diff --git a/sdk/flutter/analysis_options.yaml b/sdk/flutter/analysis_options.yaml
new file mode 100644
index 0000000..2d9eedd
--- /dev/null
+++ b/sdk/flutter/analysis_options.yaml
@@ -0,0 +1,84 @@
+include: package:flutter_lints/flutter.yaml
+
+linter:
+  rules:
+    # Style rules
+    - always_declare_return_types
+    - avoid_empty_else
+    - avoid_relative_lib_imports
+    - avoid_returning_null_for_void
+    - avoid_slow_async_io
+    - avoid_types_as_parameter_names
+    - avoid_unused_constructor_parameters
+    - await_only_futures
+    - camel_case_types
+    - cancel_subscriptions
+    - close_sinks
+    - constant_identifier_names
+    - control_flow_in_finally
+    - curly_braces_in_flow_control_structures
+    - empty_catches
+    - empty_constructor_bodies
+    - empty_statements
+    - hash_and_equals
+    - implementation_imports
+    - library_names
+    - library_prefixes
+    - no_duplicate_case_values
+    - non_constant_identifier_names
+    - null_closures
+    - overridden_fields
+    - package_names
+    - package_prefixed_library_names
+    - prefer_adjacent_string_concatenation
+    - prefer_collection_literals
+    - prefer_conditional_assignment
+    - prefer_const_constructors
+    - prefer_const_declarations
+    - prefer_contains
+    - prefer_final_fields
+    - prefer_final_locals
+    - prefer_for_elements_to_map_fromIterable
+    - prefer_generic_function_type_aliases
+    - prefer_if_null_operators
+    - prefer_initializing_formals
+    - prefer_inlined_adds
+    - prefer_interpolation_to_compose_strings
+    - prefer_is_empty
+    - prefer_is_not_empty
+    - prefer_iterable_whereType
+    - prefer_single_quotes
+    - prefer_spread_collections
+    - prefer_typing_uninitialized_variables
+    - recursive_getters
+    - slash_for_doc_comments
+    - sort_child_properties_last
+    - test_types_in_equals
+    - throw_in_finally
+    - type_init_formals
+    - unawaited_futures
+    - unnecessary_brace_in_string_interps
+    - unnecessary_const
+    - unnecessary_getters_setters
+    - unnecessary_new
+    - unnecessary_null_in_if_null_operators
+    - unnecessary_overrides
+    - unnecessary_parenthesis
+    - unnecessary_statements
+    - unnecessary_string_escapes
+    - unnecessary_string_interpolations
+    - unnecessary_this
+    - unrelated_type_equality_checks
+    - use_function_type_syntax_for_parameters
+    - use_rethrow_when_possible
+    - valid_regexps
+    - void_checks
+
+analyzer:
+  exclude:
+    - "**/*.g.dart"
+    - "**/*.freezed.dart"
+  errors:
+    missing_required_param: error
+    missing_return: error
+    todo: ignore
diff --git a/sdk/flutter/example/example.dart b/sdk/flutter/example/example.dart
new file mode 100644
index 0000000..524e757
--- /dev/null
+++ b/sdk/flutter/example/example.dart
@@ -0,0 +1,178 @@
+import 'dart:io';
+
+import 'package:synor_compute/synor_compute.dart';
+
+/// Example usage of Synor Compute SDK for Flutter/Dart
+void main() async {
+  // Initialize client with API key
+  final client = SynorCompute(
+    apiKey: Platform.environment['SYNOR_API_KEY'] ?? 'your-api-key',
+    // Optional: customize defaults
+    defaultProcessor: ProcessorType.auto,
+    defaultPrecision: Precision.fp32,
+    defaultPriority: Priority.normal,
+  );
+
+  try {
+    // Check service health
+    final isHealthy = await client.healthCheck();
+    print('Service healthy: $isHealthy\n');
+
+    // Example 1: Matrix multiplication
+    await matrixMultiplicationExample(client);
+
+    // Example 2: Tensor operations
+    await tensorOperationsExample(client);
+
+    // Example 3: LLM inference
+    await llmInferenceExample(client);
+
+    // Example 4: Streaming inference
+    await streamingInferenceExample(client);
+
+    // Example 5: Pricing and usage
+    await pricingExample(client);
+  } finally {
+    // Always dispose client to release resources
+    client.dispose();
+  }
+}
+
+/// Matrix multiplication example
+Future<void> matrixMultiplicationExample(SynorCompute client) async {
+  print('=== Matrix Multiplication ===');
+
+  // Create random matrices
+  final a = Tensor.rand([256, 512]);
+  final b = Tensor.rand([512, 256]);
+
+  print('A: ${a.shape}');
+  print('B: ${b.shape}');
+
+  // Perform multiplication on GPU with FP16 precision
+  final result = await client.matmul(
+    a,
+    b,
+    options: MatMulOptions(
+      precision: Precision.fp16,
+      processor: ProcessorType.gpu,
+      priority: Priority.high,
+    ),
+  );
+
+  if (result.isSuccess) {
+    print('Result: ${result.result!.shape}');
+    print('Execution time: ${result.executionTimeMs}ms');
+    print('Cost: \$${result.cost?.toStringAsFixed(6)}');
+    print('Processor: ${result.processor?.value}');
+  } else {
+    print('Error: ${result.error}');
+  }
+  print('');
+}
+
+/// Local tensor operations example
+Future<void> tensorOperationsExample(SynorCompute client) async {
+  print('=== Tensor Operations ===');
+
+  // Create tensors
+  final x = Tensor.randn([100], mean: 0.0, std: 1.0);
+  print('Random normal tensor: mean=${x.mean().toStringAsFixed(4)}, '
+      'std=${x.std().toStringAsFixed(4)}');
+
+  // Create identity matrix
+  final eye = Tensor.eye(4);
+  print('Identity matrix:\n${eye.toNestedList()}');
+
+  // Create linspace
+  final linspace = Tensor.linspace(0, 10, 5);
+  print('Linspace [0, 10, 5]: ${linspace.toNestedList()}');
+
+  // Reshape operations
+  final matrix = Tensor.arange(0, 12).reshape([3, 4]);
+  print('Reshaped [0..12] to [3,4]:\n${matrix.toNestedList()}');
+
+  // Transpose
+  final transposed = matrix.transpose();
+  print('Transposed to ${transposed.shape}');
+
+  // Activations
+  final input = Tensor(shape: [5], data: [-2.0, -1.0, 0.0, 1.0, 2.0]);
+  print('ReLU of $input: ${input.relu().toNestedList()}');
+  print('Sigmoid of $input: ${input.sigmoid().toNestedList()}');
+
+  // Softmax
+  final logits = Tensor(shape: [4], data: [1.0, 2.0, 3.0, 4.0]);
+  print('Softmax of $logits: ${logits.softmax().toNestedList()}');
+
+  print('');
+}
+
+/// LLM inference example
+Future<void> llmInferenceExample(SynorCompute client) async {
+  print('=== LLM Inference ===');
+
+  final result = await client.inference(
+    'llama-3-70b',
+    'What is the capital of France? Answer in one word.',
+    options: InferenceOptions(
+      maxTokens: 10,
+      temperature: 0.1,
+      processor: ProcessorType.lpu, // Use LPU for LLM
+    ),
+  );
+
+  if (result.isSuccess) {
+    print('Response: ${result.result}');
+    print('Time: ${result.executionTimeMs}ms');
+  } else {
+    print('Error: ${result.error}');
+  }
+  print('');
+}
+
+/// Streaming inference example
+Future<void> streamingInferenceExample(SynorCompute client) async {
+  print('=== Streaming Inference ===');
+  print('Response: ');
+
+  await for (final token in client.inferenceStream(
+    'llama-3-70b',
+    'Write a short poem about distributed computing.',
+    options: InferenceOptions(
+      maxTokens: 100,
+      temperature: 0.7,
+    ),
+  )) {
+    stdout.write(token);
+  }
+
+  print('\n');
+}
+
+/// Pricing and usage example
+Future<void> pricingExample(SynorCompute client) async {
+  print('=== Pricing Information ===');
+
+  final pricing = await client.getPricing();
+
+  print('Current spot prices:');
+  for (final p in pricing) {
+    print('  ${p.processor.value.toUpperCase().padRight(8)}: '
+        '\$${p.pricePerSecond.toStringAsFixed(6)}/sec, '
+        '${p.availableUnits} units available, '
+        '${p.utilizationPercent.toStringAsFixed(1)}% utilized');
+  }
+
+  print('');
+
+  // Get usage stats
+  final usage = await client.getUsage();
+  print('Usage Statistics:');
+  print('  Total jobs: ${usage.totalJobs}');
+  print('  Completed: ${usage.completedJobs}');
