Join Google Developers Group at 1pm EST today! The CEO's Guide to System Design Agent = Model + Harness. The model is a raw engine. The harness: context, tools and feedback loops, is the product. Daniel Flügger Stephen S. Ruchi K. Deepa Subramanian https://lnkd.in/g76v453W
Google Developer Groups - Providence
Technology, Information and Internet
Providence, Rhode Island 53 followers
A technology-based community in Providence, RI focused on cloud and data technologies and organized by volunteers.
About us
We are a technology-based community in the Providence, RI area focused on cloud and data technologies and are organized entirely by volunteers. Our members include developers of all skill levels who come together for monthly events discussing cloud and data topics, including AI, ML, Serverless and related areas of interest. All are welcome.
- Website
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https://gdg.community.dev/gdg-providence
External link for Google Developer Groups - Providence
- Industry
- Technology, Information and Internet
- Company size
- 2-10 employees
- Headquarters
- Providence, Rhode Island
- Type
- Nonprofit
- Founded
- 2024
- Specialties
- cloud, artificial intelligence, design, machine learning, and data
Locations
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Primary
Get directions
Providence, Rhode Island 02903, US
Employees at Google Developer Groups - Providence
Updates
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RSVP for an introductory session on AI Studio: https://lnkd.in/ef3SrpBC
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Building production context engineering: An MCP server case study Hope our GDG Community is having a productive week! Today I implemented Anthropic's context engineering framework as a working MCP server. Key insight: code execution isn't just about running scripts—it's about compacting knowledge into reusable primitives. Architecture highlights: - 4-module design: Patterns, Artifacts, Memory, Metrics - Progressive loading: search (100-500 tokens) → load (500-1K) → execute (50-200) - Quality tracking: effectiveness scores, reuse counts, token savings - Validation layer: prevents SQL injection, validates inputs Measured results: - 98.7% token reduction (150K → 2K) - <100ms execution time - 90% token savings per skill invocation The RLS policy generator alone saves 450 tokens per use. At 100 policies, that's 45,000 tokens saved—enabling longer sessions and better context retention. This proves that executable skills can be sub-agent primitives. Next step: workflow composition (chaining skills for complex operations). #Repo: https://lnkd.in/eXM-ANbp Happy to discuss implementation details or share lessons learned. What's your approach to context optimization? #MachineLearning #AI #SystemsDesign #MCP #GDG
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Here is the link for today's call: https://lnkd.in/eh5zgqaF