diff --git a/docs/build-time-augmentations.md b/docs/build-time-augmentations.md
deleted file mode 100644
index 8d401bd2..00000000
--- a/docs/build-time-augmentations.md
+++ /dev/null
@@ -1,217 +0,0 @@
-<div align="center">
-<img src="../brainy.png" alt="Brainy Logo" width="200"/>
-
-# Build-Time Augmentation Registration
-</div>
-
-This document explains how to register custom augmentations at build time using the Brainy library's augmentation
-registry system.
-
-## Overview
-
-Brainy provides a system for registering custom augmentations at build time, similar to Angular's pipeline. This allows
-you to:
-
-1. Define custom augmentations in your project
-2. Register them with the Brainy library during the build process
-3. Have them automatically available when your application runs
-
-This approach has several advantages:
-
-- Better performance as augmentations are available immediately at startup
-- Improved tree-shaking and bundle optimization
-- Type safety and better IDE support
-- No need for dynamic imports or async loading
-
-## Creating Custom Augmentations
-
-To create a custom augmentation, you need to implement one of the augmentation interfaces provided by Brainy:
-
-```typescript
-// myTextSenseAugmentation.ts
-import {
-    registerAugmentation,
-    AugmentationType,
-    BrainyAugmentations
-} from 'brainy';
-
-// Define a custom sense augmentation
-class MyTextSenseAugmentation implements BrainyAugmentations.ISenseAugmentation {
-    name = 'MyTextSenseAugmentation';
-    enabled = true;
-    type = AugmentationType.SENSE;
-
-    // Required IAugmentation methods
-    async initialize() {
-        console.log('Initializing MyTextSenseAugmentation');
-        return true;
-    }
-
-    async shutDown() {
-        console.log('Shutting down MyTextSenseAugmentation');
-        return true;
-    }
-
-    getStatus() {
-        return {
-            name: this.name,
-            enabled: this.enabled,
-            type: this.type,
-            status: 'ready'
-        };
-    }
-
-    // ISenseAugmentation methods
-    async processRawData(rawData, dataType) {
-        console.log(`Processing ${dataType} data`);
-
-        // Your implementation here
-
-        return {
-            success: true,
-            data: { /* processed data */}
-        };
-    }
-
-    async listenToFeed(feedUrl, callback) {
-        console.log(`Listening to feed at ${feedUrl}`);
-
-        // Your implementation here
-
-        return {
-            success: true,
-            data: { /* feed info */}
-        };
-    }
-}
-
-// Register the augmentation with the registry
-// This will make it available to the Brainy library at runtime
-export const myTextSenseAugmentation = registerAugmentation(new MyTextSenseAugmentation());
-```
-
-## Registering Augmentations
-
-There are two ways to register augmentations:
-
-### 1. Manual Registration
-
-You can manually register augmentations by calling `registerAugmentation()` in your code:
-
-```typescript
-import {registerAugmentation} from 'brainy';
-import {MyCustomAugmentation} from './myCustomAugmentation';
-
-// Register the augmentation
-const myAugmentation = registerAugmentation(new MyCustomAugmentation());
-
-// You can also export it for use elsewhere in your application
-export {myAugmentation};
-```
-
-### 2. Automatic Registration with Build Tools
-
-For larger projects, you can use build tools like webpack or rollup to automatically discover and register
-augmentations:
-
-#### With Webpack
-
-```javascript
-// webpack.config.js
-const {createAugmentationRegistryPlugin} = require('brainy');
-
-module.exports = {
-    // ... other webpack config
-    plugins: [
-        createAugmentationRegistryPlugin({
-            // Pattern to match files containing augmentations
-            pattern: /augmentation\.(js|ts)$/,
-            options: {
-                autoInitialize: true,
-                debug: true
-            }
-        })
-    ]
-};
-```
-
-#### With Rollup
-
-```javascript
-// rollup.config.js
-import {createAugmentationRegistryRollupPlugin} from 'brainy';
-
-export default {
-    // ... other rollup config
-    plugins: [
-        createAugmentationRegistryRollupPlugin({
-            pattern: /augmentation\.(js|ts)$/,