+  print('  Failed: ${usage.failedJobs}');
+  print('  Total compute time: ${usage.totalComputeSeconds.toStringAsFixed(2)}s');
+  print('  Total cost: \$${usage.totalCost.toStringAsFixed(4)}');
+  print('');
+}
diff --git a/sdk/flutter/lib/src/client.dart b/sdk/flutter/lib/src/client.dart
new file mode 100644
index 0000000..5e401a5
--- /dev/null
+++ b/sdk/flutter/lib/src/client.dart
@@ -0,0 +1,541 @@
+/// Main client for Synor Compute SDK
+library synor_compute.client;
+
+import 'dart:async';
+import 'dart:convert';
+
+import 'package:http/http.dart' as http;
+
+import 'job.dart';
+import 'tensor.dart';
+import 'types.dart';
+
+/// Main client for interacting with Synor Compute
+class SynorCompute {
+  final SynorConfig _config;
+  final http.Client _httpClient;
+  bool _isDisposed = false;
+
+  /// Creates a new Synor Compute client
+  SynorCompute({
+    required String apiKey,
+    String baseUrl = 'https://compute.synor.io',
+    Duration timeout = const Duration(seconds: 30),
+    int maxRetries = 3,
+    ProcessorType defaultProcessor = ProcessorType.auto,
+    Precision defaultPrecision = Precision.fp32,
+    Priority defaultPriority = Priority.normal,
+    http.Client? httpClient,
+  })  : _config = SynorConfig(
+          apiKey: apiKey,
+          baseUrl: baseUrl,
+          timeout: timeout,
+          maxRetries: maxRetries,
+          defaultProcessor: defaultProcessor,
+          defaultPrecision: defaultPrecision,
+          defaultPriority: defaultPriority,
+        ),
+        _httpClient = httpClient ?? http.Client();
+
+  /// Creates a client from configuration
+  SynorCompute.fromConfig(SynorConfig config, {http.Client? httpClient})
+      : _config = config,
+        _httpClient = httpClient ?? http.Client();
+
+  Map<String, String> get _headers => {
+        'Authorization': 'Bearer ${_config.apiKey}',
+        'Content-Type': 'application/json',
+        'Accept': 'application/json',
+        'X-SDK-Version': 'flutter/0.1.0',
+      };
+
+  void _checkDisposed() {
+    if (_isDisposed) {
+      throw StateError('Client has been disposed');
+    }
+  }
+
+  /// Perform matrix multiplication
+  Future<JobResult<Tensor>> matmul(
+    Tensor a,
+    Tensor b, {
+    MatMulOptions? options,
+  }) async {
+    _checkDisposed();
+
+    final opts = options ?? const MatMulOptions();
+    final body = {
+      'operation': 'matmul',
+      'inputs': {
+        'a': a.toJson(),
+        'b': b.toJson(),
+      },
+      'options': {
+        'precision': (opts.precision ?? _config.defaultPrecision).value,
+        'processor': (opts.processor ?? _config.defaultProcessor).value,
+        'priority': (opts.priority ?? _config.defaultPriority).value,
+        'transpose_a': opts.transposeA,
+        'transpose_b': opts.transposeB,
+      },
+    };
+
+    return _submitAndWait<Tensor>(
+      body,
+      (result) => Tensor.fromJson(result as Map<String, dynamic>),
+    );
+  }
+
+  /// Perform 2D convolution
+  Future<JobResult<Tensor>> conv2d(
+    Tensor input,
+    Tensor kernel, {
+    Conv2dOptions? options,
+  }) async {
+    _checkDisposed();
+
+    final opts = options ?? const Conv2dOptions();
+    final body = {
+      'operation': 'conv2d',
+      'inputs': {
+        'input': input.toJson(),
+        'kernel': kernel.toJson(),
+      },
+      'options': {
+        ...opts.toJson(),
+        'precision': (opts.precision ?? _config.defaultPrecision).value,
+        'processor': (opts.processor ?? _config.defaultProcessor).value,
+        'priority': (opts.priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    return _submitAndWait<Tensor>(
+      body,
+      (result) => Tensor.fromJson(result as Map<String, dynamic>),
+    );
+  }
+
+  /// Perform flash attention
+  Future<JobResult<Tensor>> attention(
+    Tensor query,
+    Tensor key,
+    Tensor value, {
+    required AttentionOptions options,
+  }) async {
+    _checkDisposed();
+
+    final body = {
+      'operation': 'flash_attention',
+      'inputs': {
+        'query': query.toJson(),
+        'key': key.toJson(),
+        'value': value.toJson(),
+      },
+      'options': {
+        ...options.toJson(),
+        'precision': (options.precision ?? _config.defaultPrecision).value,
+        'processor': (options.processor ?? _config.defaultProcessor).value,
+        'priority': (options.priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    return _submitAndWait<Tensor>(
+      body,
+      (result) => Tensor.fromJson(result as Map<String, dynamic>),
+    );
+  }
+
+  /// Run LLM inference
+  Future<JobResult<String>> inference(
+    String model,
+    String input, {
+    InferenceOptions? options,
+  }) async {
+    _checkDisposed();
+
+    final opts = options ?? const InferenceOptions();
+    final body = {
+      'operation': 'inference',
+      'model': model,
+      'input': input,
+      'options': {
+        ...opts.toJson(),
+        'processor': (opts.processor ?? _config.defaultProcessor).value,
+        'priority': (opts.priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    return _submitAndWait<String>(
+      body,
+      (result) => result['text'] as String,
+    );
+  }
+
+  /// Stream LLM inference with real-time token output
+  Stream<String> inferenceStream(
+    String model,
+    String input, {
+    InferenceOptions? options,
+  }) async* {
+    _checkDisposed();
+
+    final opts = options ?? const InferenceOptions();
+    final body = {
+      'operation': 'inference',
+      'model': model,
+      'input': input,
+      'options': {
+        ...opts.toJson(),
+        'stream': true,
+        'processor': (opts.processor ?? _config.defaultProcessor).value,
+        'priority': (opts.priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    final request = http.Request('POST', Uri.parse('${_config.baseUrl}/stream'))
+      ..headers.addAll(_headers)
+      ..body = jsonEncode(body);
+
+    final streamedResponse = await _httpClient.send(request);
+
+    if (streamedResponse.statusCode != 200) {
+      final responseBody = await streamedResponse.stream.bytesToString();
+      throw SynorException(
+        'Streaming request failed',
+        statusCode: streamedResponse.statusCode,
+        details: {'response': responseBody},
+      );
+    }
+
+    await for (final chunk in streamedResponse.stream.transform(utf8.decoder)) {
+      // Parse SSE format
+      for (final line in chunk.split('\n')) {
+        if (line.startsWith('data: ')) {
+          final data = line.substring(6);
+          if (data == '[DONE]') return;
+          try {
+            final json = jsonDecode(data) as Map<String, dynamic>;
+            if (json['token'] != null) {
+              yield json['token'] as String;
+            }
+          } catch (e) {
+            // Skip malformed JSON
+          }
+        }
+      }
+    }
+  }
+
+  /// Apply element-wise operation
+  Future<JobResult<Tensor>> elementwise(
+    String operation,
+    Tensor input, {
+    Tensor? other,
+    double? scalar,
+    Precision? precision,
+    ProcessorType? processor,
+    Priority? priority,
+  }) async {
+    _checkDisposed();
+
+    final body = {
+      'operation': 'elementwise',
+      'op': operation,
+      'inputs': {
+        'input': input.toJson(),
+        if (other != null) 'other': other.toJson(),
+        if (scalar != null) 'scalar': scalar,
+      },
+      'options': {
+        'precision': (precision ?? _config.defaultPrecision).value,
+        'processor': (processor ?? _config.defaultProcessor).value,
+        'priority': (priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    return _submitAndWait<Tensor>(
+      body,
+      (result) => Tensor.fromJson(result as Map<String, dynamic>),
+    );
+  }
+
+  /// Reduce operation (sum, mean, max, min, etc.)
