#aiinfrastructure

I built inference engines by hand and they run Qwen well. Last week I cut them from my list of flagships, because hosting someone else's model proves

Agent-based LLM workflows move beyond single-shot prompting by giving models the ability to reason, plan, and use tools across multiple turns. Instead

DeepSeek R1 is a 671 billion parameter Mixture-of-Experts reasoning model developed by DeepSeek for complex coding, mathematics, and agentic workflows

LLMs are becoming core infrastructure for modern game development, not just dialogue trees. From persistent NPCs with long-term memory to procedural q

Manufacturing data is inherently messy, verbose, and time-sensitive. Maintenance logs span years, supply chain documents pile across multilingual form

Serverless platforms are the default glue for modern audio pipelines. A cloud function triggered by an uploaded recording can handle authentication...

Developers building production vision pipelines face a predictable bottleneck. Every image passed to a large language model incurs significant token o

Natural language generation at production scale is as much an infrastructure problem as a modeling problem. When applications stream completions to us

Deploying large language models on edge devices requires trading off model capacity against thermal limits, memory bandwidth, and strict latency budge

Analyzing code with large language models has moved beyond simple autocompletion. Modern pipelines ingest entire repositories, trace cross-file depend

Running large language models in production requires more than a GPU. Kubernetes has become the default orchestration layer for teams that need autosc

Translation and localization at scale require more than dictionary matching. Large language models capture nuance, register, and cultural context, but