Kindly API Usage Guides¶

# After logging in, create an API key
curl -X POST https://api.kindly.com/api/keys \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"name": "My Application"}'

# Test your API key with a health check
curl -X GET https://api.kindly.com/api/health \
  -H "X-API-Key: YOUR_API_KEY"

# Compress some data
curl -X POST https://api.kindly.com/api/compression/compress \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "SGVsbG8gS2luZGx5IEFQSSE=",
    "privacy_level": "private"
  }'

pip install kindly-api
# Or manually:
pip install requests

import base64
import json
from typing import Dict, Any
import requests

class KindlyAPIClient:
    def __init__(self, api_key: str, base_url: str = "https://api.kindly.com"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {
            "X-API-Key": api_key,
            "Content-Type": "application/json"
        }

    def compress_data(self, data: bytes, privacy_level: str = "private") -> Dict[str, Any]:
        """Compress data with zero-knowledge proof."""
        encoded_data = base64.b64encode(data).decode('utf-8')

        response = requests.post(
            f"{self.base_url}/api/compression/compress",
            headers=self.headers,
            json={
                "data": encoded_data,
                "privacy_level": privacy_level
            }
        )
        response.raise_for_status()
        return response.json()

    def decompress_data(self, compressed_data: Dict[str, Any]) -> bytes:
        """Decompress data and verify proof."""
        response = requests.post(
            f"{self.base_url}/api/compression/decompress",
            headers=self.headers,
            json={
                "compressed_data": compressed_data,
                "verify_proof": True
            }
        )
        response.raise_for_status()
        result = response.json()
        return base64.b64decode(result["data"])

    def extract_context(self, content: str, levels: list = None) -> Dict[str, Any]:
        """Extract multi-level context from content."""
        if levels is None:
            levels = list(range(1, 8))  # All 7 levels

        response = requests.post(
            f"{self.base_url}/api/context/extract",
            headers=self.headers,
            json={
                "content": content,
                "levels": levels,
                "include_crown_jewels": True
            }
        )
        response.raise_for_status()
        return response.json()

    def quantum_predict(self, input_state: Dict[str, Any], horizon: int) -> Dict[str, Any]:
        """Generate quantum predictions."""
        response = requests.post(
            f"{self.base_url}/api/quantum/predict",
            headers=self.headers,
            json={
                "input_state": input_state,
                "prediction_horizon": horizon,
                "confidence_threshold": 0.8,
                "use_entanglement": True
            }
        )
        response.raise_for_status()
        return response.json()

# Usage Example
def main():
    client = KindlyAPIClient("your_api_key_here")

    # Compress sensitive data
    sensitive_data = b"This is confidential information"
    compressed = client.compress_data(sensitive_data, "confidential")
    print(f"Compression ratio: {compressed['compression_ratio']:.2%}")

    # Extract context
    text = "The server is experiencing high latency during peak hours"
    context = client.extract_context(text)
    print(f"Immediate entities: {context['contexts']['level1_immediate']['entities']}")

    # Quantum prediction
    quantum_state = {
        "vector": [0.707, 0.0, 0.707],
        "metadata": {"temperature": 0.1}
    }
    predictions = client.quantum_predict(quantum_state, horizon=5)
    print(f"Top prediction confidence: {predictions['predictions'][0]['confidence']}")

if __name__ == "__main__":
    main()

npm install axios
# For TypeScript:
npm install --save-dev @types/node

import axios, { AxiosInstance } from 'axios';

interface CompressedData {
  id: string;
  compressed_data: string;
  proof: Record<string, string>;
}

interface CompressionResponse {
  id: string;
  compressed_data: string;
  original_size: number;
  compressed_size: number;
  compression_ratio: number;
  privacy_level: string;
  proof: Record<string, string>;
}

interface QuantumPrediction {
  timestamp: string;
  state: {
    vector: number[];
    phase: number;
  };
  probability: number;
  confidence: number;
}

class KindlyAPIClient {
  private client: AxiosInstance;

  constructor(apiKey: string, baseURL: string = 'https://api.kindly.com') {
    this.client = axios.create({
      baseURL,
      headers: {
        'X-API-Key': apiKey,
        'Content-Type': 'application/json',
      },
    });

