HiP1/Divided-Focus

Divided-Focus proposes an architectural shift in how LLMs handle context, moving away from a flat instruction/data buffer toward a 'Harvard Architecture' style separation. While the concept is intellectually robust—addressing prompt injection through structural isolation rather than just filtering—the project currently lacks any quantitative traction (0 stars, 0 forks) and is positioned more as a paper/reference than a production-ready tool. Frontier labs like OpenAI and Anthropic are already moving toward multi-layered context management (e.g., privileged system prompts vs. untrusted user data) and will likely implement similar 'default-deny' logic natively at the inference level. The lack of an established code base or developer community makes this project highly susceptible to being superseded by official platform updates or more established security middleware like Lakera or Giskard. Its primary value currently is as a reference implementation for theoretical research rather than a defensible software product.