Khaled is a research engineer interested in distributed algorithms and machine learning models.
He began working in academia after his PhD on distributed algorithms and moved to designing and serving R&D projects in the industry around Machine Learning models. He worked across different sectors ranging from Finance to Manufacturing.
Today at Edgee, he works on the intersection of distributed systems and AI.
Compressor V2 is the combination of three independent compression strategies: brevity, tool surface reduction and tool result trimming. Each targets a different layer of an agent's request. This post measures end-to-end the gains of this new composed strategy, on real coding and tool-use workloads with paired statistical tests. The results show a combined 50% per-task cost reduction.
A first in a series overviewing token compression techniques, the way to evaluate them and the main challenges they face.
Discover how edge computing can speed up AI inference. Learn how offloading tokenization and RAG to the edge improves latency, reduces costs, and enhances user experience.
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