-            options: {
-                autoInitialize: true,
-                debug: true
-            }
-        })
-    ]
-};
-```
-
-## Using Registered Augmentations
-
-Once augmentations are registered, they are automatically available to the Brainy library. You can use them through the
-augmentation pipeline:
-
-```typescript
-import {BrainyData, augmentationPipeline, initializeAugmentationPipeline} from 'brainy';
-
-// Create a new BrainyData instance
-const db = new BrainyData();
-await db.init();
-
-// Initialize the augmentation pipeline with all registered augmentations
-initializeAugmentationPipeline();
-
-// Use the pipeline to execute augmentations
-const senseResults = await augmentationPipeline.executeSensePipeline(
-    'processRawData',
-    ['This is some example text to process', 'text']
-);
-
-// Process the results
-for (const resultPromise of senseResults) {
-    const result = await resultPromise;
-    if (result.success) {
-        console.log('Processed data:', result.data);
-    }
-}
-```
-
-## Best Practices
-
-1. **Naming Convention**: Use a consistent naming convention for your augmentation files, such as ending them with
-   `augmentation.ts` or `augmentation.js`.
-
-2. **File Organization**: Keep your augmentations organized in a dedicated directory, such as `src/augmentations/`.
-
-3. **Type Safety**: Implement the appropriate interfaces for your augmentations to ensure type safety.
-
-4. **Documentation**: Document your augmentations with JSDoc comments to provide clear usage instructions.
-
-5. **Testing**: Write tests for your augmentations to ensure they work correctly.
-
-## Troubleshooting
-
-If your augmentations are not being registered or are not working as expected, check the following:
-
-1. Make sure your augmentation implements all required methods from the interface.
-2. Verify that your augmentation is being registered with `registerAugmentation()`.
-3. If using build tools, check that your file naming matches the pattern specified in the plugin configuration.
-4. Enable debug logging in the plugin options to see detailed information about the registration process.
-5. Check the console for any error messages during initialization.
-
-## Examples
-
-For complete examples, see:
-
-- [Basic Augmentation Registration](../examples/buildTimeRegistration.js)
-- [Webpack Configuration](../examples/webpack.config.js)
-- [Rollup Configuration](../examples/rollup.config.js)
diff --git a/docs/llm-augmentation.md b/docs/llm-augmentation.md
deleted file mode 100644
index 793b87ea..00000000
--- a/docs/llm-augmentation.md
+++ /dev/null
@@ -1,443 +0,0 @@
-<div align="center">
-<img src="../brainy.png" alt="Brainy Logo" width="200"/>
-
-# LLM Augmentation for Brainy
-</div>
-
-This document describes the LLM (Language Learning Model) augmentation for Brainy, which enables creating, training, testing, exporting, and deploying language models from the data in Brainy's graph database.
-
-## Overview
-
-The LLM augmentation extends Brainy with the ability to create and train language models using the nouns and verbs stored in the graph database. It leverages TensorFlow.js to provide cross-platform compatibility, working in both browser and Node.js environments.
-
-The augmentation consists of two main components:
-
-1. **LLMCognitionAugmentation**: A cognition augmentation that provides the core functionality for creating, training, testing, exporting, and deploying LLM models.
-2. **LLMActivationAugmentation**: An activation augmentation that provides action triggers for the LLM functionality, making it accessible through the augmentation pipeline.
-
-## Features
-
-- **Create LLM Models**: Create simple sequence models or transformer models with customizable parameters.
-- **Train Models**: Train models on the nouns and verbs in Brainy's database with configurable training options.
-- **Test Models**: Evaluate model performance and generate sample predictions.
-- **Export Models**: Export trained models in various formats (TFJS, JSON) for use in different environments.
-- **Deploy Models**: Deploy models to browser, Node.js, or cloud environments.
-- **Generate Text**: Use trained models to generate text based on input prompts.
