OctoMind

AI dashboard that unites tasks, emotion, and wellness to give smart insights and balanced productivity.
Performs sentiment analysis to detect mood, give feedback, and suggest ways to improve focus.
Gamified wellness hub that tracks streaks, goals, and rewards progress toward sustainable balance.

OctoMind – The AI Multitasking Assistant

Inspiration

The idea for OctoMind came from the multitasking nature of an octopus — agile, intelligent, and capable of managing several things at once.We realized that modern life mirrors this: meetings, deadlines, personal health, and constant notifications competing for attention.

Our inspiration was to create an AI assistant that thinks like an octopus — intelligent, adaptive, and balanced — helping users manage their workflows without burnout.

How We Built It

We built OctoMind as an AI-driven productivity hub that intelligently prioritizes daily tasks based on urgency, deadlines, and user mood.

Tech Stack:

Frontend: React.js for interactive UI and visual dashboards
Backend: Node.js with Express for REST APIs
Database: Firebase
AI: Gorq Inference api model
(Planned) TensorFlow.js for sentiment detection

Architecture:

Task Schema Example:

Plain textANTLR4BashCC#CSSCoffeeScriptCMakeDartDjangoDockerEJSErlangGitGoGraphQLGroovyHTMLJavaJavaScriptJSONJSXKotlinLaTeXLessLuaMakefileMarkdownMATLABMarkupObjective-CPerlPHPPowerShell.propertiesProtocol BuffersPythonRRubySass (Sass)Sass (Scss)SchemeSQLShellSwiftSVGTSXTypeScriptWebAssemblyYAMLXMLmodel Task { id Int @id @default(autoincrement()) name String dueDate DateTime? category String? mood String? priority Int? reason String? createdAt DateTime @default(now()) }

The reason field stores a short explanation generated by the AI — for example:“Submit project report — Priority 1 (Deadline today, critical for work).”

This reasoning adds transparency to the AI’s prioritization.

AI Logic

We used gorq ai reasoning models to reorder tasks intelligently.The system takes natural language inputs like mood and deadlines, and returns a ranked list with justifications.

Prompt Example:

Plain textANTLR4BashCC#CSSCoffeeScriptCMakeDartDjangoDockerEJSErlangGitGoGraphQLGroovyHTMLJavaJavaScriptJSONJSXKotlinLaTeXLessLuaMakefileMarkdownMATLABMarkupObjective-CPerlPHPPowerShell.propertiesProtocol BuffersPythonRRubySass (Sass)Sass (Scss)SchemeSQLShellSwiftSVGTSXTypeScriptWebAssemblyYAMLXMLYou are an AI productivity assistant. Given the user's mood and these tasks, assign priority from 1 (highest) to 5 (lowest) and provide a short reason for each in JSON. User mood: "Tired but focused" Tasks: 1. Complete report (due today) 2. Grocery shopping 3. Finish code review (due tomorrow) 4. Walk dog

Sample Output:

Plain textANTLR4BashCC#CSSCoffeeScriptCMakeDartDjangoDockerEJSErlangGitGoGraphQLGroovyHTMLJavaJavaScriptJSONJSXKotlinLaTeXLessLuaMakefileMarkdownMATLABMarkupObjective-CPerlPHPPowerShell.propertiesProtocol BuffersPythonRRubySass (Sass)Sass (Scss)SchemeSQLShellSwiftSVGTSXTypeScriptWebAssemblyYAMLXML[ {"task": "Complete report", "priority": 1, "reason": "Deadline today"}, {"task": "Finish code review", "priority": 2, "reason": "Due soon, important"}, {"task": "Walk dog", "priority": 3, "reason": "Short task, improves focus"}, {"task": "Grocery shopping", "priority": 4, "reason": "Can be delayed"} ]

Challenges We Faced

Consistent AI Output:Gorq ai model sometimes returned extra text instead of pure JSON, so we fine-tuned our prompt structure.
Integrating Emotion Awareness:Balancing user mood with logical prioritization required prompt experimentation and testing multiple weightings.
Time Management:As with most hackathons, time was short — integrating backend, frontend, and AI within hours required effective teamwork.
Visual Design:We designed the “Tentacle Mode” dashboard where each tentacle represents a task domain (Work, Health, Personal). Achieving both clarity and creativity was challenging.

What We Learned

How to use LLM reasoning for structured decision-making.
The art of prompt engineering for JSON consistency.
Team coordination and efficient division of tasks.
The balance between UX simplicity and AI complexity.

Productivity Equation

We described our vision mathematically as:

Effective Productivity=f(Prioritization,Emotion,Focus)\text{Effective Productivity} = f(\text{Prioritization}, \text{Emotion}, \text{Focus})Effective Productivity=f(Prioritization,Emotion,Focus)

Without emotion awareness, productivity apps manage tasks — not people.Adding emotional intelligence makes the experience truly human-centric.