+  Future<JobResult<Tensor>> reduce(
+    String operation,
+    Tensor input, {
+    List<int>? axes,
+    bool keepDims = false,
+    Precision? precision,
+    ProcessorType? processor,
+    Priority? priority,
+  }) async {
+    _checkDisposed();
+
+    final body = {
+      'operation': 'reduce',
+      'op': operation,
+      'inputs': {
+        'input': input.toJson(),
+      },
+      'options': {
+        if (axes != null) 'axes': axes,
+        'keep_dims': keepDims,
+        'precision': (precision ?? _config.defaultPrecision).value,
+        'processor': (processor ?? _config.defaultProcessor).value,
+        'priority': (priority ?? _config.defaultPriority).value,
+      },
+    };
+
+    return _submitAndWait<Tensor>(
+      body,
+      (result) => Tensor.fromJson(result as Map<String, dynamic>),
+    );
+  }
+
+  /// Submit a custom operation
+  Future<Job<T>> submit<T>(
+    Map<String, dynamic> operation,
+    T Function(dynamic) resultParser,
+  ) async {
+    _checkDisposed();
+
+    final response = await _post('/jobs', operation);
+    final jobId = response['job_id'] as String;
+
+    return Job<T>(
+      jobId: jobId,
+      baseUrl: _config.baseUrl,
+      headers: _headers,
+      resultParser: resultParser,
+    );
+  }
+
+  /// Get job by ID
+  Future<JobResult<T>> getJob<T>(
+    String jobId, {
+    T Function(dynamic)? resultParser,
+  }) async {
+    _checkDisposed();
+
+    final response = await _get('/jobs/$jobId');
+    return JobResult<T>.fromJson(response, resultParser);
+  }
+
+  /// Cancel a job
+  Future<bool> cancelJob(String jobId) async {
+    _checkDisposed();
+
+    try {
+      await _post('/jobs/$jobId/cancel', {});
+      return true;
+    } catch (e) {
+      return false;
+    }
+  }
+
+  /// List active jobs
+  Future<List<JobResult<dynamic>>> listJobs({
+    JobStatus? status,
+    int limit = 20,
+    int offset = 0,
+  }) async {
+    _checkDisposed();
+
+    final params = <String, String>{
+      'limit': limit.toString(),
+      'offset': offset.toString(),
+      if (status != null) 'status': status.value,
+    };
+
+    final response = await _get('/jobs', params);
+    final jobs = response['jobs'] as List;
+    return jobs
+        .map((j) => JobResult<dynamic>.fromJson(j as Map<String, dynamic>, null))
+        .toList();
+  }
+
+  /// Get current pricing information
+  Future<List<PricingInfo>> getPricing() async {
+    _checkDisposed();
+
+    final response = await _get('/pricing');
+    final pricing = response['pricing'] as List;
+    return pricing
+        .map((p) => PricingInfo.fromJson(p as Map<String, dynamic>))
+        .toList();
+  }
+
+  /// Get pricing for specific processor
+  Future<PricingInfo> getPricingFor(ProcessorType processor) async {
+    final allPricing = await getPricing();
+    return allPricing.firstWhere(
+      (p) => p.processor == processor,
+      orElse: () => throw SynorException(
+        'No pricing available for processor ${processor.value}',
+      ),
+    );
+  }
+
+  /// Get account usage statistics
+  Future<UsageStats> getUsage({DateTime? from, DateTime? to}) async {
+    _checkDisposed();
+
+    final params = <String, String>{
+      if (from != null) 'from': from.toIso8601String(),
+      if (to != null) 'to': to.toIso8601String(),
+    };
+
+    final response = await _get('/usage', params);
+    return UsageStats.fromJson(response);
+  }
+
+  /// Upload a tensor for reuse
+  Future<String> uploadTensor(Tensor tensor, {String? name}) async {
+    _checkDisposed();
+
+    final body = {
+      'tensor': tensor.toJson(),
+      if (name != null) 'name': name,
+    };
+
+    final response = await _post('/tensors', body);
+    return response['tensor_id'] as String;
+  }
+
+  /// Download a tensor by ID
+  Future<Tensor> downloadTensor(String tensorId) async {
+    _checkDisposed();
+
+    final response = await _get('/tensors/$tensorId');
+    return Tensor.fromJson(response);
+  }
+
+  /// Delete a tensor by ID
+  Future<void> deleteTensor(String tensorId) async {
+    _checkDisposed();
+
+    await _delete('/tensors/$tensorId');
+  }
+
+  /// Health check
+  Future<bool> healthCheck() async {
+    try {
+      final response = await _get('/health');
+      return response['status'] == 'healthy';
+    } catch (e) {
+      return false;
+    }
+  }
+
+  // Internal HTTP methods
+
+  Future<JobResult<T>> _submitAndWait<T>(
+    Map<String, dynamic> body,
+    T Function(dynamic) resultParser,
+  ) async {
+    final response = await _post('/jobs', body);
+    final jobId = response['job_id'] as String;
+
+    final job = Job<T>(
+      jobId: jobId,
+      baseUrl: _config.baseUrl,
+      headers: _headers,
+      resultParser: resultParser,
+    );
+
+    try {
+      return await _pollJob<T>(jobId, resultParser);
+    } finally {
+      job.dispose();
+    }
+  }
+
+  Future<JobResult<T>> _pollJob<T>(
+    String jobId,
+    T Function(dynamic) resultParser, {
+    Duration interval = const Duration(milliseconds: 500),
+    Duration timeout = const Duration(minutes: 5),
+  }) async {
+    final endTime = DateTime.now().add(timeout);
+
+    while (DateTime.now().isBefore(endTime)) {
+      final response = await _get('/jobs/$jobId');
+      final result = JobResult<T>.fromJson(response, resultParser);
+
+      if (result.status.isTerminal) {
+        return result;
+      }
+
+      await Future.delayed(interval);
+    }
+
+    throw SynorException('Job polling timed out after $timeout');
+  }
+
+  Future<Map<String, dynamic>> _get(
+    String path, [
+    Map<String, String>? queryParams,
+  ]) async {
+    var uri = Uri.parse('${_config.baseUrl}$path');
+    if (queryParams != null && queryParams.isNotEmpty) {
+      uri = uri.replace(queryParameters: queryParams);
+    }
+
+    final response = await _httpClient
+        .get(uri, headers: _headers)
+        .timeout(_config.timeout);
+
+    return _handleResponse(response);
+  }
+
+  Future<Map<String, dynamic>> _post(
+    String path,
+    Map<String, dynamic> body,
+  ) async {
+    final uri = Uri.parse('${_config.baseUrl}$path');
+    final response = await _httpClient
+        .post(uri, headers: _headers, body: jsonEncode(body))
+        .timeout(_config.timeout);
+
+    return _handleResponse(response);
+  }
+
+  Future<void> _delete(String path) async {
+    final uri = Uri.parse('${_config.baseUrl}$path');
+    final response = await _httpClient
+        .delete(uri, headers: _headers)
+        .timeout(_config.timeout);
+
+    if (response.statusCode != 200 && response.statusCode != 204) {
+      _handleResponse(response);
+    }
+  }
+
+  Map<String, dynamic> _handleResponse(http.Response response) {
+    if (response.statusCode >= 200 && response.statusCode < 300) {
+      if (response.body.isEmpty) {
+        return {};
+      }
+      return jsonDecode(response.body) as Map<String, dynamic>;
+    }
+
+    Map<String, dynamic>? errorBody;
+    try {
+      errorBody = jsonDecode(response.body) as Map<String, dynamic>;
+    } catch (e) {
+      // Body is not JSON
+    }
+
+    throw SynorException(
+      errorBody?['message'] as String? ?? 'Request failed',
+      code: errorBody?['code'] as String?,
+      statusCode: response.statusCode,
+      details: errorBody,
+    );
+  }
+
+  /// Dispose the client and release resources
+  void dispose() {
+    _isDisposed = true;
+    _httpClient.close();
+  }
+}
diff --git a/sdk/flutter/lib/src/job.dart b/sdk/flutter/lib/src/job.dart
new file mode 100644
index 0000000..3ef0f76
--- /dev/null
+++ b/sdk/flutter/lib/src/job.dart
@@ -0,0 +1,394 @@
+/// Job tracking for Synor Compute SDK
+library synor_compute.job;
+
+import 'dart:async';
