Public Projects

grand-snake

by Gaurang

Build a snake game fully functional in ios and android both

omega-application

by Muhammad Sarmad Nadeem

hey i want to build application in flutter application will todo app now what will you create for me

plasma-gamification

by Archit Suthar

Gamifying Financial Literacy for Bharat – Interactive Learning through Play Background Financial literacy is a combination of financial awareness, knowledge, skills, attitudes, and behaviours necessary to make sound financial decisions and ultimately achieve financial well-being. It enables individuals to make informed choices, exercise control over personal finances, avoid financial scams, and confidently navigate evolving financial systems. Financial literacy also encourages saving and investment to manage short-term income fluctuations and achieve long-term goals. Financial well-being reflects the ability to handle unexpected financial events, manage financial stress, and confidently use financial resources. Financial literacy policies and programmes ultimately aim to strengthen this individual financial well-being. Different user groups face distinct challenges. Farmers manage irregular and seasonal incomes with limited access to formal finance. Women encounter gaps in digital, financial, and entrepreneurial confidence. Students are forming money habits for the first time. Young adults face scams, impulsive spending, and weak long-term planning. The Core Challenge This hackathon invites participants to design engaging, context-aware, and behaviour-driven gamified solutions that simplify financial concepts and make them actionable in everyday life. Solutions should move beyond awareness and enable learning through exploration, simulation, and decision-making. Teams are encouraged to interpret the problem creatively, combine or adapt user tracks, and explore ideas beyond illustrative examples—provided the solution strengthens financial resilience and promotes behavioural learning. User Tracks Track A: The Farmer Farmers face financial literacy challenges primarily related to managing irregular and seasonal incomes, meeting time-bound expenses, and coping with uncertainty. Key gaps lie in cash-flow management, savings for lean periods, responsible use of credit, and risk protection. Limited familiarity with formal banking, insurance, and digital payment systems often leads to reliance on informal credit and low preparedness for financial shocks. Strengthening budgeting skills, savings discipline, credit awareness, and risk management behaviour is critical to improving stability and resilience across agricultural cycles. Track B: The Woman Women often manage daily household finances while contributing to income generation, yet face gaps in digital confidence, formal financial usage, and long-term planning. Challenges commonly arise in budgeting across multiple needs, separating household and business finances, using banking and digital payment systems safely, and building savings for future security. Enhancing confidence in financial decision-making, strengthening skills in savings, debt management, and digital safety, and encouraging proactive use of formal financial services are central to improving financial control and independence. Track C: The Student Students are at an early stage of developing money-related understanding, habits, and attitudes. Their financial literacy needs centre on basic budgeting, saving, prioritising needs over wants, and safe digital behaviour. Limited exposure to real decision-making often results in weak behavioural control and low awareness of consequences. Building foundational knowledge, encouraging reflective spending and saving behaviour, and fostering positive attitudes toward planning and discipline are essential to shaping healthy financial habits early in life. Track D: The Young Adult Young adults face increasing exposure to complex financial decisions involving income management, credit use, investments, taxes, and digital financial platforms. Common challenges include impulsive spending, vulnerability to scams, inadequate savings, and weak long-term planning. Gaps are often observed in understanding risk–return trade-offs, managing debt responsibly, planning for retirement, and protecting oneself in digital financial environments. Strengthening decision-making skills, behavioural control, and long-term orientation is key to enabling informed choices as financial responsibilities expand. Functional & Technical Constraints The Rule of Three: Integrate at least three financial themes such as savings, budgeting, insurance, investments, retirement, digital finance, consumer rights, or fraud prevention. Rural-Ready Technology: Solutions must be lightweight, low-bandwidth friendly, offline-capable, and rely more on voice and visuals than text. Behaviour Over Theory: Go beyond quizzes and include simulation or decision-based mechanics with meaningful consequences.

garnet-farmers

by Priya Mandaviya

Create a product for Indian farmers for every available government schemes they deserve and use easy illustration drawing so they can recognise easily and make it user friendly. also they can buy their product online in bulk and bid also. use appropriate colour palette and minimal clean UI design with all working CTAs.