    // Add response interceptor for error handling
    this.client.interceptors.response.use(
      response => response,
      error => {
        if (error.response?.status === 429) {
          console.error('Rate limit exceeded. Retry after:', 
            error.response.headers['x-ratelimit-reset']);
        }
        return Promise.reject(error);
      }
    );
  }

  async compressData(
    data: string, 
    privacyLevel: 'public' | 'private' | 'confidential' | 'top_secret' = 'private'
  ): Promise<CompressionResponse> {
    const response = await this.client.post<CompressionResponse>(
      '/api/compression/compress',
      {
        data: Buffer.from(data).toString('base64'),
        privacy_level: privacyLevel,
      }
    );
    return response.data;
  }

  async homomorphicAdd(
    value1: CompressedData,
    value2: CompressedData
  ): Promise<{ result: CompressedData; proof: Record<string, string> }> {
    const response = await this.client.post('/api/compression/homomorphic-add', {
      value1,
      value2,
    });
    return response.data;
  }

  async neuralTransform(
    inputData: number[],
    useQuantumPathways: boolean = true
  ): Promise<{
    output: number[];
    computation_time_ms: number;
    pathways_used: number;
    quantum_efficiency: number;
  }> {
    const response = await this.client.post('/api/neural/transform', {
      input_data: inputData,
      use_quantum_pathways: useQuantumPathways,
    });
    return response.data;
  }

  async quantumPredict(
    inputState: { vector: number[]; metadata?: Record<string, any> },
    horizon: number
  ): Promise<{
    predictions: QuantumPrediction[];
    quantum_fidelity: number;
    decoherence_factor: number;
    entanglement_score: number;
  }> {
    const response = await this.client.post('/api/quantum/predict', {
      input_state: inputState,
      prediction_horizon: horizon,
      confidence_threshold: 0.8,
      use_entanglement: true,
    });
    return response.data;
  }

  async queryKnowledge(
    query: string,
    context: string[] = []
  ): Promise<any> {
    const response = await this.client.post('/api/agi/knowledge/query', {
      query,
      context,
      max_results: 10,
      include_reasoning: true,
    });
    return response.data;
  }
}

// Usage Example
async function main() {
  const client = new KindlyAPIClient(process.env.KINDLY_API_KEY!);

  try {
    // Compress data
    const compressed = await client.compressData(
      'Sensitive financial data',
      'confidential'
    );
    console.log(`Saved ${100 - compressed.compression_ratio * 100}% space`);

    // Neural transformation
    const neuralResult = await client.neuralTransform([0.1, 0.2, 0.3, 0.4, 0.5]);
    console.log('Neural output:', neuralResult.output);

    // Quantum prediction
    const predictions = await client.quantumPredict(
      { vector: [0.707, 0.0, 0.707] },
      10
    );
    console.log(`Generated ${predictions.predictions.length} quantum predictions`);

    // Knowledge query
    const knowledge = await client.queryKnowledge(
      'How does quantum entanglement affect distributed systems?',
      ['quantum_computing', 'distributed_systems']
    );
    console.log('Top result:', knowledge.results[0].concept);

  } catch (error) {
    console.error('API Error:', error);
  }
}

main();

go get github.com/kindly/go-client
# Or create your own client

package main

import (
    "bytes"
    "context"
    "encoding/base64"
    "encoding/json"
    "fmt"
    "net/http"
    "time"
)

type KindlyClient struct {
    apiKey     string
    baseURL    string
    httpClient *http.Client
}

type CompressionRequest struct {
    Data         string `json:"data"`
    PrivacyLevel string `json:"privacy_level"`
}

type CompressionResponse struct {
    ID               string                 `json:"id"`
    CompressedData   string                 `json:"compressed_data"`
    OriginalSize     int                    `json:"original_size"`
    CompressedSize   int                    `json:"compressed_size"`
    CompressionRatio float64                `json:"compression_ratio"`
    PrivacyLevel     string                 `json:"privacy_level"`
    Proof            map[string]interface{} `json:"proof"`
}

type ContextExtractRequest struct {
    Content           string `json:"content"`
    Levels            []int  `json:"levels"`
    IncludeCrownJewels bool   `json:"include_crown_jewels"`
}

