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Building a Fair Benchmark for AI Agent Memory Systems

Everyone is building AI memory systems. But how do we know which ones actually work? As AI agents move from one-off interactions toward long-term collaboration, memory is becoming a core capability. Yet evaluating memory systems fairly is surprisingly difficult. Different systems often use different datasets, answer models, prompts, and evaluation methods. When the final score changes, it can be…

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The Browsing LLM's Frustrating Limitations

直接在瀏覽器跑 LLM,既兼顧隱私又不用複雜的GPU設定,太完美了吧? 從 WebLLM 到 Transformers.js,前端社群有一群反骨仔吹起一股「邊緣 LLM」的熱潮,可是,當真正將模型落地到使用者的瀏覽器時,第一個面對的考驗就是,WebGPU 真的有比 WASM 快嗎? 這陣子實測的結論比想像中更加戲劇化, 500M 以下的微型模型,WASM 反而快了 12%,但是對於 3B…

  • Browser-based LLMs face performance limits
  • WASM outperforms WebGL/WebGPU for models <500 million parameters
  • Chrome crashes with models >3 billion parameters due to lack of physical head

The Agentic Economy Needs a Market for Work

Most AI agents still live inside a chat window. They can write code, search for information, call an API, or prepare a document, but they usually stop when the task leaves the boundaries of their own…

  • Agents can hire other agents or pay humans for tasks beyond their capabilities
  • Agentic economy enables marketplace for task collaboration and payment
  • Smart contracts ensure immutable financial transactions and rules

Learn Provider-Agnostic Model Routing by Building a Tiny LLM Switchboard

Every few weeks a new model drops and my study group chat fills up with screenshots: "this one is cheaper," "this one is better at code," "switch now." I can never verify any of it quickly, because my…

  • Python script enables provider-agnostic routing between multiple LLM providers
  • Switchboard routes tasks to different backends based on task type (summarize, code, chat)
  • Script adds single entry to adapt to new models, maintains evaluation criteria