prime-ai

by Janvi S

Scope of Work (SOW) Project: AI-Based Design & Schematic Generation Platform 1. Objective To develop an AI-powered chat-based application that enables users to describe ideas via text, image, or video and receive design schematics, 2D/3D models, and build guidance for real-world product creation. 2. Scope Overview The system will function as an AI Design Assistant, capable of: Understanding user intent through conversational interaction Processing multi-modal inputs (text, image, video) Generating conceptual and semi-technical design outputs Delivering structured outputs such as schematics, models, and step-by-step instructions 3. Input Modes (Core Differentiator) The platform will support three types of user inputs: 1. Text-Based Input (ChatGPT-style) Users describe ideas via text AI interprets and generates design outputs Iterative refinement via conversation 2. Image-Based Input Users upload reference images AI analyzes structure, components, and form Generates similar or improved design schematics 3. Video-Based Input (Phase-wise) Users upload short videos for reference AI extracts frames and key elements Generates structured design outputs based on motion/structure understanding 4. Key Modules & Features 4.1 Chat Interface Conversational UI (ChatGPT-like) Multi-modal input: text, image, video Context-aware interactions Iterative design refinement 4.2 AI Processing Engine LLM integration for reasoning and interaction Prompt orchestration and memory management Multi-step design reasoning workflows 4.3 Vision Processing Module Image and video frame analysis Object detection and feature extraction Reference-based design interpretation 4.4 Design Generation Module Conceptual design generation 2D schematics (diagrams, layouts) Basic 3D model generation (STL/OBJ) System architecture diagrams 4.5 Output & Export Module Export formats: STL / OBJ / PDF / Images Bill of Materials (basic) Step-by-step build instructions Downloadable design reports 4.6 Iteration & Customization Modify designs via prompts Adjust constraints (size, cost, materials) Version history of designs 4.7 Admin Panel User management Usage monitoring File and output tracking Basic analytics 5. Technology Stack (Indicative) Frontend: Web-based application Backend: Python / Node.js AI Models: LLM + Vision models Database: PostgreSQL / NoSQL Storage: Cloud storage Optional: Vector DB for contextual retrieval 6. Deliverables Web-based AI chat platform Text + Image + (Phase-wise) Video input support Design generation (2D + basic 3D outputs) File export (STL/OBJ/PDF) Admin panel Cloud deployment 7. Project Timeline (Refined) Phase 1 – MVP (6–8 Weeks) Chat interface (text-based) Basic AI design generation 2D schematic outputs Initial 3D model generation (basic STL/OBJ) File export (PDF/images) Phase 2 – Vision Integration (4–6 Weeks) Image input processing Reference-based design generation Improved 3D outputs Basic BOM + structured outputs Phase 3 – Advanced Capabilities (6–8 Weeks) Video input processing (frame extraction + interpretation) Enhanced AI reasoning and iteration Improved accuracy and customization Performance optimization Phase 4 – Scaling & Enhancements (Optional | 4–6 Weeks) UI/UX improvements Advanced export formats System optimization and scalability Domain-specific enhancements 8. Assumptions Initial outputs will be conceptual to semi-technical High-precision CAD outputs require advanced integrations (future phase) Third-party AI models/APIs may be used 9. Out of Scope (Initial Phases) Certified engineering-grade schematics Advanced simulations (stress, thermal, etc.) Manufacturing validation 10. Summary Multi-modal AI system (Text + Image + Video) Converts ideas → buildable designs Phased approach for faster launch and scalability

wild-luxury

by Purva

create a luxury interior design studio website

epic-candles

by Ritvik Ranjan

We're indian scented candle brand , where we're presenting indian culture thorough candle

mammoth-app

by Dennis Busienei (KILOMETRE)