type QuantumPredictRequest struct {
    InputState struct {
        Vector   []float64         `json:"vector"`
        Metadata map[string]float64 `json:"metadata,omitempty"`
    } `json:"input_state"`
    PredictionHorizon   int     `json:"prediction_horizon"`
    ConfidenceThreshold float64 `json:"confidence_threshold"`
    UseEntanglement     bool    `json:"use_entanglement"`
}

func NewKindlyClient(apiKey string) *KindlyClient {
    return &KindlyClient{
        apiKey:  apiKey,
        baseURL: "https://api.kindly.com",
        httpClient: &http.Client{
            Timeout: 30 * time.Second,
        },
    }
}

func (c *KindlyClient) doRequest(ctx context.Context, method, path string, body interface{}, result interface{}) error {
    var reqBody []byte
    var err error

    if body != nil {
        reqBody, err = json.Marshal(body)
        if err != nil {
            return fmt.Errorf("marshal request: %w", err)
        }
    }

    req, err := http.NewRequestWithContext(ctx, method, c.baseURL+path, bytes.NewBuffer(reqBody))
    if err != nil {
        return fmt.Errorf("create request: %w", err)
    }

    req.Header.Set("X-API-Key", c.apiKey)
    req.Header.Set("Content-Type", "application/json")

    resp, err := c.httpClient.Do(req)
    if err != nil {
        return fmt.Errorf("do request: %w", err)
    }
    defer resp.Body.Close()

    if resp.StatusCode != http.StatusOK {
        return fmt.Errorf("API error: status %d", resp.StatusCode)
    }

    if result != nil {
        if err := json.NewDecoder(resp.Body).Decode(result); err != nil {
            return fmt.Errorf("decode response: %w", err)
        }
    }

    return nil
}

func (c *KindlyClient) CompressData(ctx context.Context, data []byte, privacyLevel string) (*CompressionResponse, error) {
    req := CompressionRequest{
        Data:         base64.StdEncoding.EncodeToString(data),
        PrivacyLevel: privacyLevel,
    }

    var resp CompressionResponse
    err := c.doRequest(ctx, "POST", "/api/compression/compress", req, &resp)
    if err != nil {
        return nil, err
    }

    return &resp, nil
}

func (c *KindlyClient) ExtractContext(ctx context.Context, content string, levels []int) (map[string]interface{}, error) {
    if len(levels) == 0 {
        levels = []int{1, 2, 3, 4, 5, 6, 7}
    }

    req := ContextExtractRequest{
        Content:            content,
        Levels:             levels,
        IncludeCrownJewels: true,
    }

    var resp map[string]interface{}
    err := c.doRequest(ctx, "POST", "/api/context/extract", req, &resp)
    if err != nil {
        return nil, err
    }

    return resp, nil
}

func (c *KindlyClient) QuantumPredict(ctx context.Context, inputVector []float64, horizon int) (map[string]interface{}, error) {
    req := QuantumPredictRequest{
        PredictionHorizon:   horizon,
        ConfidenceThreshold: 0.8,
        UseEntanglement:     true,
    }
    req.InputState.Vector = inputVector
    req.InputState.Metadata = map[string]float64{"temperature": 0.1}

    var resp map[string]interface{}
    err := c.doRequest(ctx, "POST", "/api/quantum/predict", req, &resp)
    if err != nil {
        return nil, err
    }

    return resp, nil
}

// Concurrent operations example
func (c *KindlyClient) BatchCompress(ctx context.Context, items [][]byte, privacyLevel string) ([]*CompressionResponse, error) {
    results := make([]*CompressionResponse, len(items))
    errChan := make(chan error, len(items))

    for i, data := range items {
        go func(idx int, d []byte) {
            resp, err := c.CompressData(ctx, d, privacyLevel)
            if err != nil {
                errChan <- err
                return
            }
            results[idx] = resp
            errChan <- nil
        }(i, data)
    }

    // Wait for all goroutines
    for range items {
        if err := <-errChan; err != nil {
            return nil, err
        }
    }

    return results, nil
}

func main() {
    client := NewKindlyClient("your_api_key_here")
    ctx := context.Background()

    // Example 1: Compress data
    data := []byte("Confidential business data")
    compressed, err := client.CompressData(ctx, data, "confidential")
    if err != nil {
        panic(err)
    }
    fmt.Printf("Compression saved %.1f%% space\n", (1-compressed.CompressionRatio)*100)

