blog: Introduce blog post on long context models for code AI #7266
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* Adds a new blog post exploring the benefits and tradeoffs of using long context window models in AI coding assistants like Cody * Includes evaluation results comparing Cody's performance with 10k vs 1M token context windows * Discusses the layered context architecture used to balance latency and coverage * Outlines the road ahead for long context models in enterprise codebases Co-authored-by: Beyang Liu <beyang@sourcegraph.com>
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* Adds more details on the performance evaluation of Cody with 10k vs 1M token context windows * Includes new examples showcasing the benefits of long context for code understanding tasks * Discusses the challenges and tradeoffs of implementing nearest neighbor search over code embeddings * Outlines the integration of the Cody context API within the Sourcegraph codebase Co-authored-by: Beyang Liu <beyang@sourcegraph.com>
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