Buld app that I can text with each other and it's well encrypted only sms, no video or voice note or picture, time stamp available and no delete chat, and also have password ercypted

venus-loading

by Gmail For adobe

Designing Load Management for Upper Helping delivery drivers load 100+ stops without confusion Overview Product: Upper Route Planner Company: Space-O Technologies Role: UX/UI Designer (end-to-end) Platform: Driver mobile app + Admin dashboard Status: Live What is Upper? Upper is a route planning tool used by delivery businesses. Admins plan and assign routes Drivers receive routes on their phone The app guides navigation and captures proof of delivery This part worked well. But one critical step was missing: What happens before the driver starts driving — loading the vehicle. The Problem Drivers often handle 100+ deliveries per route. But loading those items into the vehicle was slow, confusing, and unstructured. What was going wrong? 1. Too many steps Drivers had to open each stop one by one Add details → go back → find next stop This repeated 100+ times → Created friction and wasted time 2. No guidance for loading order App showed stops in delivery order But loading works in reverse Drivers had to mentally reverse the list while working fast. 3. No tracking of item placement No way to note where items were kept Drivers had to search the entire vehicle during delivery 4. Small delays became big losses Even tiny inefficiencies scaled badly across fleets. Why this mattered Upper helped with: Planning routes Navigation Delivery tracking But loading was left to guesswork. This broke the flow between planning and execution. Goal Make loading: Faster Easier to follow Less dependent on memory Useful during delivery Research & Understanding 1. Looking at other tools I reviewed similar products in this space. What I found: All focused on route planning and delivery None helped with loading 👉 This confirmed a clear gap. 2. Understanding real-world behavior Loading follows a simple rule: Last delivery → loaded first (goes inside) First delivery → loaded last (near the door) This is how drivers naturally work. But the app didn’t support this at all. Key Insight The app was optimized for planning and delivery, but not for the real-world step in between. If we guide loading properly: Drivers save time during loading Drivers save even more time during delivery Design Approach Instead of adding more features, I focused on: Reducing steps Matching real-world behavior Making information useful later (not just during input) What I Designed 1. Loading order that matches real life I reversed the stop list. Top = load first Bottom = load last Now drivers don’t need to think — they just follow the list. 2. One screen instead of many Before: Open each stop separately After: All stops visible in one screen Tap to expand and add details Why this works: No constant back-and-forth Faster interaction Clear sense of progress 3. Quick placement tags Instead of typing notes, drivers select from simple options: Front / Middle / Back Shelf / Floor Left / Right Why: Faster than typing Consistent across users Easy to scan later 4. Built-in scanning Drivers can scan items while loading. Confirms correct item Prevents mistakes early Creates a record 5. Show loading info during delivery At each stop, drivers see: Item count Placement Scan status Example: “2 items — Back, Floor — Scanned ✓” This is where the real value shows. Design Decisions (What I considered) Decision: List structure Option 1: Keep delivery order Familiar But confusing during loading Option 2: Reverse order Matches real-world behavior Easier to follow 👉 Chose reverse order Decision: Input method Option 1: Free text Flexible Slow and inconsistent Option 2: Tags Fast Structured Easy to reuse 👉 Chose tags Decision: Interaction model Option 1: Separate screens per stop Familiar Too slow Option 2: Single scrollable screen Faster Less friction 👉 Chose single screen How it fits into the product Before: Plan route Send to driver Driver starts driving After: Plan route Send to driver Load vehicle (new step) Deliver Capture proof Track everything Impact Loading time reduced Before: ~40–60 min After: ~15–25 min → ~30 minutes saved per driver Faster deliveries Less searching at each stop Even small savings per stop add up Fleet-level impact For 20 drivers: Dozens of hours saved daily Thousands of hours saved yearly What I Learned Real-world workflows matter more than screens If something isn’t faster than the manual way, people won’t use it Small improvements at each step can create huge impact at scale What I’d Improve Next Observe drivers in real conditions Test with real constraints (gloves, lighting, time pressure) Explore visual layouts for smaller routes Final Thought This wasn’t just about designing a feature. It was about removing guesswork from a critical part of the workflow. Why this version works Clear story from start to finish Shows thinking, not just UI Easy to scan quickly Strong product mindset visible

steady-prompt

by Shrishti Karn

create me a website that takes prompt from the user and 2d and 3d model floor design of the given prompt the prompt and design should perfectly align with each other and name of the website should ne floorforge the website should take prompt in natural language there should be sign in page login page and everything else that makes the website looks premium everything should be backed up with the back end too

venus-trading

by Nk Banger

Create an advanced trading and investment platform

arctic-steel

by MIHIR DOSHI

build my mild steel pipe business website