Our Projects
Explore our collection of open-source AI projects designed to transform various domains through ethical and effective artificial intelligence solutions.
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AI Skunkworks
A distinctive experiential learning community at Northeastern University that bridges theoretical knowledge and practical application in artificial intelligence. Through our "Learn AI by Doing AI" philosophy, students develop real-world AI expertise while building impressive portfolios before graduation.
About the Project
AI Skunkworks at Northeastern University is a distinctive experiential learning community that bridges theoretical knowledge and practical application in artificial intelligence. Unlike Humanitarians AI which works with graduates, AI Skunkworks enables current students to "Code for a Cause" and "Learn AI by Doing AI" through extracurricular and summer work on real-world projects.
Key Features
Learning by Doing
Students gain expertise by working on authentic AI projects with real-world applications rather than just theoretical exercises.
Trust but Verify
We encourage critical thinking and rigorous validation of AI solutions through systematic testing and evaluation.
Try It
We create a safe space for experimentation, innovation, and productive failure where students can learn from both successes and mistakes.
Cross-Disciplinary Integration
We bring together students from Computer Science, Arts, Business, Life Sciences, and Humanities to tackle meaningful challenges across domains.
Botspeak
Empowering individuals through the transformative power of AI fluency, fostering effective human-AI collaboration. An innovative program that combines theoretical frameworks with practical skills to enhance your ability to work with AI systems.
About the Project
Botspeak is a comprehensive AI fluency framework that transforms you from an AI tourist into a fluent native. Through our YouTube series, books, and articles, we cultivate your ability to think alongside AI, distributing cognitive tasks strategically, communicating intentions clearly, evaluating outputs critically, and using these powerful tools responsibly.
Key Features
Strategic Delegation
Thoughtfully distributing tasks between human and AI based on comparative strengths.
Effective Communication
Crafting precise prompts with clear intent and context for optimal AI response.
Critical Evaluation
Systematically assessing AI outputs for accuracy and bias through multiple sources.
Ethical Reasoning
Maintaining accountability and managing privacy boundaries in AI collaboration.
Dayhoff
An open-source, agent-based AI bioinformatics framework for computational biology, epidemiology, and public health. Named after pioneering scientist Margaret Belle Dayhoff, this framework orchestrates specialized AI agents to deliver cohesive, data-driven solutions for biological and health sciences.
About the Project
The Dayhoff framework (inspired by Margaret Belle Dayhoff, the pioneer in bioinformatics) is an open-source platform for AI-powered biological and public health intelligence. This experimental project creates an ecosystem of specialized agents orchestrated by a central coordination layer to tackle complex challenges in biology, epidemiology, and public health.
Key Features
Genomic Analysis Agents
Process and interpret genetic sequences, identify patterns, and predict structural and functional properties of biomolecules.
Epidemiological Agents
Track disease spread, analyze transmission patterns, and model potential interventions for public health crises.
Clinical Intelligence Agents
Analyze medical literature, patient data, and treatment outcomes to identify effective health interventions.
Molecular Modeling Agents
Simulate protein folding, drug interactions, and molecular dynamics to accelerate discovery processes.
Dewey
An open-source, agent-based AI educational framework designed to transform how we learn and teach. Named after educational philosopher John Dewey, this framework orchestrates specialized AI agents to deliver personalized, interactive educational experiences across disciplines.
About the Project
The Dewey framework is an open-source platform for AI-powered educational intelligence. This experimental project creates an ecosystem of specialized educational agents orchestrated by a central coordination layer to transform learning experiences for students, educators, and professionals. Led by Professor Nik Bear Brown, PhD, MBA, this educational experiment emphasizes learning through building, inviting contributors to discover effective approaches through practical implementation.
Key Features
Content Generation Agents
Create personalized learning materials, intelligent AI books, and dynamic educational content tailored to individual learning styles and needs.
Tutorial Agents
Guide learners through complex topics with step-by-step explanations, Socratic questioning, and adaptive scaffolding techniques.
Assessment Agents
Analyze learner performance, provide formative feedback, and adapt content difficulty based on demonstrated understanding.
Simulation Agents
Create interactive learning environments, algorithm visualizations, and scientific simulations for hands-on experiential learning.
Lyrical Literacy
Empowering youth through the transformative power of music and poetry, fostering creativity and self-expression. An innovative program that combines AI with creative writing and music to enhance literacy skills.
About the Project
Lyrical Literacy is a pioneering initiative from Humanitarians AI that harnesses artificial intelligence to enhance cognitive and language development through music. This research-based program explores the powerful intersection of neuroscience, music, and education to create personalized learning experiences for diverse populations.
Key Features
AI-Generated Musical Content
Custom-created songs tailored to individual learning objectives, cultural contexts, and developmental needs that leverage the power of music to enhance memory formation and retention.
Creative Storytelling
Interactive storybooks with accompanying musical elements and audiobooks that incorporate rhythmic and melodic components, supporting both literacy development and creative expression.
Personalized Learning
AI algorithms that adapt content based on learning progress, preferences, and specific developmental goals, creating customized learning paths for each individual.
Music Integration
Materials designed for diverse populations, including those with learning differences, language barriers, or neurodevelopmental conditions, making education more accessible and engaging for all learners.