    // Example 2: Extract context
    content := "The payment processing system is failing during checkout"
    context, err := client.ExtractContext(ctx, content, []int{1, 2, 3})
    if err != nil {
        panic(err)
    }
    fmt.Printf("Context extracted: %v\n", context["contexts"])

    // Example 3: Quantum prediction
    predictions, err := client.QuantumPredict(ctx, []float64{0.707, 0.0, 0.707}, 5)
    if err != nil {
        panic(err)
    }
    fmt.Printf("Quantum predictions: %v\n", predictions["predictions"])

    // Example 4: Batch compression (concurrent)
    items := [][]byte{
        []byte("Data 1"),
        []byte("Data 2"),
        []byte("Data 3"),
    }
    batchResults, err := client.BatchCompress(ctx, items, "private")
    if err != nil {
        panic(err)
    }
    fmt.Printf("Compressed %d items\n", len(batchResults))
}

gem install httparty
# Or add to Gemfile:
# gem 'httparty'

require 'httparty'
require 'base64'
require 'json'

class KindlyAPIClient
  include HTTParty
  base_uri 'https://api.kindly.com'

  def initialize(api_key)
    @api_key = api_key
    @headers = {
      'X-API-Key' => api_key,
      'Content-Type' => 'application/json'
    }
  end

  # Zero-Knowledge Compression
  def compress_data(data, privacy_level = 'private')
    encoded_data = Base64.encode64(data).strip

    response = self.class.post('/api/compression/compress',
      headers: @headers,
      body: {
        data: encoded_data,
        privacy_level: privacy_level
      }.to_json
    )

    handle_response(response)
  end

  def decompress_data(compressed_data, verify_proof = true)
    response = self.class.post('/api/compression/decompress',
      headers: @headers,
      body: {
        compressed_data: compressed_data,
        verify_proof: verify_proof
      }.to_json
    )

    result = handle_response(response)
    Base64.decode64(result['data'])
  end

  # Context Extraction
  def extract_context(content, levels = nil)
    levels ||= (1..7).to_a

    response = self.class.post('/api/context/extract',
      headers: @headers,
      body: {
        content: content,
        levels: levels,
        include_crown_jewels: true
      }.to_json
    )

    handle_response(response)
  end

  # Neural Topology
  def evolve_neural_topology(evolution_params = {}, performance_targets = {})
    default_params = {
      mutation_rate: 0.1,
      crossover_rate: 0.7,
      selection_pressure: 2.0
    }

    response = self.class.post('/api/neural/evolve',
      headers: @headers,
      body: {
        evolution_params: default_params.merge(evolution_params),
        performance_targets: performance_targets
      }.to_json
    )

    handle_response(response)
  end

  # Quantum Prediction
  def quantum_predict(input_state, horizon, options = {})
    request_body = {
      input_state: input_state,
      prediction_horizon: horizon,
      confidence_threshold: options[:confidence_threshold] || 0.8,
      use_entanglement: options[:use_entanglement] != false
    }

    response = self.class.post('/api/quantum/predict',
      headers: @headers,
      body: request_body.to_json
    )

    handle_response(response)
  end

  # AGI Knowledge Query
  def query_knowledge(query, context = [], max_results = 10)
    response = self.class.post('/api/agi/knowledge/query',
      headers: @headers,
      body: {
        query: query,
        context: context,
        max_results: max_results,
        include_reasoning: true
      }.to_json
    )

    handle_response(response)
  end

  # Analytics
  def get_analytics_overview(start_date = nil, end_date = nil)
    params = {}
    params[:start_date] = start_date if start_date
    params[:end_date] = end_date if end_date

    response = self.class.get('/api/analytics/overview',
      headers: @headers,
      query: params
    )

    handle_response(response)
  end

  private

  def handle_response(response)
    case response.code
    when 200, 201
      response.parsed_response
    when 429
      reset_time = response.headers['x-ratelimit-reset']
      raise "Rate limit exceeded. Reset at: #{Time.at(reset_time.to_i)}"
    else
      error = response.parsed_response
      raise "API Error: #{error['message']} (#{error['error']})"
    end
  end
end