+import 'dart:convert';
+
+import 'package:web_socket_channel/web_socket_channel.dart';
+
+import 'tensor.dart';
+import 'types.dart';
+
+/// Result of a compute job
+class JobResult<T> {
+  /// Unique job identifier
+  final String jobId;
+
+  /// Current job status
+  final JobStatus status;
+
+  /// Result data (if completed)
+  final T? result;
+
+  /// Error message (if failed)
+  final String? error;
+
+  /// Execution time in milliseconds
+  final int? executionTimeMs;
+
+  /// Cost in credits
+  final double? cost;
+
+  /// Processor that executed the job
+  final ProcessorType? processor;
+
+  /// Metadata from execution
+  final Map<String, dynamic>? metadata;
+
+  const JobResult({
+    required this.jobId,
+    required this.status,
+    this.result,
+    this.error,
+    this.executionTimeMs,
+    this.cost,
+    this.processor,
+    this.metadata,
+  });
+
+  /// Whether the job completed successfully
+  bool get isSuccess => status == JobStatus.completed && result != null;
+
+  /// Whether the job failed
+  bool get isFailed => status == JobStatus.failed;
+
+  /// Whether the job is still running
+  bool get isRunning => !status.isTerminal;
+
+  factory JobResult.fromJson(
+    Map<String, dynamic> json,
+    T Function(dynamic)? resultParser,
+  ) {
+    final status = JobStatus.fromString(json['status'] as String);
+    T? result;
+
+    if (json['result'] != null && resultParser != null) {
+      result = resultParser(json['result']);
+    }
+
+    return JobResult<T>(
+      jobId: json['job_id'] as String,
+      status: status,
+      result: result,
+      error: json['error'] as String?,
+      executionTimeMs: json['execution_time_ms'] as int?,
+      cost: (json['cost'] as num?)?.toDouble(),
+      processor: json['processor'] != null
+          ? ProcessorType.fromString(json['processor'] as String)
+          : null,
+      metadata: json['metadata'] as Map<String, dynamic>?,
+    );
+  }
+
+  /// Transform the result to a different type
+  JobResult<R> map<R>(R Function(T) transform) {
+    return JobResult<R>(
+      jobId: jobId,
+      status: status,
+      result: result != null ? transform(result as T) : null,
+      error: error,
+      executionTimeMs: executionTimeMs,
+      cost: cost,
+      processor: processor,
+      metadata: metadata,
+    );
+  }
+
+  @override
+  String toString() {
+    if (isSuccess) {
+      return 'JobResult(id: $jobId, status: ${status.value}, '
+          'time: ${executionTimeMs}ms, cost: \$${cost?.toStringAsFixed(6)})';
+    } else if (isFailed) {
+      return 'JobResult(id: $jobId, status: ${status.value}, error: $error)';
+    }
+    return 'JobResult(id: $jobId, status: ${status.value})';
+  }
+}
+
+/// Job status update event
+class JobStatusUpdate {
+  final String jobId;
+  final JobStatus status;
+  final double? progress;
+  final String? message;
+  final DateTime timestamp;
+
+  const JobStatusUpdate({
+    required this.jobId,
+    required this.status,
+    this.progress,
+    this.message,
+    required this.timestamp,
+  });
+
+  factory JobStatusUpdate.fromJson(Map<String, dynamic> json) {
+    return JobStatusUpdate(
+      jobId: json['job_id'] as String,
+      status: JobStatus.fromString(json['status'] as String),
+      progress: (json['progress'] as num?)?.toDouble(),
+      message: json['message'] as String?,
+      timestamp: json['timestamp'] != null
+          ? DateTime.parse(json['timestamp'] as String)
+          : DateTime.now(),
+    );
+  }
+}
+
+/// Job handle for tracking and managing a submitted job
+class Job<T> {
+  final String jobId;
+  final String _baseUrl;
+  final Map<String, String> _headers;
+  final T Function(dynamic)? _resultParser;
+
+  WebSocketChannel? _wsChannel;
+  StreamController<JobStatusUpdate>? _statusController;
+  JobResult<T>? _cachedResult;
+  bool _isDisposed = false;
+
+  Job({
+    required this.jobId,
+    required String baseUrl,
+    required Map<String, String> headers,
+    T Function(dynamic)? resultParser,
+  })  : _baseUrl = baseUrl,
+        _headers = headers,
+        _resultParser = resultParser;
+
+  /// Stream of status updates for this job
+  Stream<JobStatusUpdate> get statusUpdates {
+    _statusController ??= StreamController<JobStatusUpdate>.broadcast(
+      onListen: _startWebSocket,
+      onCancel: _stopWebSocket,
+    );
+    return _statusController!.stream;
+  }
+
+  void _startWebSocket() {
+    if (_isDisposed) return;
+
+    final wsUrl = _baseUrl
+        .replaceFirst('http://', 'ws://')
+        .replaceFirst('https://', 'wss://');
+
+    _wsChannel = WebSocketChannel.connect(
+      Uri.parse('$wsUrl/jobs/$jobId/stream'),
+    );
+
+    _wsChannel!.stream.listen(
+      (data) {
+        if (_isDisposed) return;
+        try {
+          final json = jsonDecode(data as String) as Map<String, dynamic>;
+          final update = JobStatusUpdate.fromJson(json);
+          _statusController?.add(update);
+
+          if (update.status.isTerminal) {
+            _stopWebSocket();
+          }
+        } catch (e) {
+          // Ignore parse errors
+        }
+      },
+      onError: (error) {
+        if (!_isDisposed) {
+          _statusController?.addError(error);
+        }
+      },
+      onDone: _stopWebSocket,
+    );
+  }
+
+  void _stopWebSocket() {
+    _wsChannel?.sink.close();
+    _wsChannel = null;
+  }
+
+  /// Poll for job result (for environments without WebSocket support)
+  Future<JobResult<T>> poll({
+    Duration interval = const Duration(milliseconds: 500),
+    Duration timeout = const Duration(minutes: 5),
+  }) async {
+    if (_cachedResult?.status.isTerminal == true) {
+      return _cachedResult!;
+    }
+
+    final endTime = DateTime.now().add(timeout);
+    final client = _createHttpClient();
+
+    try {
+      while (DateTime.now().isBefore(endTime)) {
+        final response = await client.get(
+          Uri.parse('$_baseUrl/jobs/$jobId'),
+          headers: _headers,
+        );
+
+        if (response.statusCode != 200) {
+          throw SynorException(
+            'Failed to poll job status',
+            statusCode: response.statusCode,
+          );
+        }
+
+        final json = jsonDecode(response.body) as Map<String, dynamic>;
+        final result = JobResult<T>.fromJson(json, _resultParser);
+
+        if (result.status.isTerminal) {
+          _cachedResult = result;
+          return result;
+        }
+
+        await Future.delayed(interval);
+      }
+
+      throw SynorException('Job polling timed out after $timeout');
+    } finally {
+      client.close();
+    }
+  }
+
+  /// Wait for job completion with automatic strategy selection
+  Future<JobResult<T>> wait({
+    Duration timeout = const Duration(minutes: 5),
+    bool useWebSocket = true,
+  }) async {
+    if (_cachedResult?.status.isTerminal == true) {
+      return _cachedResult!;
+    }
+
+    if (useWebSocket) {
+      try {
+        final completer = Completer<JobResult<T>>();
+        late StreamSubscription<JobStatusUpdate> subscription;
+
+        subscription = statusUpdates.listen(
+          (update) async {
+            if (update.status.isTerminal && !completer.isCompleted) {
+              final result = await poll(
+                interval: Duration.zero,
+                timeout: const Duration(seconds: 10),
+              );
+              completer.complete(result);
+              await subscription.cancel();
+            }
+          },
+          onError: (error) {
+            if (!completer.isCompleted) {
+              completer.completeError(error);
+            }
+          },
+        );
+
+        return await completer.future.timeout(
+          timeout,
