector Search
Pinecone's Nexus Engine compiles business context into structured knowledge graphs for agents. Nemotron 3 Embed tops RTEB. Vector search alone is now insufficient. Here is the memory architecture...
The Agent Testing Revolution: Why QA Is The Next Infrastructure Layer
Agent testing infrastructure separates prototype agents from production systems. The Strix framework signals a structural shift.
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...
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...
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