# Usage Examples
if __FILE__ == $0
  client = KindlyAPIClient.new(ENV['KINDLY_API_KEY'])

  begin
    # Example 1: Compress sensitive data
    puts "=== Zero-Knowledge Compression ==="
    sensitive_data = "Credit card: 4111-1111-1111-1111"
    compressed = client.compress_data(sensitive_data, 'top_secret')
    puts "Compression ratio: #{(compressed['compression_ratio'] * 100).round(1)}%"
    puts "Space saved: #{100 - (compressed['compression_ratio'] * 100).round(1)}%"

    # Example 2: Extract multi-level context
    puts "\n=== Context Extraction ==="
    text = "Customer reported slow checkout process on mobile app during Black Friday sale"
    context = client.extract_context(text)

    immediate = context['contexts']['level1_immediate']
    puts "Entities found: #{immediate['entities'].join(', ')}"
    puts "Temporal markers: #{immediate['temporal_markers'].join(', ')}"

    # Example 3: Quantum prediction
    puts "\n=== Quantum Prediction ==="
    quantum_state = {
      vector: [0.707, 0.0, 0.707],
      metadata: { temperature: 0.1 }
    }
    predictions = client.quantum_predict(quantum_state, 5)

    puts "Quantum fidelity: #{predictions['quantum_fidelity']}"
    puts "Number of predictions: #{predictions['predictions'].length}"
    puts "Top prediction confidence: #{predictions['predictions'][0]['confidence']}"

    # Example 4: Knowledge query
    puts "\n=== AGI Knowledge Query ==="
    knowledge = client.query_knowledge(
      "What are the best practices for microservices architecture?",
      ["software_architecture", "distributed_systems"]
    )

    top_result = knowledge['results'][0]
    puts "Top concept: #{top_result['concept']}"
    puts "Relevance: #{(top_result['relevance'] * 100).round(1)}%"
    puts "Reasoning: #{top_result['reasoning']}"

    # Example 5: Analytics
    puts "\n=== Analytics Overview ==="
    analytics = client.get_analytics_overview
    summary = analytics['summary']
    puts "Total requests: #{summary['total_requests']}"
    puts "Compression savings: #{summary['compression_savings_gb']} GB"
    puts "Quantum predictions: #{summary['quantum_predictions_made']}"

  rescue => e
    puts "Error: #{e.message}"
  end
end

dotnet add package Newtonsoft.Json
dotnet add package RestSharp

using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Newtonsoft.Json;
using RestSharp;

namespace KindlyAPI
{
    public class KindlyApiClient
    {
        private readonly string _apiKey;
        private readonly RestClient _client;

        public KindlyApiClient(string apiKey, string baseUrl = "https://api.kindly.com")
        {
            _apiKey = apiKey;
            _client = new RestClient(baseUrl);
        }

        private RestRequest CreateRequest(string resource, Method method)
        {
            var request = new RestRequest(resource, method);
            request.AddHeader("X-API-Key", _apiKey);
            request.AddHeader("Content-Type", "application/json");
            return request;
        }

        // Zero-Knowledge Compression
        public async Task<CompressionResponse> CompressDataAsync(byte[] data, string privacyLevel = "private")
        {
            var request = CreateRequest("/api/compression/compress", Method.Post);
            var encodedData = Convert.ToBase64String(data);

            request.AddJsonBody(new
            {
                data = encodedData,
                privacy_level = privacyLevel
            });

            var response = await _client.ExecuteAsync<CompressionResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return response.Data;
        }

        public async Task<byte[]> DecompressDataAsync(CompressedData compressedData)
        {
            var request = CreateRequest("/api/compression/decompress", Method.Post);
            request.AddJsonBody(new
            {
                compressed_data = compressedData,
                verify_proof = true
            });

            var response = await _client.ExecuteAsync<DecompressionResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return Convert.FromBase64String(response.Data.Data);
        }

        // Context Extraction
        public async Task<ContextExtractionResponse> ExtractContextAsync(string content, int[] levels = null)
        {
            var request = CreateRequest("/api/context/extract", Method.Post);
            levels ??= new[] { 1, 2, 3, 4, 5, 6, 7 };

            request.AddJsonBody(new
            {
                content = content,
                levels = levels,
                include_crown_jewels = true
            });

            var response = await _client.ExecuteAsync<ContextExtractionResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return response.Data;
        }