+          onTimeout: () {
+            subscription.cancel();
+            throw SynorException('Job wait timed out after $timeout');
+          },
+        );
+      } catch (e) {
+        // Fall back to polling if WebSocket fails
+        return poll(timeout: timeout);
+      }
+    }
+
+    return poll(timeout: timeout);
+  }
+
+  /// Cancel the job
+  Future<bool> cancel() async {
+    final client = _createHttpClient();
+    try {
+      final response = await client.post(
+        Uri.parse('$_baseUrl/jobs/$jobId/cancel'),
+        headers: _headers,
+      );
+
+      if (response.statusCode == 200) {
+        _cachedResult = JobResult<T>(
+          jobId: jobId,
+          status: JobStatus.cancelled,
+        );
+        return true;
+      }
+      return false;
+    } finally {
+      client.close();
+    }
+  }
+
+  /// Get current job status
+  Future<JobStatus> getStatus() async {
+    final client = _createHttpClient();
+    try {
+      final response = await client.get(
+        Uri.parse('$_baseUrl/jobs/$jobId/status'),
+        headers: _headers,
+      );
+
+      if (response.statusCode != 200) {
+        throw SynorException(
+          'Failed to get job status',
+          statusCode: response.statusCode,
+        );
+      }
+
+      final json = jsonDecode(response.body) as Map<String, dynamic>;
+      return JobStatus.fromString(json['status'] as String);
+    } finally {
+      client.close();
+    }
+  }
+
+  /// Dispose resources
+  void dispose() {
+    _isDisposed = true;
+    _stopWebSocket();
+    _statusController?.close();
+    _statusController = null;
+  }
+
+  // Creates an HTTP client - in a real app, use http package
+  dynamic _createHttpClient() {
+    // This is a placeholder - actual implementation uses http package
+    throw UnimplementedError('HTTP client should be injected');
+  }
+}
+
+/// Batch job operations
+class JobBatch<T> {
+  final List<Job<T>> jobs;
+
+  JobBatch(this.jobs);
+
+  /// Wait for all jobs to complete
+  Future<List<JobResult<T>>> waitAll({
+    Duration timeout = const Duration(minutes: 10),
+  }) async {
+    return Future.wait(
+      jobs.map((job) => job.wait(timeout: timeout)),
+    );
+  }
+
+  /// Wait for first job to complete
+  Future<JobResult<T>> waitAny({
+    Duration timeout = const Duration(minutes: 5),
+  }) async {
+    return Future.any(
+      jobs.map((job) => job.wait(timeout: timeout)),
+    );
+  }
+
+  /// Cancel all jobs
+  Future<void> cancelAll() async {
+    await Future.wait(jobs.map((job) => job.cancel()));
+  }
+
+  /// Dispose all job resources
+  void dispose() {
+    for (final job in jobs) {
+      job.dispose();
+    }
+  }
+}
diff --git a/sdk/flutter/lib/src/tensor.dart b/sdk/flutter/lib/src/tensor.dart
new file mode 100644
index 0000000..82b8b31
--- /dev/null
+++ b/sdk/flutter/lib/src/tensor.dart
@@ -0,0 +1,520 @@
+/// Tensor implementation for Synor Compute SDK
+library synor_compute.tensor;
+
+import 'dart:convert';
+import 'dart:math' as math;
+import 'dart:typed_data';
+
+import 'types.dart';
+
+/// Multi-dimensional tensor for compute operations
+class Tensor {
+  /// Tensor shape (dimensions)
+  final List<int> shape;
+
+  /// Underlying data as Float64List
+  final Float64List data;
+
+  /// Data type
+  final DType dtype;
+
+  /// Unique identifier (assigned by server)
+  final String? id;
+
+  /// Creates a tensor with given shape and data
+  Tensor({
+    required this.shape,
+    required List<double> data,
+    this.dtype = DType.float64,
+    this.id,
+  }) : data = Float64List.fromList(data) {
+    final expectedSize = shape.fold<int>(1, (a, b) => a * b);
+    if (this.data.length != expectedSize) {
+      throw ArgumentError(
+        'Data length ${this.data.length} does not match shape $shape '
+        '(expected $expectedSize elements)',
+      );
+    }
+  }
+
+  /// Creates a tensor from Float64List
+  Tensor.fromTypedData({
+    required this.shape,
+    required this.data,
+    this.dtype = DType.float64,
+    this.id,
+  }) {
+    final expectedSize = shape.fold<int>(1, (a, b) => a * b);
+    if (data.length != expectedSize) {
+      throw ArgumentError(
+        'Data length ${data.length} does not match shape $shape '
+        '(expected $expectedSize elements)',
+      );
+    }
+  }
+
+  /// Creates a tensor filled with zeros
+  factory Tensor.zeros(List<int> shape, {DType dtype = DType.float64}) {
+    final size = shape.fold<int>(1, (a, b) => a * b);
+    return Tensor(
+      shape: shape,
+      data: List.filled(size, 0.0),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates a tensor filled with ones
+  factory Tensor.ones(List<int> shape, {DType dtype = DType.float64}) {
+    final size = shape.fold<int>(1, (a, b) => a * b);
+    return Tensor(
+      shape: shape,
+      data: List.filled(size, 1.0),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates a tensor filled with a specific value
+  factory Tensor.full(
+    List<int> shape,
+    double value, {
+    DType dtype = DType.float64,
+  }) {
+    final size = shape.fold<int>(1, (a, b) => a * b);
+    return Tensor(
+      shape: shape,
+      data: List.filled(size, value),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates a tensor with random values from uniform distribution [0, 1)
+  factory Tensor.rand(List<int> shape, {DType dtype = DType.float64}) {
+    final size = shape.fold<int>(1, (a, b) => a * b);
+    final random = math.Random();
+    return Tensor(
+      shape: shape,
+      data: List.generate(size, (_) => random.nextDouble()),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates a tensor with random values from normal distribution
+  factory Tensor.randn(
+    List<int> shape, {
+    double mean = 0.0,
+    double std = 1.0,
+    DType dtype = DType.float64,
+  }) {
+    final size = shape.fold<int>(1, (a, b) => a * b);
+    final random = math.Random();
+
+    // Box-Muller transform for normal distribution
+    double nextGaussian() {
+      final u1 = random.nextDouble();
+      final u2 = random.nextDouble();
+      return math.sqrt(-2 * math.log(u1)) * math.cos(2 * math.pi * u2);
+    }
+
+    return Tensor(
+      shape: shape,
+      data: List.generate(size, (_) => mean + std * nextGaussian()),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates an identity matrix
+  factory Tensor.eye(int n, {DType dtype = DType.float64}) {
+    final data = List.filled(n * n, 0.0);
+    for (var i = 0; i < n; i++) {
+      data[i * n + i] = 1.0;
+    }
+    return Tensor(shape: [n, n], data: data, dtype: dtype);
+  }
+
+  /// Creates a tensor with evenly spaced values
+  factory Tensor.linspace(
+    double start,
+    double end,
+    int steps, {
+    DType dtype = DType.float64,
+  }) {
+    if (steps < 2) {
+      throw ArgumentError('Steps must be at least 2');
+    }
+    final step = (end - start) / (steps - 1);
+    return Tensor(
+      shape: [steps],
+      data: List.generate(steps, (i) => start + i * step),
+      dtype: dtype,
+    );
+  }
+
+  /// Creates a tensor with values in a range
+  factory Tensor.arange(
+    double start,
+    double end, {
+    double step = 1.0,
+    DType dtype = DType.float64,
+  }) {
+    final data = <double>[];
+    for (var v = start; v < end; v += step) {
+      data.add(v);
+    }
+    return Tensor(shape: [data.length], data: data, dtype: dtype);
+  }
+
+  /// Creates a tensor from JSON
+  factory Tensor.fromJson(Map<String, dynamic> json) {
+    final shape = (json['shape'] as List).cast<int>();
+    final rawData = json['data'];
+
+    List<double> data;
+    if (rawData is String) {
+      // Base64-encoded binary data
+      final bytes = base64Decode(rawData);
