Papers for operating decisions that need evidence.
The paper archive collects the methodology, data, and conclusions behind CREATE SOMETHING research so a pattern can move from observation into implementation with a trail.
33 papers / methodology, data, and conclusions you can verifypublished papers
research categories
available to inspect
database / automation / judgment frame
Webflow Analyzer Productization
How CREATE SOMETHING translated reviewer-side analyzer infrastructure into creator-facing validation, autofill, screenshot packaging, and submission UX without collapsing trust boundaries.
The Analyzer MCP: A Policy-Grounded Review Architecture
How CREATE SOMETHING turned Webflow template review into a multi-surface MCP system that joins Designer state, published-site evidence, policy ingestion, and governed review output.
Composio in the MCP Delivery System
A decision-grade analysis of why Composio is included for commodity connectivity, how the wrap pattern protects brand and margin, and how delivery remains aligned to Database, Automation, and Judgment control boundaries.
The Wrap Pattern: Commodity Integration as Invisible Infrastructure
A structural pattern for integrating commodity MCP vendors as invisible infrastructure while preserving the client-facing surface, the Intelligence Layer margin, and the Three-Tier alignment.
The Webflow Way, Automated
A case study on exposing Webflow Way QA signals to agents from a published template preview, aligned to WebMCP-style in-browser tools.
Open-Weight Models in Client MCP Work
Guidance for consultancies building MCP integrations: how to choose between OpenAI open-weight models (gpt-oss-20b/120b, gpt-oss-safeguard) and hosted models, with concrete patterns for education, production, and compliance.
The Three-Tier Framework: Database, Rules, Policy
A hierarchical ontology identifying three tiers connected by typed Artifacts and spanning four cross-cutting concerns, with MCP as natural encapsulation.
The Andon Protocol
AI-native structured escalation for agent harnesses and multi-agent systems. v3.1 adds Silent Running Detection, cost-parameter defaults and worked examples, Resolution Surface design for batch review, and a three-phase implementation plan. The canonical boundary between Automation and Judgment in the Three-Tier Framework.
Ground: Verification-First Code Analysis
Case study: How Ground saved 8+ hours analyzing an 80+ package monorepo by preventing AI hallucination in code analysis.
Tufte for Mobile: Design Intent Across Screen Sizes
A methodology demonstrating how wireframe intent survives responsive transformation through five Tufte principles: data-ink ratio, sparklines, direct labeling, information density, and small multiples.
Ground: Evidence-Based Claims for AI Code Analysis
A tool that blocks AI agents from claiming code is dead, duplicated, or orphaned without first computing the evidence. Now with AI-native features: batch analysis, incremental diff mode, structured fix output, and fix verification. Rated 10/10 by agent testing across two production codebases.
Recursive Language Models: Context as Environment Variable
This paper documents the implementation and empirical validation of Recursive Language Models (RLMs) based on MIT CSAIL research. We identified critical bugs, validated the pattern against the original repository, and demonstrated practical application for codebase analysis—processing 157K characters to find 165+ DRY violations.