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(essay) Life At The Edge "Local AI" today is mostly about giving models OS-level access so that more files and context can be transferred to the cloud for inference. But intelligence is about to diffuse to the edge just as computing did in the 80s and 90s Some thoughts on rent vs own for inference, Apple events becoming great again, God models, and the coming dance of edge and cloud
This has a linked resource worth reading, a tool worth trying, or an idea worth prototyping
Quick Insight
The author argues that "local AI" is currently just a fancy way to send more data to cloud models, but true edge computing for AI inference is coming soon. This matters for Brian because it could fundamentally change how he integrates AI into his fintech platform and side projects - potentially reducing API costs and latency while improving privacy.
Actionable Takeaway
Test current local AI tools (like Ollama or local LLMs) for a specific use case in one of his side projects to understand the current state vs. his cloud AI workflows. This will help him evaluate when the shift becomes worth making.
Related to Your Work
For Brian's fintech platform, edge AI could enable real-time transaction analysis or fraud detection without sending sensitive financial data to external APIs. His webhook integrations and analytics dashboards could benefit from reduced latency and improved data privacy if inference moves local.
Source Worth Reading
This appears to reference a longer essay. The full piece would likely contain more specific technical details about the edge/cloud transition timeline and implementation strategies that would be valuable for planning Brian's AI integrations.