+      data = Float64List.view(bytes.buffer).toList();
+    } else if (rawData is List) {
+      data = _flattenList(rawData);
+    } else {
+      throw ArgumentError('Invalid tensor data format');
+    }
+
+    return Tensor(
+      shape: shape,
+      data: data,
+      dtype: DType.fromString(json['dtype'] as String? ?? 'float64'),
+      id: json['id'] as String?,
+    );
+  }
+
+  /// Flattens a nested list to 1D
+  static List<double> _flattenList(List<dynamic> nested) {
+    final result = <double>[];
+    void flatten(dynamic item) {
+      if (item is List) {
+        for (final e in item) {
+          flatten(e);
+        }
+      } else if (item is num) {
+        result.add(item.toDouble());
+      }
+    }
+    flatten(nested);
+    return result;
+  }
+
+  /// Number of dimensions
+  int get ndim => shape.length;
+
+  /// Total number of elements
+  int get size => data.length;
+
+  /// Number of bytes
+  int get nbytes => data.lengthInBytes;
+
+  /// Get element at index (for 1D tensors)
+  double operator [](int index) {
+    if (ndim != 1) {
+      throw StateError('Use at() for multi-dimensional indexing');
+    }
+    return data[index];
+  }
+
+  /// Get element at multi-dimensional index
+  double at(List<int> indices) {
+    if (indices.length != ndim) {
+      throw ArgumentError(
+        'Expected $ndim indices, got ${indices.length}',
+      );
+    }
+    var flatIndex = 0;
+    var stride = 1;
+    for (var i = ndim - 1; i >= 0; i--) {
+      if (indices[i] < 0 || indices[i] >= shape[i]) {
+        throw RangeError('Index ${indices[i]} out of bounds for axis $i '
+            'with size ${shape[i]}');
+      }
+      flatIndex += indices[i] * stride;
+      stride *= shape[i];
+    }
+    return data[flatIndex];
+  }
+
+  /// Reshape tensor to new shape
+  Tensor reshape(List<int> newShape) {
+    final newSize = newShape.fold<int>(1, (a, b) => a * b);
+    if (newSize != size) {
+      throw ArgumentError(
+        'Cannot reshape tensor of size $size to shape $newShape '
+        '(size $newSize)',
+      );
+    }
+    return Tensor.fromTypedData(
+      shape: newShape,
+      data: data,
+      dtype: dtype,
+      id: id,
+    );
+  }
+
+  /// Flatten tensor to 1D
+  Tensor flatten() => reshape([size]);
+
+  /// Transpose tensor (swap last two dimensions)
+  Tensor transpose() {
+    if (ndim < 2) {
+      return this;
+    }
+
+    final newShape = List<int>.from(shape);
+    final tmp = newShape[ndim - 1];
+    newShape[ndim - 1] = newShape[ndim - 2];
+    newShape[ndim - 2] = tmp;
+
+    final newData = Float64List(size);
+    final rows = shape[ndim - 2];
+    final cols = shape[ndim - 1];
+    final batchSize = size ~/ (rows * cols);
+
+    for (var b = 0; b < batchSize; b++) {
+      final offset = b * rows * cols;
+      for (var i = 0; i < rows; i++) {
+        for (var j = 0; j < cols; j++) {
+          newData[offset + j * rows + i] = data[offset + i * cols + j];
+        }
+      }
+    }
+
+    return Tensor.fromTypedData(
+      shape: newShape,
+      data: newData,
+      dtype: dtype,
+    );
+  }
+
+  /// Sum of all elements
+  double sum() => data.fold(0.0, (a, b) => a + b);
+
+  /// Mean of all elements
+  double mean() => sum() / size;
+
+  /// Standard deviation of all elements
+  double std() {
+    final m = mean();
+    final variance = data.fold(0.0, (sum, x) => sum + (x - m) * (x - m)) / size;
+    return math.sqrt(variance);
+  }
+
+  /// Minimum value
+  double min() => data.reduce(math.min);
+
+  /// Maximum value
+  double max() => data.reduce(math.max);
+
+  /// Index of minimum value
+  int argmin() {
+    var minIdx = 0;
+    var minVal = data[0];
+    for (var i = 1; i < size; i++) {
+      if (data[i] < minVal) {
+        minVal = data[i];
+        minIdx = i;
+      }
+    }
+    return minIdx;
+  }
+
+  /// Index of maximum value
+  int argmax() {
+    var maxIdx = 0;
+    var maxVal = data[0];
+    for (var i = 1; i < size; i++) {
+      if (data[i] > maxVal) {
+        maxVal = data[i];
+        maxIdx = i;
+      }
+    }
+    return maxIdx;
+  }
+
+  /// Element-wise addition
+  Tensor add(Tensor other) {
+    _checkShapesMatch(other);
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] + other.data[i];
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// Element-wise subtraction
+  Tensor sub(Tensor other) {
+    _checkShapesMatch(other);
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] - other.data[i];
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// Element-wise multiplication
+  Tensor mul(Tensor other) {
+    _checkShapesMatch(other);
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] * other.data[i];
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// Element-wise division
+  Tensor div(Tensor other) {
+    _checkShapesMatch(other);
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] / other.data[i];
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// Scalar operations
+  Tensor addScalar(double scalar) {
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] + scalar;
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  Tensor mulScalar(double scalar) {
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = data[i] * scalar;
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// Apply function element-wise
+  Tensor map(double Function(double) fn) {
+    final result = Float64List(size);
+    for (var i = 0; i < size; i++) {
+      result[i] = fn(data[i]);
+    }
+    return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+  }
+
+  /// ReLU activation
+  Tensor relu() => map((x) => x > 0 ? x : 0);
+
+  /// Sigmoid activation
+  Tensor sigmoid() => map((x) => 1.0 / (1.0 + math.exp(-x)));
+
+  /// Tanh activation
+  Tensor tanh() => map(math.tanh);
+
+  /// Softmax (for 1D or last axis of 2D)
+  Tensor softmax() {
+    if (ndim == 1) {
+      final maxVal = max();
+      final expData = data.map((x) => math.exp(x - maxVal)).toList();
+      final sumExp = expData.fold(0.0, (a, b) => a + b);
+      return Tensor(
+        shape: shape,
+        data: expData.map((x) => x / sumExp).toList(),
+        dtype: dtype,
+      );
+    } else if (ndim == 2) {
+      final rows = shape[0];
+      final cols = shape[1];
+      final result = Float64List(size);
+
+      for (var i = 0; i < rows; i++) {
+        var maxVal = double.negativeInfinity;
+        for (var j = 0; j < cols; j++) {
+          final v = data[i * cols + j];
+          if (v > maxVal) maxVal = v;
+        }
+
+        var sumExp = 0.0;
+        for (var j = 0; j < cols; j++) {
+          final exp = math.exp(data[i * cols + j] - maxVal);
+          result[i * cols + j] = exp;
+          sumExp += exp;
+        }
+
+        for (var j = 0; j < cols; j++) {
+          result[i * cols + j] /= sumExp;
+        }
+      }
+
+      return Tensor.fromTypedData(shape: shape, data: result, dtype: dtype);
+    }
+    throw UnsupportedError('Softmax only supported for 1D and 2D tensors');
+  }
+
+  void _checkShapesMatch(Tensor other) {
+    if (shape.length != other.shape.length) {
+      throw ArgumentError('Shape mismatch: $shape vs ${other.shape}');
+    }
+    for (var i = 0; i < shape.length; i++) {
+      if (shape[i] != other.shape[i]) {
+        throw ArgumentError('Shape mismatch: $shape vs ${other.shape}');
+      }
+    }
+  }
+
+  /// Convert to JSON for API serialization
+  Map<String, dynamic> toJson() => {
+        'shape': shape,
+        'data': base64Encode(data.buffer.asUint8List()),
+        'dtype': dtype.value,
+        if (id != null) 'id': id,
+      };
+
+  /// Convert to nested list representation
+  List<dynamic> toNestedList() {
+    if (ndim == 1) {
+      return data.toList();
+    }
+
