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AlgoTrade Sentinel — Helps users decide whether to buy, sell, or hold company stocks by using various machine learning algorithms. It instantly analyzes and trains on historical data to generate the best possible outcomes and support smarter wealth-building decisions.
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- 1 decision made based on gut feelings rather than evidence.
- 1 decision driven by personal opinion — "I'm the CEO, and I think this is better."
- 1 team debating which option is better without any supporting data.
- >1 platform filled with distractions, making it difficult to focus on creating or participating in A/B tests.
- >1 person struggling to make an everyday decision — for example, choosing which mouse is better.
- 1 version that could have performed better, but was never tested.
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🚀 Maybe you're right. Maybe I'm right. Let's find out.
🔥 How it works
- Creating a test costs 500 Gold.
- You earn Gold by voting on other people's tests.
- The system encourages everyone to contribute value back to the community before asking for feedback.
- Once a test is created, you can revisit it anytime, copy the link, and share it with friends, teammates, clients, or anyone whose opinion matters.
- Get fast, clear insights that help you make decisions without digging through endless discussions.
- Track your ranking and see where you stand within the community.
🔥 AI-Powered Features
- Generate and structure A/B tests faster with AI assistance.
- Automatically summarize community comments and feedback.
- Unlock deeper AI-powered analysis to understand not just what won, but why it won.
👍 Use Cases
- Which UI is better?
- Which landing page converts better?
- Which logo should I choose?
- Which product idea has more potential?
- Which mouse should I buy?
- What should I wear today?
From product decisions to everyday choices, A/B Battle helps turn uncertainty into confidence.
Spend less time overthinking.
Make decisions faster.
Have a better day with mAyBe!!!
- Discover it yourself : https://steep-odd-85537659.figma.site/
- 255 versions in Figma make: https://www.figma.com/make/8iwith7uVSU9iiSeIY3weC/mAyBe?t=uowgHgbDZU4tUyGt-1
- by MAX.terpiece -
I like your creativity
KILLBEROS
Risk-First (Educational · Fully simulated)
Measure the danger before the gain.
Most small investors don't lose for lack of ideas they lose for lack of risk discipline: they over-trade, double a losing position, and over-size out of fear or greed. KILLBEROS gives anyone the risk discipline of an investment fund, on a modest $1,000 budget ($500 in play, €500 kept untouchable). 100% educational, fully simulated no real money, no broker, no orders ever placed.
• Risk Planner — real position sizing on any instrument: size, risk, reward-to-risk, computed live.
• The 7 Gates — every trade must pass 7 risk checks; break one (e.g. a reward:risk below 2:1) and it's BLOCKED. The danger is measured first.
• Market Regime — live market "weather" (real Fear & Greed + real crypto prices) decides which strategies are even allowed to play.
• AI Advisor + a mistake "pokédex" — so the same error never repeats.
• Journal — log real trades, see your true edge: win rate, expectancy, drawdown (real backend, not a mockup).
Innovative Workflow)
KILLBEROS was designed and built in Figma Make, then reviewed live and iterated, version after version (V2 -> V43), by an AI agent "Cowork Brain" working through Figma's MCP.
But the real innovation is HOWdecisions were made: every significant choice (doctrine, a risk gate, a strategy, the UI) was debated by a standing panel of 11 AI "voices" that the agent embodies in one head — 5 trader profiles (macro, chartist, a risk/Aladdin voice with veto power, a short-term "gambler", and a long-term allocator) + 5 engineer profiles (backend, UX, API, security, data).
Risk speaks first, the angles are confronted, a decision is made, and the dissenting view is written down. (Junior) signs off on anything irreversible. It's a small AI team with a real method not a single prompt. Build-in-public: every version and decision is journaled.
And After :
KILLBEROS is the educational tip of the iceberg. Behind it, a far more powerful engine the full GambleRisk is in the works: the same risk-first doctrine, but it paper-trades itself for weeks to PROVE its edge, market regime by market regime, before anything else. Still risk-first, still discipline-before-gain. This is just the beginning.
Team : Junior Odika (+ Cowork Brain, AI collaborator).
URL :
Hey Junior, don't forget to share your project on Instagram, X, or LinkedIn with #ConfigMakeathon and @figma to complete your submission! 😊
Check the guidelines and get that in before the deadline!
🚀 Stockvel Credit System: Community Savings Platform
Preview Video: https://www.youtube.com/watch?v=PFPlZueA-OQ
A full-featured multi-role financial savings and credit platform built to manage community-based group savings (Stockvel/Stokvel model), where members contribute regularly and receive structured payouts based on defined rules.
This system is designed for real-world financial group management, not a demo toy.
💡 What This Project Solves
Traditional community savings groups often struggle with:
Lack of transparency in contributions
Manual tracking of payments and withdrawals
No proper role control (leader vs member confusion)
Poor record keeping and accountability
No structured approval workflows
This platform solves all of that with a clean, scalable digital system.
⚙️ Core Capabilities
👥 Role-Based System
Admin, Leader, and Member roles
Each role has strict permissions and responsibilities
Leader controls pack operations and member approvals
💰 Stockvel (Group Savings) Management
Create and manage savings groups (packs)
Define contribution rules and schedules
Set withdrawal cycles and limits
Member join requests with approval workflow
🔐 Trust & Verification Layer
Identity verification via document uploads
Admin approval for packs and leaders
Controlled onboarding flow
📊 Financial Structure Logic
Contribution tracking per member
Scheduled withdrawal assignment
Pack lifecycle management (active, pending, closed)
🔗 Sharing & Growth
Invite/share links for public joining
Controlled access via secure links
Scalable group expansion system
🧠 Engineering Notes (Important)
This project was built before ChatGPT and AI-assisted coding tools became mainstream.
That means:
No AI-generated architecture
No “vibe coding” or prompt-based scaffolding
Entire system was designed, structured, and debugged manually
Real engineering decisions were made for database design, permissions, and workflows
It reflects traditional full-stack engineering discipline rather than AI-assisted prototyping.
🛠️ Tech Stack
PHP 7.4 (Backend logic)
MySQL 5.7 (Relational data layer)
Apache (Server environment)
Docker (Containerized development & deployment)
Composer (Dependency management)
PDO (Secure database interactions)
📦 Why This Project Stands Out
Real-world financial system logic (not a CRUD demo)
Multi-role permission architecture
Complex relational database design
Migration-based schema system
Production-style Docker setup
Built for scalability and long-term use
🎯 Ideal Use Cases
This system can be adapted for:
Community savings groups (Stockvels / Rotating savings clubs)
Microfinance group platforms
Cooperative financial systems
Membership-based contribution platforms
NGO financial tracking systems
🔥 Summary
This is a production-grade community finance platform designed with real-world constraints, structured workflows, and strong role-based control.
It demonstrates:
Backend architecture thinking
Database design skills
System-level engineering
Real product development mindset
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