Madison
An open-source, agent-based AI marketing intelligence framework designed to transform branding, marketing, and advertising. Madison organizes specialized AI agents that collaborate under an orchestration layer to deliver cohesive, data-driven marketing solutions.
About the Project
Madison (informally known as "Mads and Madison") is an open-source, agent-based AI marketing intelligence framework that integrates specialized AI agents to deliver comprehensive marketing solutions. The framework maps projects to agent layers, details core technologies, and outlines implementation strategies for data-driven marketing. This experimental project emphasizes collaborative learning and innovation, allowing marketers to build tailored solutions for branding, customer experience, content creation, and market research through an integrated agent ecosystem.
Key Features
Intelligence Agents
Gather and analyze data to provide actionable insights into market dynamics and consumer sentiment through reputation monitoring and trend analysis.
Content Agents
Create, optimize, and distribute marketing materials across channels with consistent brand voice and multi-platform adaptation.
Research Agents
Process data to uncover customer insights through automated survey analysis and synthetic persona development.
Experience Agents
Enhance customer interactions through AI concierge systems and customer journey transformation.
Mycroft
An open source educational experiment in AI-powered investment intelligence. Named after Sherlock Holmes's enigmatic elder brother, the Mycroft framework explores how a ecosystem of specialized agents might work in concert to analyze the AI sector while implementing disciplined investment strategies.
About the Project
The Mycroft framework represents an educational experiment in AI-powered investment intelligence. With its tagline "Using AI to Invest in AI," this open-source platform explores how a ecosystem of specialized agents might work in concert to analyze the rapidly evolving artificial intelligence sector while implementing disciplined investment strategies.
Key Features
Analytical Agents
Gathering, processing, and interpreting vast amounts of information about AI companies and their competitive landscape.
Portfolio Agents
Testing approaches to transform knowledge into actionable investment strategies with proper diversification and risk management.
Advisory Agents
Exploring human-AI interaction through conversational financial advising, goal-setting, and educational components.
Intelligence Agents
Maintaining market awareness by monitoring news, social sentiment, financial reports, and regulatory developments.
Northeastern & Humanitarians AI
"Learn AI by Doing AI" through our collaborative "Code for a Cause" projects where Northeastern University students and Humanitarians AI build impactful AI applications across education, research, and community service.
About the Project
Northeastern University and Humanitarians AI have joined forces to create a series of innovative "Code for a Cause" projects where current students and recent graduates gain hands-on experience building AI applications that make a real difference in education and society.
Key Features
Ada: Calculus Learning Assistant
Provides step-by-step guidance, scaffolding, and Socratic questioning to help students understand complex calculus concepts without giving direct answers.
Newton: Interactive Physics Tutor
Enhances learning with dynamic graphs and interactive simulations, providing students with visual and hands-on ways to explore complex physics concepts.
Grace: Algorithms Visualization Tool
Simulates algorithms and incorporates interactive visualizations to help students grasp abstract algorithmic concepts more effectively.
CatBot: Adoption Assistant
Helps cats find forever homes by streamlining the adoption process, matching potential adopters with suitable cats, and promoting special needs adoption.
Popper Framework
An open-source framework for computational skepticism and AI validation. Named after philosopher Karl Popper, this framework embraces the scientific method by rigorously examining evidence both for and against AI systems.
About the Project
The Popper framework is an open-source platform for systematic AI validation and computational skepticism. Rather than claiming models are "correct," Popper establishes a systematic approach to identifying strengths, weaknesses, biases, and inconsistencies in AI systems through an ecosystem of specialized validation agents orchestrated by a central verification layer.
Key Features
Balanced Evidence Assessment
Methodically gather and evaluate evidence both supporting and challenging AI systems.
Conjecture and Refutation
Propose potential strengths and weaknesses, then test them systematically.
Critical Rationalism
Subject all claims to rigorous scrutiny, regardless of source or confidence.
Scientific Skepticism
Embrace doubt as the path to reliable knowledge while recognizing corroborating evidence.
The RAMAN Effect
An open-source platform revolutionizing public health through AI-enhanced Wastewater-Based Epidemiology (WBE) using Surface-Enhanced Raman Spectroscopy (SERS). This project develops sophisticated AI software capable of analyzing large volumes of spectral data to detect pathogens and pollutants with unprecedented accuracy.
About the Project
The RAMAN Effect project leverages Surface-Enhanced Raman Spectroscopy (SERS) combined with artificial intelligence to revolutionize wastewater-based epidemiology and public health monitoring. Named after Sir C. V. Raman, who discovered the Raman scattering phenomenon in 1928, this initiative aims to develop cutting-edge AI software capable of analyzing complex spectral data from wastewater samples to detect pathogens, pollutants, and substances of concern with unprecedented sensitivity and specificity.
Key Features
Spectral Data Processing
Advanced algorithms for cleaning, normalizing, and extracting features from raw Raman spectral data obtained from wastewater samples.
Pattern Recognition
Deep learning models specifically trained to identify characteristic spectral signatures of pathogens, including viruses, bacteria, and emerging variants.
Multi-Analyte Detection
Systems capable of simultaneously identifying multiple substances in complex wastewater matrices, from pharmaceuticals to pollutants and pathogens.
Real-time Monitoring
Infrastructure for continuous sampling and analysis, providing near-real-time insights into community health and environmental conditions.