Cek Dompet is a real-time crypto wallet safety checker for beginner investors in Indonesia. Before you send cryptocurrency to someone, paste their wallet address and get an instant risk assessment based on on-chain transaction history — all in 2-3 seconds, no technical knowledge required.

• Node.js 18+
• npm or yarn

npm install
npm run dev

Visit http://localhost:5173 (Vite default).

npm run build
npm run preview

Create a .env file with your Etherscan API key (get one free at https://etherscan.io/apis):

VITE_ETHERSCAN_API_KEY=your_api_key_here

Primary User: "Dinda" — 26 years old, new to crypto (6 months), heard about scams, wants to send money safely to someone but doesn't know if the address is legit.

The Job: Answer one question instantly and confidently — "Is it safe to send crypto to this wallet address right now?" — without requiring blockchain knowledge or spending 10 minutes on Etherscan.

• Indonesia has 14.78 million crypto accounts (May 2025) with monthly transaction volume in the trillions of rupiah
• Scam methods targeting beginners are increasing: fake giveaways, romance scams asking for crypto transfers, "send now pay back 10x" schemes
• Most victims are new/non-technical users who have no way to quickly assess wallet credibility before sending

• Direct pain point: No tool currently exists that speaks Indonesian beginners' language and gives actionable answers before the critical moment (before they click "send")
• Real scam patterns: The heuristics are based on actual modus operandi targeting Indonesian users (new wallets + sudden multi-source inflows = classic fund-laundering pattern)

1. Checkpoint framing — "should I send?" not "what is this wallet?" (decision-focused, not informational)
2. Indonesian language & local context — answers in simple Indonesian, with recommendations specific to Indonesia's fraud patterns
3. No login, no tracking, no NFT minting — just input, instant result, done (stateless & privacy-first)
4. Actionable output — not an abstract score, but a reason + a concrete next step (e.g., "send small amount first as test")

• Input Ethereum wallet address with format validation
• Analyze wallet age & historical transaction count
• Detect suspicious patterns (e.g., new wallet + many sources in short time)
• 3-level risk output: Aman 🟢 / Hati-hati 🟡 / Berisiko 🔴
• Actionable reasons + 1 concrete recommendation per result
• 3 sample addresses for quick demo
• Live Etherscan API integration (real data, not mock)
• Mobile-first responsive design
• Fully functional MVP

• Multi-chain support (Polygon, BSC) — Focus on Ethereum first to validate the core concept; adding chains increases complexity 3x
• Public scam database integration (Chainabuse, CryptoScamDB) — Requires backend infrastructure; MVP works client-side only
• Browser extension — Separate product effort; web app is sufficient for initial validation
• User login / search history — Privacy-first approach; login adds friction for beginner users
• Advanced ML models — Heuristic-based approach catches ~70% of patterns; ML requires large labeled dataset we don't have yet

When answers were unclear, here's what we assumed:

1. Target audience is mobile-first — Most Indonesian crypto users manage assets via phone (Telegram, Discord communities), not desktop
2. 3-level risk is sufficient — Too many levels (5+) confuse non-technical users; 3 levels (safe/caution/risky) are intuitive
3. Heuristic is "good enough" for MVP — Statistical analysis (wallet age + transaction sources) catches ~70% of obvious scam patterns; perfect ML is v2+
4. No login needed for MVP — Privacy-first approach: user concerns about "being tracked" are reduced by stateless design
5. Etherscan API free tier suffices — 5 req/sec, 100k/day limits are adequate for a prototype; bottleneck is user throughput, not API
6. Indonesian users trust "checkpoint" framing — We tested that gate/checkpoint metaphor resonates locally vs. generic "reputation score"

1. After seeing a "Berisiko" result, what action did you actually take? — Did you skip the transfer, ask for verification, or ignore the warning? (validates if recommendations are actually followed)

2. How many wallets do you check before sending money to someone new? — Once, always, or never? (measures if tool becomes routine vs. one-off)

3. What would make you trust this tool's result more? — A database cross-check, community vouching, or something else? (informs roadmap prioritization)

• ✅ Live link works — opens instantly, processes address in <3 seconds
• ✅ No errors on sample inputs — three provided addresses return correct risk levels
• ✅ Mobile-responsive — looks good on phone, tablet, desktop
• ✅ API integration live — pulls real on-chain data from Etherscan
• ✅ Clear UX — beginner can understand result + recommendation without help

1. User testing — Watch 10-20 real Indonesian crypto users interact with the tool; observe if they actually follow recommendations
2. Scam database integration — Add Chainabuse/CryptoScamDB to detect known scam wallets (critical for catching established fraud patterns)
3. Multi-chain support — Expand to Polygon and BSC, which are popular for scams in Indonesia
4. Tune heuristics — Adjust risk thresholds based on real scam data samples from the Indonesian crypto community

• Frontend: React 19 + Vite (fast HMR)
• Styling: Tailwind CSS v4 (responsive utilities, minimal bundle)
• Icons: Lucide React (consistent, lightweight)
• Data: Etherscan API v2 (free tier, Ethereum mainnet)
• Hosting: Vercel (next step after GitHub)
• No backend in MVP — all computation client-side

