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Developer documentation, guides, and reference materials.
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Boyle System adaptive instructional architecture — five learning modes and multi-armed bandit engine
Technical explanation for engineers, learning scientists, and program designers implementing the Boyle System
multi-armed bandit
adaptive learning
instructional design
Thompson Sampling
GAMBITTS
scaffolding
Socratic questioning
cognitive load theory
Zone of Proximal Development
Boyle System
NotebookLM pedagogy
AI tutoring
CMAB
contextual bandit
NotebookLM corpus management guide — source ingestion, Ouroboros technique, and notebook segmentation
Operational reference for researchers and team leads managing NotebookLM corpora within the Boyle System. Covers source format performance, the Ouroboros note-to-source conversion technique, source stitching strategies, and notebook segmentation taxonomy. After reading, you can structure and maintain a NotebookLM corpus that maximizes retrieval quality and stays within platform limits.
NotebookLM
corpus management
RAG
source ingestion
Ouroboros technique
source stitching
notebook segmentation
retrieval quality
Boyle System
document management
50-source limit
citation preservation
Boyle System deployment guide — cross-system analysis, operations, integrations, and security
Operational reference for institutional leads, IT administrators, and implementation teams deploying the Boyle System. Covers RAG vs. long-context tradeoffs, active pilot metrics, integration methods and stability, security data classifications by context, and target deployment scenarios. After reading, you can plan a technically sound deployment and assess data classification requirements for your institution.
Boyle System deployment
NotebookLM enterprise
RAG vs long-context
data governance
security classification
API integration
HIPAA
pilot metrics
research infrastructure
Humanitarians AI
MCP server
Python SDK
Discovery Engine
The Boyle System
Technical reference for the Boyle System — AI-assisted documentary infrastructure for scientific reproducibility. Covers MVAL protocol, corpus management, adaptive instructional design, and implementation roadmap for research labs, graduate programs, think tanks, and executive education.
reproducibility
MVAL
NotebookLM
RAG
adaptive learning
research documentation
AI education
multi-armed bandit
Humanitarians AI
Bear Brown
MVAL protocol reference — Minimum Viable Analytical Log field specifications
Field-level reference for researchers, fellows, and team leads using the Boyle System. Defines all six MVAL fields — What, Why, How, Environment, Results, Questions — with required content standards, sufficient vs. insufficient entry examples, and the failure artifact protocol. After reading, you can write a complete, compliant MVAL entry for any experiment or analytical session.
MVAL
Minimum Viable Analytical Log
research logging
experiment documentation
reproducibility
failure artifact
research protocol
Boyle System
Humanitarians AI
analytical log
documentation standard
Boyle System roadmap — prioritized improvements, open questions, and feature pipeline
Planning reference for institutional partners, program directors, and the Boyle System development team. Documents critical and high-priority action items with effort estimates, open architectural questions requiring partner input, and the full feature roadmap. After reading, partners can identify which open questions require their input and track the development trajectory of the system.
Boyle System roadmap
MVAL enforcement
Ouroboros citation preservation
MAB deployment
feature roadmap
open questions
technical debt
institutional deployment
Bear Brown
Humanitarians AI
CRITIQ integration
GAMBITTS
Boyle System overview — AI documentary infrastructure for scientific reproducibility
Explanation document for research leads, institutional partners, and program evaluators assessing the Boyle System. Covers the reproducibility problem, NotebookLM RAG architecture, and the three-role AI partnership model. After reading, you can evaluate whether the Boyle System addresses your institution
Boyle System
scientific reproducibility
AI research documentation
NotebookLM
retrieval-augmented generation
vanishing laboratory
institutional knowledge
research infrastructure
Humanitarians AI
Bear Brown
MVAL
Medhavi lexicon pipeline — technical specification v0.2
Revised technical specification for the Medhavi intelligent textbook lexicon system. Defines two distinct pipelines: Wikipedia keyword detection for pop-up generation, and NanoLex fine-tuning for lexical entry extension. Written for Thejus Thomson, Prof. Brown, and Dhruv.
Medhavi
NanoLex
lexicon pipeline
Wikipedia keyword detection
MDX pop-up
fine-tuning
Llama
Mistral
Discovery cluster
nanomedicine
intelligent textbook
system prompt
Medhavy Platform — White Label Architecture
Technical specification, pedagogical framework, and business model for deploying the Medhavy adaptive learning platform under institutional branding. Covers multi-tenant architecture, five learning approaches, content pipeline, and white label implementation roadmap for business schools, think tanks, K-12 networks, and professional associations.
white label
adaptive learning
Medhavy
multi-tenant
pedagogy
bandit architecture
edtech
executive education
content pipeline
Bear Brown
Humanitarians AI
Medhavy pedagogy architecture — why the adaptive system works the way it does
Explanation of the pedagogical architecture underlying the Medhavy adaptive learning platform. Covers the five learning approaches, the multi-armed bandit selection mechanism, why TEXTBOOK_ONLY is a pedagogical decision and not just a safety constraint, and what content must contain to support each approach. For developers and content leads who need to understand the system before building for it or extending it.
Medhavy
pedagogy
adaptive learning
multi-armed bandit
direct instruction
Socratic method
case-based learning
spaced retrieval
project-based learning
TEXTBOOK_ONLY
edtech architecture
Paul-Elder
Diátaxis
bearbrown
Humanitarians AI
Medhavy white label architecture — system reference
Technical reference for developers working on the Medhavy adaptive learning platform
Medhavy
white label
multi-tenant
tenant registry
Orama
Next.js
Clerk
Supabase
bandit architecture
pedagogy selector
analytics
edtech
bearbrown
Humanitarians AI
developer reference
Medhavy white label deployment — how to onboard a new institutional client
Step-by-step deployment guide for developers onboarding a new institutional client to the Medhavy white label platform. Covers tenant registry setup, credential configuration, theme injection, persona calibration, Orama index provisioning, content pipeline handoff, and launch verification. After completing this guide, a developer can bring a new white label deployment from zero to live without assistance.
Medhavy
white label deployment
tenant registry
Orama setup
persona calibration
content pipeline
edtech deployment
Clerk organization
Supabase analytics
developer guide
bearbrown
Humanitarians AI