        // Neural Topology Evolution
        public async Task<NeuralEvolutionResponse> EvolveNeuralTopologyAsync(
            EvolutionParams evolutionParams = null,
            PerformanceTargets performanceTargets = null)
        {
            var request = CreateRequest("/api/neural/evolve", Method.Post);

            request.AddJsonBody(new
            {
                evolution_params = evolutionParams ?? new EvolutionParams(),
                performance_targets = performanceTargets ?? new PerformanceTargets()
            });

            var response = await _client.ExecuteAsync<NeuralEvolutionResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return response.Data;
        }

        // Quantum Prediction
        public async Task<QuantumPredictionResponse> QuantumPredictAsync(
            QuantumState inputState,
            int predictionHorizon,
            double confidenceThreshold = 0.8)
        {
            var request = CreateRequest("/api/quantum/predict", Method.Post);

            request.AddJsonBody(new
            {
                input_state = inputState,
                prediction_horizon = predictionHorizon,
                confidence_threshold = confidenceThreshold,
                use_entanglement = true
            });

            var response = await _client.ExecuteAsync<QuantumPredictionResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return response.Data;
        }

        // AGI Knowledge Query
        public async Task<KnowledgeQueryResponse> QueryKnowledgeAsync(
            string query,
            List<string> context = null,
            int maxResults = 10)
        {
            var request = CreateRequest("/api/agi/knowledge/query", Method.Post);

            request.AddJsonBody(new
            {
                query = query,
                context = context ?? new List<string>(),
                max_results = maxResults,
                include_reasoning = true
            });

            var response = await _client.ExecuteAsync<KnowledgeQueryResponse>(request);
            if (!response.IsSuccessful)
                throw new Exception($"API Error: {response.ErrorMessage}");

            return response.Data;
        }
    }

    // Data Models
    public class CompressionResponse
    {
        public string Id { get; set; }
        public string CompressedData { get; set; }
        public int OriginalSize { get; set; }
        public int CompressedSize { get; set; }
        public double CompressionRatio { get; set; }
        public string PrivacyLevel { get; set; }
        public Dictionary<string, object> Proof { get; set; }
    }

    public class CompressedData
    {
        public string Id { get; set; }
        public string CompressedData { get; set; }
        public Dictionary<string, object> Proof { get; set; }
    }

    public class DecompressionResponse
    {
        public string Data { get; set; }
        public bool Verified { get; set; }
        public int OriginalSize { get; set; }
    }

    public class ContextExtractionResponse
    {
        public Dictionary<string, object> Contexts { get; set; }
        public List<CrownJewel> CrownJewels { get; set; }
        public double ProcessingTimeMs { get; set; }
    }

    public class CrownJewel
    {
        public string Type { get; set; }
        public string Value { get; set; }
        public double Confidence { get; set; }
    }

    public class EvolutionParams
    {
        public double MutationRate { get; set; } = 0.1;
        public double CrossoverRate { get; set; } = 0.7;
        public double SelectionPressure { get; set; } = 2.0;
    }

    public class PerformanceTargets
    {
        public double? MinAccuracy { get; set; }
        public int? MaxLatencyMs { get; set; }
        public int? MaxMemoryMb { get; set; }
    }

    public class NeuralEvolutionResponse
    {
        public string EvolutionId { get; set; }
        public int Generation { get; set; }
        public double FitnessScore { get; set; }
        public List<TopologyChange> TopologyChanges { get; set; }
    }

    public class TopologyChange
    {
        public string Type { get; set; }
        public Dictionary<string, object> Details { get; set; }
    }

    public class QuantumState
    {
        public double[] Vector { get; set; }
        public Dictionary<string, double> Metadata { get; set; }
    }

    public class QuantumPredictionResponse
    {
        public List<QuantumPrediction> Predictions { get; set; }
        public double QuantumFidelity { get; set; }
        public double DecoherenceFactor { get; set; }
        public double EntanglementScore { get; set; }
    }

    public class QuantumPrediction
    {
        public DateTime Timestamp { get; set; }
        public QuantumState State { get; set; }
        public double Probability { get; set; }
        public double Confidence { get; set; }
    }

    public class KnowledgeQueryResponse
    {
        public List<KnowledgeResult> Results { get; set; }
        public QueryUnderstanding QueryUnderstanding { get; set; }
    }

    public class KnowledgeResult
    {
        public string Concept { get; set; }
        public double Relevance { get; set; }
        public List<string> Connections { get; set; }
        public string Reasoning { get; set; }
    }

    public class QueryUnderstanding
    {
        public string Intent { get; set; }
        public List<string> Entities { get; set; }
        public double Confidence { get; set; }
    }