-
-## Installation
-
-The LLM augmentation is included as an optional component in the Brainy package. No additional installation is required if you have already installed Brainy.
-
-```bash
-npm install @soulcraft/brainy --legacy-peer-deps
-```
-
-## Usage
-
-### Simplified Usage (Recommended for New Users)
-
-```javascript
-import { BrainyData, augmentationPipeline } from '@soulcraft/brainy'
-import { createLLMAugmentations } from '@soulcraft/brainy/src/augmentations/llmAugmentations.js'
-
-// Initialize Brainy
-const db = new BrainyData()
-await db.init()
-
-// Create LLM augmentations
-const { cognition, activation } = await createLLMAugmentations({
-  cognitionName: 'my-llm-cognition',
-  activationName: 'my-llm-activation',
-  brainyDb: db
-})
-
-// Register augmentations with the pipeline
-augmentationPipeline.register(cognition)
-augmentationPipeline.register(activation)
-
-// Create a model using a preset (tiny, small, medium, large)
-const createResult = await cognition.createModelFromPreset('small', 'my-model')
-
-const modelId = createResult.data.modelId
-
-// Train the model with simplified options (quick, standard, thorough)
-await cognition.trainModelSimple(modelId, 'standard')
-
-// Generate text with simplified creativity options (conservative, balanced, creative)
-const generateResult = await cognition.generateTextSimple(modelId, 'What is a', 'balanced')
-
-console.log(`Generated text: ${generateResult.data.text}`)
-```
-
-### Advanced Usage
-
-```javascript
-import { BrainyData, augmentationPipeline } from '@soulcraft/brainy'
-import { createLLMAugmentations } from '@soulcraft/brainy/src/augmentations/llmAugmentations.js'
-
-// Initialize Brainy
-const db = new BrainyData()
-await db.init()
-
-// Create LLM augmentations
-const { cognition, activation } = await createLLMAugmentations({
-  cognitionName: 'my-llm-cognition',
-  activationName: 'my-llm-activation',
-  brainyDb: db
-})
-
-// Register augmentations with the pipeline
-augmentationPipeline.register(cognition)
-augmentationPipeline.register(activation)
-
-// Create a model with advanced configuration
-const createResult = await cognition.createModel({
-  name: 'my-model',
-  modelType: 'simple',
-  vocabSize: 5000,
-  embeddingDim: 64,
-  hiddenDim: 128,
-  numLayers: 1
-})
-
-const modelId = createResult.data.modelId
-
-// Train the model with advanced options
-await cognition.trainModel(modelId, {
-  maxSamples: 100,
-  validationSplit: 0.2
-})
-
-// Generate text with advanced options
-const generateResult = await cognition.generateText(modelId, 'What is a', {
-  temperature: 0.7
-})
-
-console.log(`Generated text: ${generateResult.data.text}`)
-```
-
-### Using the Augmentation Pipeline
-
-```javascript
-// Create a model through the pipeline
-const pipelineCreateResult = await augmentationPipeline.executeCognitionPipeline(
-  'createModel',
-  [{
-    name: 'pipeline-model',
-    modelType: 'transformer',
-    numHeads: 2,
-    numLayers: 1
-  }]
-)
-
-if (pipelineCreateResult[0] && (await pipelineCreateResult[0]).success) {
-  const pipelineModelId = (await pipelineCreateResult[0]).data.modelId
-
-  // Train the model through the pipeline
-  await augmentationPipeline.executeCognitionPipeline(
-    'trainModel',
-    [pipelineModelId, { maxSamples: 50 }]
-  )
-}
-```
-
-## CLI Usage
-
-The LLM functionality is also available through the Brainy CLI. The CLI provides both simplified and advanced commands for working with LLM models.
