Learn crypto-native AI
Practical guides, product notes, and technical deep dives for building with NeuronGate.
11 articles
How NeuronGate Routes Every Request
A technical walkthrough of the NeuronGate proxy architecture, from balance reservation to upstream dispatch to settlement.
Open-Weight Models Are Forcing Better Routing Decisions
The return of serious open-weight releases in 2025 changed the build-versus-buy conversation for AI product teams.
The EU AI Act Code of Practice Is an API Problem Too
As GPAI guidance matured in Europe, the operational burden moved closer to logs, documentation, provider choice, and traceability.
AI Browsers Turn Search Into an API Workload
The rise of agentic browsers made one trend obvious: search, browsing, and summarization are becoming chained model workloads.
Apple's On-Device AI Push Changes User Expectations
Apple's developer story around local and private AI made users more aware of where inference happens and why product teams need clearer boundaries.
Claude 4 and the Return of Enterprise Model Policy
Claude 4's release sharpened the case for model policy: stronger models are valuable, but production teams still need controls around where they run.
After Google I/O, Latency Became a Product Feature
Google I/O pushed multimodal and fast Gemini models forward, but the practical takeaway for API teams is simple: latency is now part of model selection.
LlamaCon Proved Open Models Need Product Infrastructure
Meta's Llama ecosystem momentum showed that open models are not only research artifacts. They need the same product infrastructure as closed APIs.
Blackwell Supply Is Already Showing Up in API Strategy
As new GPU capacity comes online, AI teams are rethinking pricing, latency, and how much provider flexibility they need.
The Agents SDK Era Needs Better API Boundaries
As agent frameworks become mainstream, clean routing, spend controls, and model policy matter more than any single prompt pattern.
DeepSeek R1 Made Reasoning Feel Like Infrastructure
After the January R1 release, teams started treating reasoning models less like demos and more like production infrastructure decisions.