    // Example Usage
    class Program
    {
        static async Task Main(string[] args)
        {
            var client = new KindlyApiClient(Environment.GetEnvironmentVariable("KINDLY_API_KEY"));

            try
            {
                // Example 1: Zero-Knowledge Compression
                Console.WriteLine("=== Zero-Knowledge Compression ===");
                var data = System.Text.Encoding.UTF8.GetBytes("Sensitive corporate data");
                var compressed = await client.CompressDataAsync(data, "confidential");
                Console.WriteLine($"Compression ratio: {compressed.CompressionRatio:P}");
                Console.WriteLine($"Space saved: {(1 - compressed.CompressionRatio):P}");

                // Example 2: Context Extraction
                Console.WriteLine("\n=== Context Extraction ===");
                var context = await client.ExtractContextAsync(
                    "The API gateway is experiencing timeout errors during database queries"
                );
                Console.WriteLine($"Processing time: {context.ProcessingTimeMs}ms");
                if (context.CrownJewels?.Count > 0)
                {
                    Console.WriteLine($"Crown jewel found: {context.CrownJewels[0].Type}");
                }

                // Example 3: Quantum Prediction
                Console.WriteLine("\n=== Quantum Prediction ===");
                var quantumState = new QuantumState
                {
                    Vector = new[] { 0.707, 0.0, 0.707 },
                    Metadata = new Dictionary<string, double> { { "temperature", 0.1 } }
                };
                var predictions = await client.QuantumPredictAsync(quantumState, 5);
                Console.WriteLine($"Quantum fidelity: {predictions.QuantumFidelity:F3}");
                Console.WriteLine($"Predictions generated: {predictions.Predictions.Count}");

                // Example 4: Knowledge Query
                Console.WriteLine("\n=== AGI Knowledge Query ===");
                var knowledge = await client.QueryKnowledgeAsync(
                    "How can I improve API performance?",
                    new List<string> { "performance", "optimization" }
                );
                var topResult = knowledge.Results[0];
                Console.WriteLine($"Top concept: {topResult.Concept}");
                Console.WriteLine($"Relevance: {topResult.Relevance:P}");
                Console.WriteLine($"Reasoning: {topResult.Reasoning}");

            }
            catch (Exception ex)
            {
                Console.WriteLine($"Error: {ex.Message}");
            }
        }
    }
}

# Store user data with maximum privacy
def store_user_data(user_id: str, sensitive_data: dict):
    # Serialize and compress with top-secret privacy
    data_bytes = json.dumps(sensitive_data).encode()
    compressed = api.compress_data(data_bytes, "top_secret")

    # Store only the compressed ID and proof
    db.store_user_data(user_id, {
        "compressed_id": compressed["id"],
        "proof": compressed["proof"],
        "timestamp": datetime.utcnow()
    })

    # Original data never touches the database
    return compressed["id"]

# Retrieve and verify
def get_user_data(user_id: str):
    stored = db.get_user_data(user_id)

    # Decompress with proof verification
    decompressed = api.decompress_data(stored["compressed_id"])

    if decompressed["verified"]:
        return json.loads(decompressed["data"])
    else:
        raise SecurityError("Proof verification failed")

// Monitor system metrics with quantum prediction
async function monitorSystemHealth() {
  const metrics = collectSystemMetrics();

  // Convert to quantum state
  const quantumState = {
    vector: normalizeMetrics(metrics),
    metadata: {
      temperature: 0.1,
      coherence: 0.95
    }
  };

  // Get predictions for next 10 time steps
  const predictions = await api.quantumPredict(quantumState, 10);

  // Check for anomalies
  for (const prediction of predictions.predictions) {
    if (prediction.confidence > 0.8 && isAnomalous(prediction.state)) {
      await alertOps({
        severity: 'high',
        probability: prediction.probability,
        predictedTime: prediction.timestamp,
        anomalyType: classifyAnomaly(prediction.state)
      });
    }
  }
}