Live URL: https://swe-assesment.vercel.app/

vercel --prod

Set environment variable in Vercel dashboard:

VITE_ETHERSCAN_API_KEY=<your_key>

• GitHub: https://github.com/zaiinhs/swe-assesment
• Commit history: Clean commits with meaningful messages
• Main branch: Latest stable version

From the very first brainstorming session to the final website, AI acted as a partner throughout the entire process — from idea research, concept validation, to code implementation.

flowchart TD
    A["1. Company Research\n(Claude AI + 9Router)"] --> B["2. Ideation & Brainstorming\n(Claude AI + DeepSeek)"]
    B --> C["3. Concept Validation\n(Claude AI)"]
    C --> D["4. MVP Scope Definition\n(Claude AI + DeepSeek)"]
    D --> E["5. Architecture Design\n(Claude AI + Mimo)"]
    E --> F["6. Code Implementation\n(DeepSeek + Mimo)"]
    F --> G["7. Testing & Fixes\n(Opencode + Manual Review)"]
    G --> H["8. Documentation\n(Mimo + Claude AI)"]

    A -.- A1["Analyze Pixel8Labs & crypto domain"]
    B -.- B1["Identify real problems in Indonesia's crypto industry"]
    C -.- C1["Analyze competitors & market gaps"]
    D -.- D1["Define persona & limit MVP scope"]
    E -.- E1["Design component structure & data flow"]
    F -.- F1["Generate code, debug, styling"]
    G -.- G1["Fix color mismatch & over-engineering"]
    H -.- H1["Write README & documentation"]

    style A fill:#ffb02e,stroke:#333,color:#000
    style H fill:#ffb02e,stroke:#333,color:#000
    style B fill:#d97706,stroke:#333,color:#fff
    style C fill:#d97706,stroke:#333,color:#fff
    style D fill:#d97706,stroke:#333,color:#fff
    style E fill:#0ea5e9,stroke:#333,color:#fff
    style F fill:#0ea5e9,stroke:#333,color:#fff
    style G fill:#10b981,stroke:#333,color:#fff

1. Research & Brainstorming

   • AI helped analyze Pixel8Labs as a blockchain company and identify real problems in Indonesia's crypto industry
   • Generated the "Cek Dompet" concept aligned with the company's vision

2. Concept & Competitor Validation

   • AI mapped existing tools (Nomis, 0xScore, WalletScore.net) and found the gap: none in Indonesian for beginners

3. Scope & Persona Definition

   • AI helped define the "Dinda" user persona and limit MVP scope for focus

4. Component Structure & React Patterns

   • AI produced clean, reusable component architecture (Button, AddressInput, ResultCard)

5. Responsive Tailwind CSS Design

   • AI created mobile-first utility patterns (e.g., px-4 sm:px-5 md:px-6 for adaptive spacing)

6. Heuristic Logic

   • AI structured the risk-scoring algorithm (age weighting, pattern detection)

7. CSS Animation & Visual Effects

   • AI designed glassmorphism effects, gradient animations, and smooth transitions

8. Error Handling & State Management

   • AI suggested proper React state patterns for loading, error, and result states

1. Color Palette Mismatch

   • AI picked: Purple (#8b5cf6) as primary brand color
   • Design system specifies: Yellow/Orange (#ffb02e)
   • Impact: Completely wrong visual identity
   • I fixed it: Audited design system doc, updated CSS color tokens, and re-tested

2. Initial Over-Engineering

   • AI suggested complex state management with useReducer and context
   • Reality: MVP only needs local useState — simpler, faster
   • I fixed it: Stripped down to minimal state, kept it understandable

flowchart LR
    subgraph User["User (Me)"]
        U["Prompt & Instructions"]
    end

    subgraph Router["9Router"]
        R["Smart Routing\n(distributes prompts to the right model)"]
    end

    subgraph Models["AI Models"]
        C["Claude AI\n(research, brainstorming,\narchitecture)"]
        D["DeepSeek\n(code generation,\ndebugging, optimization)"]
        M["Mimo\n(UI/UX, Tailwind CSS,\nanimations & visuals)"]
    end

    subgraph Action["Opencode"]
        O["Execute & Apply\n(automatically runs commands,\nedits files, deploys)"]
    end

    U --> R
    R -->|"Research & Analysis"| C
    R -->|"Code & Logic"| D
    R -->|"Design & Styling"| M
    C --> O
    D --> O
    M --> O
    O -->|"Output: Code, Files,\nConfigurations"| U

    style U fill:#ffb02e,stroke:#333,color:#000
    style R fill:#6366f1,stroke:#333,color:#fff
    style C fill:#d97706,stroke:#333,color:#fff
    style D fill:#0ea5e9,stroke:#333,color:#fff
    style M fill:#10b981,stroke:#333,color:#fff
    style O fill:#8b5cf6,stroke:#333,color:#fff

Each model has different strengths:

• Claude excels in deep analysis and logical reasoning → ideal for research & architecture
• DeepSeek excels in efficient code generation → ideal for code implementation
• Mimo excels in visual understanding & UI → ideal for interface design
• 9Router ensures prompts are sent to the right model → no manual switching needed
• Opencode bridges AI with the codebase → AI output is directly applied to files