+    List<dynamic> buildNested(int dim, int offset) {
+      if (dim == ndim - 1) {
+        return data.sublist(offset, offset + shape[dim]).toList();
+      }
+
+      final stride =
+          shape.sublist(dim + 1).fold<int>(1, (a, b) => a * b);
+      return List.generate(
+        shape[dim],
+        (i) => buildNested(dim + 1, offset + i * stride),
+      );
+    }
+
+    return buildNested(0, 0);
+  }
+
+  @override
+  String toString() {
+    if (size <= 20) {
+      return 'Tensor(shape: $shape, data: ${toNestedList()})';
+    }
+    return 'Tensor(shape: $shape, dtype: ${dtype.value})';
+  }
+
+  @override
+  bool operator ==(Object other) {
+    if (identical(this, other)) return true;
+    if (other is! Tensor) return false;
+    if (shape.length != other.shape.length) return false;
+    for (var i = 0; i < shape.length; i++) {
+      if (shape[i] != other.shape[i]) return false;
+    }
+    for (var i = 0; i < size; i++) {
+      if (data[i] != other.data[i]) return false;
+    }
+    return true;
+  }
+
+  @override
+  int get hashCode => Object.hash(shape, data);
+}
diff --git a/sdk/flutter/lib/src/types.dart b/sdk/flutter/lib/src/types.dart
new file mode 100644
index 0000000..f4c934d
--- /dev/null
+++ b/sdk/flutter/lib/src/types.dart
@@ -0,0 +1,371 @@
+/// Type definitions for Synor Compute SDK
+library synor_compute.types;
+
+import 'package:collection/collection.dart';
+
+/// Numeric precision for compute operations
+enum Precision {
+  fp64('fp64'),
+  fp32('fp32'),
+  fp16('fp16'),
+  bf16('bf16'),
+  int8('int8'),
+  int4('int4');
+
+  const Precision(this.value);
+  final String value;
+
+  static Precision fromString(String s) =>
+      Precision.values.firstWhere((p) => p.value == s, orElse: () => fp32);
+}
+
+/// Target processor type for compute operations
+enum ProcessorType {
+  cpu('cpu'),
+  gpu('gpu'),
+  tpu('tpu'),
+  npu('npu'),
+  lpu('lpu'),
+  fpga('fpga'),
+  dsp('dsp'),
+  webgpu('webgpu'),
+  wasm('wasm'),
+  auto('auto');
+
+  const ProcessorType(this.value);
+  final String value;
+
+  static ProcessorType fromString(String s) =>
+      ProcessorType.values.firstWhere((p) => p.value == s, orElse: () => auto);
+}
+
+/// Job priority levels
+enum Priority {
+  low('low'),
+  normal('normal'),
+  high('high'),
+  critical('critical');
+
+  const Priority(this.value);
+  final String value;
+
+  static Priority fromString(String s) =>
+      Priority.values.firstWhere((p) => p.value == s, orElse: () => normal);
+}
+
+/// Job execution status
+enum JobStatus {
+  pending('pending'),
+  queued('queued'),
+  running('running'),
+  completed('completed'),
+  failed('failed'),
+  cancelled('cancelled');
+
+  const JobStatus(this.value);
+  final String value;
+
+  bool get isTerminal =>
+      this == completed || this == failed || this == cancelled;
+
+  static JobStatus fromString(String s) =>
+      JobStatus.values.firstWhere((p) => p.value == s, orElse: () => pending);
+}
+
+/// Balancing strategy for load distribution
+enum BalancingStrategy {
+  speed('speed'),
+  energy('energy'),
+  balanced('balanced'),
+  cost('cost'),
+  latency('latency');
+
+  const BalancingStrategy(this.value);
+  final String value;
+
+  static BalancingStrategy fromString(String s) => BalancingStrategy.values
+      .firstWhere((p) => p.value == s, orElse: () => balanced);
+}
+
+/// Tensor data type
+enum DType {
+  float64('float64'),
+  float32('float32'),
+  float16('float16'),
+  bfloat16('bfloat16'),
+  int64('int64'),
+  int32('int32'),
+  int16('int16'),
+  int8('int8'),
+  uint8('uint8'),
+  bool_('bool');
+
+  const DType(this.value);
+  final String value;
+
+  static DType fromString(String s) =>
+      DType.values.firstWhere((p) => p.value == s, orElse: () => float32);
+}
+
+/// Configuration for SDK client
+class SynorConfig {
+  final String apiKey;
+  final String baseUrl;
+  final Duration timeout;
+  final int maxRetries;
+  final ProcessorType defaultProcessor;
+  final Precision defaultPrecision;
+  final Priority defaultPriority;
+
+  const SynorConfig({
+    required this.apiKey,
+    this.baseUrl = 'https://compute.synor.io',
+    this.timeout = const Duration(seconds: 30),
+    this.maxRetries = 3,
+    this.defaultProcessor = ProcessorType.auto,
+    this.defaultPrecision = Precision.fp32,
+    this.defaultPriority = Priority.normal,
+  });
+
+  SynorConfig copyWith({
+    String? apiKey,
+    String? baseUrl,
+    Duration? timeout,
+    int? maxRetries,
+    ProcessorType? defaultProcessor,
+    Precision? defaultPrecision,
+    Priority? defaultPriority,
+  }) {
+    return SynorConfig(
+      apiKey: apiKey ?? this.apiKey,
+      baseUrl: baseUrl ?? this.baseUrl,
+      timeout: timeout ?? this.timeout,
+      maxRetries: maxRetries ?? this.maxRetries,
+      defaultProcessor: defaultProcessor ?? this.defaultProcessor,
+      defaultPrecision: defaultPrecision ?? this.defaultPrecision,
+      defaultPriority: defaultPriority ?? this.defaultPriority,
+    );
+  }
+}
+
+/// Matrix multiplication options
+class MatMulOptions {
+  final Precision? precision;
+  final ProcessorType? processor;
+  final Priority? priority;
+  final bool transposeA;
+  final bool transposeB;
+
+  const MatMulOptions({
+    this.precision,
+    this.processor,
+    this.priority,
+    this.transposeA = false,
+    this.transposeB = false,
+  });
+
+  Map<String, dynamic> toJson() => {
+        if (precision != null) 'precision': precision!.value,
+        if (processor != null) 'processor': processor!.value,
+        if (priority != null) 'priority': priority!.value,
+        'transpose_a': transposeA,
+        'transpose_b': transposeB,
+      };
+}
+
+/// Convolution options
+class Conv2dOptions {
+  final List<int> kernel;
+  final List<int> stride;
+  final List<int> padding;
+  final List<int> dilation;
+  final int groups;
+  final Precision? precision;
+  final ProcessorType? processor;
+  final Priority? priority;
+
+  const Conv2dOptions({
+    this.kernel = const [3, 3],
+    this.stride = const [1, 1],
+    this.padding = const [0, 0],
+    this.dilation = const [1, 1],
+    this.groups = 1,
+    this.precision,
+    this.processor,
+    this.priority,
+  });
+
+  Map<String, dynamic> toJson() => {
+        'kernel': kernel,
+        'stride': stride,
+        'padding': padding,
+        'dilation': dilation,
+        'groups': groups,
+        if (precision != null) 'precision': precision!.value,
+        if (processor != null) 'processor': processor!.value,
+        if (priority != null) 'priority': priority!.value,
+      };
+}
+
+/// Flash attention options
+class AttentionOptions {
+  final int numHeads;
+  final double? scale;
+  final bool causal;
+  final double? dropoutP;
+  final Precision? precision;
+  final ProcessorType? processor;
+  final Priority? priority;
+
+  const AttentionOptions({
+    required this.numHeads,
+    this.scale,
+    this.causal = false,
+    this.dropoutP,
+    this.precision,
+    this.processor,
+    this.priority,
+  });
+
+  Map<String, dynamic> toJson() => {
+        'num_heads': numHeads,
+        if (scale != null) 'scale': scale,
+        'causal': causal,
+        if (dropoutP != null) 'dropout_p': dropoutP,
+        if (precision != null) 'precision': precision!.value,
+        if (processor != null) 'processor': processor!.value,
+        if (priority != null) 'priority': priority!.value,
+      };
+}
+
+/// LLM inference options
+class InferenceOptions {
+  final int maxTokens;
+  final double temperature;
+  final double topP;
+  final int? topK;
+  final double? frequencyPenalty;
+  final double? presencePenalty;
+  final List<String>? stopSequences;
+  final bool stream;
