The AiEdge Newsletter
“The AiEdge Newsletter” provides insights into cutting-edge AI applications at the intersection of edge computing and artificial intelligence. It delves into topics like data augmentation for machine learning, Python programming for web3 and blockchain, and the importance of short-term memory in AI applications. The newsletter explores the challenges of enterprise RAG implementations and the need for intelligent systems that go beyond simple data retrieval. With a focus on practical tools and techniques, it offers a comprehensive guide to leveraging AI technologies for real-world applications. Stay informed about the latest advancements in AI and edge computing with “The AiEdge Newsletter.”
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How the KV Cache Shapes AI Agent Costs
Why shrinking the cache, reusing a prefix, and keeping it nearby save different resources.
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How Hybrid LLMs Combine Memory, Attention, and Experts
Follow Mamba-2, Gated DeltaNet and KDA from state updates to the costs that active parameter counts miss.
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How Persistent Agents Keep Working
Reconstruct a coding task’s context, preserve accepted work, and recover safely when a worker disappears.
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How Self-Improving Agents Learn from Experience
How executable tasks and adaptive curricula turn production failures into useful practice.
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How DeepSeek V4.1 Flash Separates Reading and Generation
How a shorter input path, shared attention memory, and selective reconstruction change the cost of long conversations.
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Build a Jev-Supervised AI Agent
Typed decisions, guarded tools, and a measured comparison with LLM supervisors
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How Jev Turns Questions Into Decisions
Inside the state, typed-output, probability, and application-policy contracts of a decision model
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The Complete Guide to Evaluation Pipelines for AI Agents
From production traces and isolated trials to evidence you can use for release decisions
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How AI Agent Memory Updates a Changed Preference
Follow one correction from a user message to the active fact a future task retrieves.
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How LLM Routers Balance Cache Reuse and Queue Time
Four routing strategies, one repeated prompt, and the point where a cold replica wins
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How Test-Time Scaling Allocates LLM Reasoning Across Depth, Width, and Feedback
Why the same inference budget can finish one path, explore alternatives, or learn from a failure
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How Constrained Decoding Makes LLM Outputs Follow a Schema
The decoder can guarantee the shape of a tool call while leaving its values completely wrong.
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