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This article explains how LinkedIn improved the response time of its Hiring Assistant AI by implementing speculative decoding. The technique allows the model to draft and verify multiple tokens simultaneously, significantly reducing latency while maintaining output quality.
This article critiques the performance of LLM memory systems like Mem0 and Zep, revealing they are significantly less efficient and accurate than traditional methods. The author highlights the architectural flaws that lead to high costs and latency, arguing that these systems are misaligned with their intended use cases.