-
-### Simplified CLI Commands (Recommended for New Users)
-
-```bash
-# Create a model using a preset (tiny, small, medium, large)
-brainy llm create-simple [preset] --name my-model
-
-# Train a model with simplified options (quick, standard, thorough)
-brainy llm train-simple <modelId> [level]
-
-# Generate text with simplified creativity options (conservative, balanced, creative)
-brainy llm generate-simple <modelId> "Your prompt here" [creativity]
-```
-
-### Advanced CLI Commands
-
-```bash
-# Create a model with advanced configuration
-brainy llm create --name my-model --type simple --vocab-size 5000
-
-# Train a model with advanced options
-brainy llm train <modelId> --max-samples 100 --epochs 10
-
-# Test a model
-brainy llm test <modelId> --generate-samples
-
-# Export a model
-brainy llm export <modelId> --format json --include-metadata
-
-# Deploy a model
-brainy llm deploy <modelId> --target browser
-
-# Generate text with advanced options
-brainy llm generate <modelId> "Your prompt here" --temperature 0.7
-```
-
-## API Reference
-
-### LLMCognitionAugmentation
-
-#### Simplified Methods (Recommended for New Users)
-
-##### Creating Models with Presets
-
-```javascript
-createModelFromPreset(presetName: string = 'small', customName?: string): Promise<AugmentationResponse<{ modelId: string, metadata: LLMModelMetadata }>>
-```
-
-Creates a new LLM model using a predefined preset.
-
-**Parameters:**
-- `presetName`: Name of the preset to use ('tiny', 'small', 'medium', 'large')
-- `customName`: Optional custom name for the model
-
-**Returns:**
-- `modelId`: Unique identifier for the created model
-- `metadata`: Metadata about the model
-
-**Preset Details:**
-- `tiny`: Simple model with minimal parameters, fast but less accurate
-- `small`: Simple model with balanced parameters, good for most use cases
-- `medium`: Transformer model with moderate parameters, better accuracy
-- `large`: Transformer model with more parameters, best accuracy but slower
-
-##### Training Models with Simplified Options
-
-```javascript
-trainModelSimple(modelId: string, trainingLevel: 'quick' | 'standard' | 'thorough' = 'standard'): Promise<AugmentationResponse<{ modelId: string, metadata: LLMModelMetadata, trainingHistory: tf.History, metrics?: { loss: number, accuracy: number, epochs: number } }>>
-```
-
-Trains an LLM model with simplified options.
-
-**Parameters:**
-- `modelId`: ID of the model to train
-- `trainingLevel`: Training level ('quick', 'standard', 'thorough')
-
-**Returns:**
-- `modelId`: ID of the trained model
-- `metadata`: Updated metadata about the model
-- `trainingHistory`: Training history with metrics
-- `metrics`: Training metrics (loss, accuracy, epochs)
-
-**Training Level Details:**
-- `quick`: Faster training with fewer samples (100 samples, 5 epochs)
-- `standard`: Balanced training (500 samples, 10 epochs)
-- `thorough`: More thorough training for better results (1000 samples, 20 epochs)
-
-##### Generating Text with Simplified Creativity Options
-
-```javascript
-generateTextSimple(modelId: string, prompt: string, creativity: 'conservative' | 'balanced' | 'creative' = 'balanced'): Promise<AugmentationResponse<{ text: string, tokens?: number, timeMs?: number }>>
-```
-
-Generates text using the LLM model with simplified creativity options.
-
-**Parameters:**
-- `modelId`: ID of the model to use
-- `prompt`: Input prompt for text generation
-- `creativity`: Creativity level ('conservative', 'balanced', 'creative')
-
-**Returns:**
-- `text`: Generated text
-- `tokens`: Number of tokens generated (if available)
-- `timeMs`: Generation time in milliseconds (if available)
-
-**Creativity Level Details:**
-- `conservative`: More predictable, focused output (temperature: 0.3, topK: 3)
-- `balanced`: Mix of predictability and creativity (temperature: 0.7, topK: 5)
-- `creative`: More varied, unexpected output (temperature: 1.0, topK: 10)
-
-#### Advanced Methods
-
-##### Creating Models
-
-```javascript
-createModel(config: Partial<LLMModelConfig>): Promise<AugmentationResponse<{ modelId: string, metadata: LLMModelMetadata }>>
-```
-
-Creates a new LLM model with the specified configuration.
-
-**Parameters:**
-- `config`: Configuration options for the model
-  - `name`: Name of the model (optional, auto-generated if not provided)
-  - `description`: Description of the model (optional)
-  - `modelType`: Type of model to create ('simple', 'transformer', or 'custom')
-  - `vocabSize`: Size of the vocabulary (default: 10000)
-  - `embeddingDim`: Dimension of the embedding vectors (default: 128)
-  - `hiddenDim`: Dimension of the hidden layers (default: 256)
-  - `numLayers`: Number of layers in the model (default: 2)
-  - `numHeads`: Number of attention heads for transformer models (default: 4)
-  - `dropoutRate`: Dropout rate for regularization (default: 0.1)
-  - `maxSequenceLength`: Maximum sequence length for input (default: 100)
-  - `learningRate`: Learning rate for training (default: 0.001)
-  - `batchSize`: Batch size for training (default: 32)
-  - `epochs`: Number of training epochs (default: 10)
-  - `customModelPath`: Path to a custom model (required for 'custom' modelType)
-
-**Returns:**
-- `modelId`: Unique identifier for the created model
-- `metadata`: Metadata about the model
-
-#### Training Models
-
-```javascript
-trainModel(modelId: string, options: LLMTrainingOptions): Promise<AugmentationResponse<{ modelId: string, metadata: LLMModelMetadata, trainingHistory: tf.History }>>
-```
-
-Trains an LLM model on Brainy data.
-
-**Parameters:**
-- `modelId`: ID of the model to train
-- `options`: Training options
-  - `nounTypes`: Types of nouns to include in training (default: all)
-  - `verbTypes`: Types of verbs to include in training (default: all)
-  - `maxSamples`: Maximum number of training samples (default: all)
-  - `validationSplit`: Fraction of data to use for validation (default: 0.2)
-  - `includeMetadata`: Whether to include metadata in training (default: false)
-  - `includeEmbeddings`: Whether to include embeddings in training (default: false)
-  - `augmentData`: Whether to augment training data (default: false)
-  - `earlyStoppingPatience`: Number of epochs with no improvement before stopping (default: 3)
-
-**Returns:**
-- `modelId`: ID of the trained model
-- `metadata`: Updated metadata about the model
-- `trainingHistory`: Training history with metrics
-
-#### Testing Models
-
-```javascript
-testModel(modelId: string, options: LLMTestingOptions): Promise<AugmentationResponse<{ modelId: string, metrics: Record<string, number>, samples?: Array<{ input: string, expected: string, generated: string }> }>>
-```
-
-Tests an LLM model on Brainy data.
-
-**Parameters:**
-- `modelId`: ID of the model to test
-- `options`: Testing options
-  - `testSize`: Number of test samples (default: 100)
-  - `randomSeed`: Random seed for reproducibility (optional)
-  - `metrics`: Metrics to calculate (default: ['accuracy', 'loss'])
-  - `generateSamples`: Whether to generate sample predictions (default: false)
-  - `sampleCount`: Number of samples to generate (default: 5)
-
-**Returns:**
-- `modelId`: ID of the tested model
-- `metrics`: Test metrics (accuracy, loss, etc.)
-- `samples`: Sample predictions (if generateSamples is true)
-
-#### Exporting Models
-
-```javascript
-exportModel(modelId: string, options: LLMExportOptions): Promise<AugmentationResponse<{ modelId: string, format: string, exportPath?: string, modelJSON?: string }>>
-```
-
-Exports an LLM model for deployment.
-
-**Parameters:**
-- `modelId`: ID of the model to export
-- `options`: Export options
-  - `format`: Export format ('tfjs', 'onnx', 'savedmodel', 'json')
-  - `quantize`: Whether to quantize the model (default: false)
-  - `outputPath`: Path to save the exported model (optional)
-  - `includeMetadata`: Whether to include metadata (default: false)
-  - `includeVocab`: Whether to include vocabulary (default: false)
-
-**Returns:**
-- `modelId`: ID of the exported model
-- `format`: Export format
-- `exportPath`: Path where the model was saved (if outputPath was provided)
-- `modelJSON`: JSON representation of the model (if outputPath was not provided)
-
-#### Deploying Models
-
-```javascript
-deployModel(modelId: string, options: LLMDeploymentOptions): Promise<AugmentationResponse<{ modelId: string, deploymentTarget: string, deploymentUrl?: string, status: string }>>
-```
-
-Deploys an LLM model to the specified target.
-
-**Parameters:**
-- `modelId`: ID of the model to deploy
-- `options`: Deployment options
-  - `target`: Deployment target ('browser', 'node', 'cloud')
-  - `cloudProvider`: Cloud provider for cloud deployment ('aws', 'gcp', 'azure')
-  - `endpoint`: Endpoint URL for cloud deployment
-  - `apiKey`: API key for cloud deployment
-  - `region`: Region for cloud deployment
-  - `containerize`: Whether to containerize the model (default: false)
-  - `autoScale`: Whether to enable auto-scaling (default: false)
-  - `memory`: Memory allocation for deployment
-  - `cpu`: CPU allocation for deployment
-
-**Returns:**
-- `modelId`: ID of the deployed model
-- `deploymentTarget`: Target where the model was deployed
-- `deploymentUrl`: URL where the model is accessible (for cloud deployments)
-- `status`: Deployment status
-
-#### Generating Text
-
-```javascript
-generateText(modelId: string, prompt: string, options: { maxLength?: number, temperature?: number, topK?: number }): Promise<AugmentationResponse<string>>
-```
-
-Generates text using the LLM model.
-
-**Parameters:**
-- `modelId`: ID of the model to use
-- `prompt`: Input prompt for text generation
-- `options`: Generation options
-  - `maxLength`: Maximum length of generated text (default: model-dependent)
-  - `temperature`: Temperature for sampling (default: 1.0)
-  - `topK`: Number of top tokens to consider (default: all)
-
-**Returns:**
-- Generated text
-
-### LLMActivationAugmentation
-
-The activation augmentation provides action triggers for the LLM functionality, making it accessible through the augmentation pipeline. It supports the following actions:
-
-- `createModel`: Creates a new LLM model
-- `trainModel`: Trains an LLM model
-- `testModel`: Tests an LLM model
-- `exportModel`: Exports an LLM model
-- `deployModel`: Deploys an LLM model
-- `generateText`: Generates text using an LLM model
-
-## Model Types
-
-### Simple Sequence Model
-
-A simple sequence model with an embedding layer, LSTM layers, and a dense output layer. This model is suitable for simpler language modeling tasks and requires less computational resources.
-
-### Transformer Model
-
-A transformer model with multi-head attention, suitable for more complex language modeling tasks. This model can capture longer-range dependencies in text but requires more computational resources.
-
-## Limitations
-
-- The current implementation is focused on training small language models suitable for specific domains rather than large general-purpose models.
-- Training large models in the browser may be limited by available memory and computational resources.
-- Cloud deployment functionality is a placeholder and requires additional implementation for specific cloud providers.
-- ONNX export is not implemented in the current version.
-
-## Examples
-
-See the [llmAugmentationExample.js](../examples/llmAugmentationExample.js) file for a complete example of using the LLM augmentation.
-
-## Future Enhancements
-
-- Support for larger model architectures
-- Improved training efficiency for browser environments
-- Full implementation of cloud deployment options
-- Support for ONNX export
-- Fine-tuning of pre-trained models
-- More advanced text generation capabilities
diff --git a/rollup.unified.js b/rollup.unified.js
deleted file mode 100644
index d4b0583b..00000000
--- a/rollup.unified.js
+++ /dev/null
@@ -1,154 +0,0 @@
-import typescript from '@rollup/plugin-typescript';
-import resolve from '@rollup/plugin-node-resolve';
-import commonjs from '@rollup/plugin-commonjs';
-import json from '@rollup/plugin-json';
-import { terser } from 'rollup-plugin-terser';
-import replace from '@rollup/plugin-replace';
-
-// Custom plugin to provide empty shims for Node.js built-in modules in browser environments
-const nodeModuleShims = () => {
-  return {
-    name: 'node-module-shims',
-    resolveId(source) {
-      // List of Node.js built-in modules to shim
-      const nodeBuiltins = [
-        'fs', 'path', 'util', 'crypto', 'os', 'stream', 
-        'http', 'http2', 'https', 'zlib', 'child_process'
-      ];
-
-      if (nodeBuiltins.includes(source)) {
-        // Return a virtual module ID for the shim
-        return `\0${source}-shim`;
-      }
-      return null;
-    },
-    load(id) {
-      // If this is one of our shims, return an empty module
-      if (id.startsWith('\0') && id.endsWith('-shim')) {
-        console.log(`Providing empty shim for Node.js module: ${id.slice(1, -5)}`);
-        return 'export default {}; export const promises = {};';
-      }
-      return null;
-    }
-  };
-};
-
-// Custom plugin to fix 'this' references in specific files
-const fixThisReferences = () => {
-  return {
-    name: 'fix-this-references',
-    transform(code, id) {
-      // Only transform the specific files that have issues with 'this'
-      if (id.includes('@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js')) {
-        // Replace 'this' with 'globalThis' in the problematic code
-        return {
-          code: code.replace(/\bthis\b/g, 'globalThis'),
-          map: { mappings: '' } // Provide an empty sourcemap to avoid warnings
-        };
-      }
-      return null; // Return null to let Rollup handle other files normally
-    }
-  };
-};
-
-export default {
-  input: 'src/unified.ts', // Use the unified entry point
-  inlineDynamicImports: true,
-  output: [
-    {
-      file: 'dist/unified.js',
-      format: 'es',
-      sourcemap: true,
-      intro: `
-// Environment detection
-const isBrowser = typeof window !== 'undefined';
-const isNode = typeof process !== 'undefined' && process.versions && process.versions.node;
-const isServerless = !isBrowser && !isNode;
-
-// Global Buffer polyfill for browser environments
-if (isBrowser) {
-  try {
-    // Import Buffer from the buffer package
-    import('buffer').then(({ Buffer }) => {
-      globalThis.Buffer = Buffer;
-    }).catch(err => {
-      console.warn('Failed to load Buffer polyfill:', err);
-    });
-  } catch (e) {
-    console.warn('Failed to import buffer package:', e);
-  }
-}
-
-// Global variable to store environment
-globalThis.__ENV__ = {
-  isBrowser,
-  isNode,
-  isServerless
-};
-      `
-    },
-    {
-      file: 'dist/unified.min.js',
-      format: 'es',
-      sourcemap: true,
-      plugins: [terser()],
-      intro: `
-// Environment detection
-const isBrowser = typeof window !== 'undefined';
-const isNode = typeof process !== 'undefined' && process.versions && process.versions.node;
-const isServerless = !isBrowser && !isNode;
-
-// Global Buffer polyfill for browser environments
-if (isBrowser) {
-  try {
-    // Import Buffer from the buffer package
-    import('buffer').then(({ Buffer }) => {
-      globalThis.Buffer = Buffer;
-    }).catch(err => {
-      console.warn('Failed to load Buffer polyfill:', err);
-    });
-  } catch (e) {
-    console.warn('Failed to import buffer package:', e);
-  }
-}
-
-// Global variable to store environment