+  final ProcessorType? processor;
+  final Priority? priority;
+
+  const InferenceOptions({
+    this.maxTokens = 256,
+    this.temperature = 0.7,
+    this.topP = 1.0,
+    this.topK,
+    this.frequencyPenalty,
+    this.presencePenalty,
+    this.stopSequences,
+    this.stream = false,
+    this.processor,
+    this.priority,
+  });
+
+  Map<String, dynamic> toJson() => {
+        'max_tokens': maxTokens,
+        'temperature': temperature,
+        'top_p': topP,
+        if (topK != null) 'top_k': topK,
+        if (frequencyPenalty != null) 'frequency_penalty': frequencyPenalty,
+        if (presencePenalty != null) 'presence_penalty': presencePenalty,
+        if (stopSequences != null) 'stop_sequences': stopSequences,
+        'stream': stream,
+        if (processor != null) 'processor': processor!.value,
+        if (priority != null) 'priority': priority!.value,
+      };
+}
+
+/// Pricing information for compute resources
+class PricingInfo {
+  final ProcessorType processor;
+  final double pricePerSecond;
+  final double pricePerGflop;
+  final int availableUnits;
+  final double utilizationPercent;
+  final String region;
+
+  const PricingInfo({
+    required this.processor,
+    required this.pricePerSecond,
+    required this.pricePerGflop,
+    required this.availableUnits,
+    required this.utilizationPercent,
+    required this.region,
+  });
+
+  factory PricingInfo.fromJson(Map<String, dynamic> json) => PricingInfo(
+        processor: ProcessorType.fromString(json['processor'] as String),
+        pricePerSecond: (json['price_per_second'] as num).toDouble(),
+        pricePerGflop: (json['price_per_gflop'] as num).toDouble(),
+        availableUnits: json['available_units'] as int,
+        utilizationPercent: (json['utilization_percent'] as num).toDouble(),
+        region: json['region'] as String,
+      );
+}
+
+/// Compute usage statistics
+class UsageStats {
+  final int totalJobs;
+  final int completedJobs;
+  final int failedJobs;
+  final double totalComputeSeconds;
+  final double totalCost;
+  final Map<ProcessorType, double> costByProcessor;
+
+  const UsageStats({
+    required this.totalJobs,
+    required this.completedJobs,
+    required this.failedJobs,
+    required this.totalComputeSeconds,
+    required this.totalCost,
+    required this.costByProcessor,
+  });
+
+  factory UsageStats.fromJson(Map<String, dynamic> json) {
+    final costMap = <ProcessorType, double>{};
+    final rawCostMap = json['cost_by_processor'] as Map<String, dynamic>?;
+    if (rawCostMap != null) {
+      for (final entry in rawCostMap.entries) {
+        costMap[ProcessorType.fromString(entry.key)] =
+            (entry.value as num).toDouble();
+      }
+    }
+
+    return UsageStats(
+      totalJobs: json['total_jobs'] as int,
+      completedJobs: json['completed_jobs'] as int,
+      failedJobs: json['failed_jobs'] as int,
+      totalComputeSeconds: (json['total_compute_seconds'] as num).toDouble(),
+      totalCost: (json['total_cost'] as num).toDouble(),
+      costByProcessor: costMap,
+    );
+  }
+}
+
+/// Exception thrown by Synor Compute operations
+class SynorException implements Exception {
+  final String message;
+  final String? code;
+  final int? statusCode;
+  final Map<String, dynamic>? details;
+
+  const SynorException(
+    this.message, {
+    this.code,
+    this.statusCode,
+    this.details,
+  });
+
+  @override
+  String toString() => 'SynorException: $message (code: $code)';
+
+  factory SynorException.fromJson(Map<String, dynamic> json) => SynorException(
+        json['message'] as String? ?? 'Unknown error',
+        code: json['code'] as String?,
+        statusCode: json['status_code'] as int?,
+        details: json['details'] as Map<String, dynamic>?,
+      );
+}
diff --git a/sdk/flutter/lib/synor_compute.dart b/sdk/flutter/lib/synor_compute.dart
new file mode 100644
index 0000000..81f5228
--- /dev/null
+++ b/sdk/flutter/lib/synor_compute.dart
@@ -0,0 +1,92 @@
+/// Synor Compute SDK for Flutter/Dart
+///
+/// A high-performance SDK for distributed heterogeneous computing.
+/// Supports CPU, GPU, TPU, NPU, LPU, FPGA, DSP, WebGPU, and WASM processors.
+///
+/// ## Quick Start
+///
+/// ```dart
+/// import 'package:synor_compute/synor_compute.dart';
+///
+/// void main() async {
+///   // Create client
+///   final client = SynorCompute(apiKey: 'your-api-key');
+///
+///   // Matrix multiplication
+///   final a = Tensor.rand([512, 512]);
+///   final b = Tensor.rand([512, 512]);
+///   final result = await client.matmul(a, b, options: MatMulOptions(
+///     precision: Precision.fp16,
+///     processor: ProcessorType.gpu,
+///   ));
+///
+///   print('Result shape: ${result.result!.shape}');
+///   print('Execution time: ${result.executionTimeMs}ms');
+///
+///   // LLM Inference
+///   final response = await client.inference(
+///     'llama-3-70b',
+///     'Explain quantum computing',
+///     options: InferenceOptions(maxTokens: 256),
+///   );
+///   print(response.result);
+///
+///   // Streaming inference
+///   await for (final token in client.inferenceStream(
+///     'llama-3-70b',
+///     'Write a haiku about computing',
+///   )) {
+///     stdout.write(token);
+///   }
+///
+///   // Clean up
+///   client.dispose();
+/// }
+/// ```
+///
+/// ## Features
+///
+/// - **Matrix Operations**: matmul, conv2d, attention, elementwise, reduce
+/// - **LLM Inference**: Standard and streaming inference
+/// - **Tensor Management**: Upload, download, and delete tensors
+/// - **Job Management**: Submit, poll, cancel, and list jobs
+/// - **Pricing**: Get real-time pricing for all processor types
+/// - **Usage Statistics**: Track compute usage and costs
+///
+/// ## Supported Processors
+///
+/// | Processor | Best For |
+/// |-----------|----------|
+/// | CPU | General compute, small batches |
+/// | GPU | Large matrix operations, training |
+/// | TPU | Tensor operations, inference |
+/// | NPU | Neural network inference |
+/// | LPU | Large language model inference |
+/// | FPGA | Custom operations, low latency |
+/// | DSP | Signal processing |
+/// | WebGPU | Browser-based compute |
+/// | WASM | Portable compute |
+library synor_compute;
+
+export 'src/types.dart'
+    show
+        Precision,
+        ProcessorType,
+        Priority,
+        JobStatus,
+        BalancingStrategy,
+        DType,
+        SynorConfig,
+        MatMulOptions,
+        Conv2dOptions,
+        AttentionOptions,
+        InferenceOptions,
+        PricingInfo,
+        UsageStats,
+        SynorException;
+
+export 'src/tensor.dart' show Tensor;
+
+export 'src/job.dart' show JobResult, JobStatusUpdate, Job, JobBatch;
+
+export 'src/client.dart' show SynorCompute;
diff --git a/sdk/flutter/pubspec.yaml b/sdk/flutter/pubspec.yaml
new file mode 100644
index 0000000..eaeea06
--- /dev/null
+++ b/sdk/flutter/pubspec.yaml
@@ -0,0 +1,37 @@
+name: synor_compute
+description: Flutter/Dart SDK for Synor Compute - distributed heterogeneous computing platform
+version: 0.1.0
+homepage: https://github.com/mrgulshanyadav/Blockchain.cc
+repository: https://github.com/mrgulshanyadav/Blockchain.cc/tree/main/sdk/flutter
+issue_tracker: https://github.com/mrgulshanyadav/Blockchain.cc/issues
+
+environment:
+  sdk: '>=3.0.0 <4.0.0'
+  flutter: '>=3.10.0'
+
+dependencies:
+  flutter:
+    sdk: flutter
+  http: ^1.1.0
+  web_socket_channel: ^2.4.0
+  json_annotation: ^4.8.1
+  crypto: ^3.0.3
+  collection: ^1.18.0
+
+dev_dependencies:
+  flutter_test:
+    sdk: flutter
+  flutter_lints: ^3.0.0
+  build_runner: ^2.4.0
+  json_serializable: ^6.7.0
+  mockito: ^5.4.0
+
+flutter:
+
+platforms:
+  android:
+  ios:
+  linux:
+  macos:
+  web:
+  windows: