# JAKA - **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon) - **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST) - **Where:** Online - **Team:** [Kelvin Abubakar](https://cerebralvalley.ai/u/JakaTheGreat) - **GitHub:** https://github.com/keladeza0072-lgtm/silkweb - **Demo video:** https://youtube.com/shorts/8Ky_I-_rc2Y?si=m79GgHAnkNPV-9tx - **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery - **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1291 SilkWeb: Making AI More Capable Through Structured Intelligence AI models are becoming increasingly capable, yet much of their potential is lost through inefficient context management, repeated reasoning, fragmented knowledge, and unnecessary token consumption. Today, AI agents often process large amounts of information to find a small amount of relevant knowledge. They repeatedly rediscover instructions, documentation, and solutions that could have been organized and reused. This increases execution time, inference costs, and the likelihood of inconsistent results. SilkWeb addresses this problem by building a reusable intelligence infrastructure that helps AI systems access the right knowledge at the right time. Instead of making models larger or retraining them, SilkWeb focuses on making their existing intelligence more effective. Through structured knowledge registries, task-specific retrieval, reusable instructions, and agent-accessible tools, SilkWeb enables AI systems to work with relevant information rather than repeatedly processing unnecessary context. Four Applications, One Infrastructure: We developed and evaluated SilkWeb across four application areas to demonstrate its potential beyond a single industry or workflow. 1. SilkWeb Code: Intelligence for AI Coding Agents SilkWeb Code provides coding agents with access to structured, reusable project knowledge through a Git-backed registry and Model Context Protocol (MCP) integration. Instead of repeatedly exploring documentation and project context, agents can retrieve relevant knowledge to complete development tasks more efficiently. In our controlled RES-002 benchmark using Meta Muse Spark 1.3, SilkWeb achieved: - 15.20% reduction in coding time - 56.28% reduction in uncached input tokens - 29.84% reduction in output tokens - Quality improvement from 98.5/100 to 100/100 These measurements represent one paired benchmark trial. 2. SilkWeb Agents: Reusable Intelligence for AI Agents SilkWeb Agents explores how reusable, structured intelligence can improve autonomous agent workflows by reducing repeated context processing and improving access to task-relevant information. Across nine trials in our AGENTS-002 benchmark, SilkWeb achieved: - 16.43% reduction in execution time - 76.58% reduction in uncached input tokens - 48.51% reduction in output tokens These results demonstrate the potential of structured intelligence to improve agent efficiency without modifying the underlying foundation model. 3. SilkWeb Data: Targeted Knowledge for Accurate AI Reasoning SilkWeb Data investigates how providing relevant documentation and structured knowledge, rather than entire information collections, affects AI task performance. In our REV-004 evaluation, providing selected documentation achieved 5/5 correct results, matching the accuracy obtained with full documentation while reducing execution time by 27.85%. This demonstrates how targeted information retrieval can preserve accuracy while reducing unnecessary processing. 4. SilkWeb Creative: Intelligence for AI-Powered Creative Production SilkWeb Creative demonstrates how structured intelligence can extend beyond technical workflows into practical creative applications. We built a working AI advertising application powered by Meta Muse Spark 1.3 and Muse Image 1.0. Users simply upload a product image and select their campaign objective. SilkWeb applies structured creative knowledge and advertising principles to guide the generation process, producing three distinct image advertisements, each with accompanying advertising copy. The application handles creative strategy internally, allowing users to focus on their products and marketing goals rather than complex prompting or design decisions. What Makes SilkWeb Different? SilkWeb is not another foundation model, chatbot, or application-specific prompt library. It is an infrastructure approach designed to make useful intelligence reusable across models, agents, and applications. Its core principle is that AI performance depends not only on model capability, but also on how effectively information is organized, retrieved, and applied. By separating reusable knowledge from repeated model reasoning, SilkWeb aims to improve execution efficiency, reduce unnecessary token consumption, and maintain or improve output quality. Our experiments demonstrate these benefits across coding, agent workflows, and data retrieval, while our live Creative Studio shows how the approach can power a practical, user-facing product. Live Demonstration: Website: https://silkweb-us-inference-test.onrender.com/ The live Creative Studio uses Meta's Muse models to generate advertising assets. To protect the project's limited inference credits, live generation requires a demo access code. Access code: 519911085 Please use this code to test the image advertising workflow. Generation requests are subject to a server-session limit to manage inference usage. Our Vision: I believe the next major improvements in AI will come not only from building more powerful models, but also from improving the infrastructure through which models access and reuse intelligence. SilkWeb is an exploration of that future. 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