-globalThis.__ENV__ = {
-  isBrowser,
-  isNode,
-  isServerless
-};
-      `
-    }
-  ],
-  plugins: [
-    // Add environment replacement
-    replace({
-      preventAssignment: true,
-      'process.env.NODE_ENV': JSON.stringify('production')
-    }),
-    // Add our custom plugins
-    fixThisReferences(),
-    nodeModuleShims(),
-    resolve({
-      browser: true,
-      preferBuiltins: false
-    }),
-    commonjs({
-      transformMixedEsModules: true
-    }),
-    json(),
-    typescript({
-      tsconfig: './tsconfig.unified.json',
-      declaration: true,
-      declarationMap: true
-    })
-  ],
-  external: [
-    // Add any dependencies you want to exclude from the bundle
-    '@aws-sdk/client-s3',
-    '@smithy/util-stream',
-    '@smithy/node-http-handler',
-    '@aws-crypto/crc32c'
-  ]
-};
diff --git a/rollup_config.js b/rollup_config.js
deleted file mode 100644
index c699c872..00000000
--- a/rollup_config.js
+++ /dev/null
@@ -1,98 +0,0 @@
-import typescript from '@rollup/plugin-typescript'
-import resolve from '@rollup/plugin-node-resolve'
-import commonjs from '@rollup/plugin-commonjs'
-import json from '@rollup/plugin-json'
-import { terser } from 'rollup-plugin-terser'
-
-// Custom plugin to fix 'this' references in specific files
-const fixThisReferences = () => {
-  return {
-    name: 'fix-this-references',
-    transform(code, id) {
-      // Only transform the specific files that have issues with 'this'
-      if (id.includes('@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js')) {
-        // Replace 'this' with 'globalThis' in the problematic code
-        return {
-          code: code.replace(/\bthis\b/g, 'globalThis'),
-          map: { mappings: '' } // Provide an empty sourcemap to avoid warnings
-        };
-      }
-      return null; // Return null to let Rollup handle other files normally
-    }
-  };
-};
-
-// Custom plugin to provide empty shims for Node.js built-in modules
-const nodeModuleShims = () => {
-  return {
-    name: 'node-module-shims',
-    resolveId(source) {
-      // List of Node.js built-in modules to shim
-      const nodeBuiltins = [
-        'fs', 'path', 'util', 'crypto', 'os', 'stream', 
-        'http', 'http2', 'https', 'zlib', 'child_process'
-      ];
-
-      if (nodeBuiltins.includes(source)) {
-        // Return a virtual module ID for the shim
-        return `\0${source}-shim`;
-      }
-      return null;
-    },
-    load(id) {
-      // If this is one of our shims, return an empty module
-      if (id.startsWith('\0') && id.endsWith('-shim')) {
-        console.log(`Providing empty shim for Node.js module: ${id.slice(1, -5)}`);
-        return 'export default {}; export const promises = {};';
-      }
-      return null;
-    }
-  };
-};
-
-export default {
-  input: 'examples/browser_compatible_exports.ts', // Browser-compatible entry point
-  inlineDynamicImports: true,
-  output: [
-    {
-      file: 'dist/brainy.js',
-      format: 'es',
-      sourcemap: true,
-      intro: 'var global = typeof window !== "undefined" ? window : this;',
-    },
-    {
-      file: 'dist/brainy.min.js',
-      format: 'es',
-      sourcemap: true,
-      intro: 'var global = typeof window !== "undefined" ? window : this;',
-      plugins: [terser()]
-    }
-  ],
-  plugins: [
-    // Add our custom plugins first to ensure they run before other transformations
-    fixThisReferences(),
-    nodeModuleShims(),
-    resolve({
-      browser: true,
-      preferBuiltins: false
-    }),
-    commonjs({
-      transformMixedEsModules: true
-    }),
-    json(),
-    typescript({
-      tsconfig: './tsconfig.browser.json',
-      declaration: false,
-      declarationMap: false
-    })
-  ],
-  external: [
-    // Add any dependencies you want to exclude from the bundle
-    // AWS SDK modules with circular dependencies
-    '@aws-sdk/client-s3',
-    '@smithy/util-stream',
-    '@smithy/node-http-handler',
-    '@aws-crypto/crc32c'
-    // Node.js built-ins are now handled by the nodeModuleShims plugin
-  ]
-}