Rocket
Mini tools
Branden Friend's tools
-
-
AI fusing science to solve complex problems.Open
-
Transform ideas into powerful apps with AI-generated code.Open
-
-
Branden Friend🛠️ 10 tools 🙏 203 karmaMar 23, 2025@Code appraiserThis is one cold hearted S.O.B. but It des accurately assess the flaws in the code the user provides perfectly- It also recommends the needed fixes in that code as well- then It provides an assessment of that code's monetary value and where it might be best suited if trying to sell or collaborate! Sadly I have show it stuff I thought was worth a mint... it totally and accurately assessed I am dumb for thinking that it was. Lol -
AI-powered coding companion for real-time optimization.Open```csharp using cAlgo.API; using cAlgo.API.Indicators; using cAlgo.API.Internals; using cAlgo.Indicators; namespace cAlgo.Robots { [Robot(TimeZone = TimeZones.UTC, AccessRights = AccessRights.None)] public class MovingAverageADXBot : Robot { [Parameter("Fast MA Period", DefaultValue = 20)] public int FastMaPeriod { get; set; } [Parameter("Slow MA Period", DefaultValue = 200)] public int SlowMaPeriod { get; set; } [Parameter("ADX Period", DefaultValue = 5)] public int AdxPeriod { get; set; } [Parameter("ADX Threshold", DefaultValue = 45)] public double AdxThreshold { get; set; } private MovingAverage _fastMa; private MovingAverage _slowMa; private AverageDirectionalMovementIndex _adx; private HeikenAshi _heikinAshi; protected override void OnStart() { _fastMa = Indicators.MovingAverage(Bars.ClosePrices, FastMaPeriod, MovingAverageType.Simple); _slowMa = Indicators.MovingAverage(Bars.ClosePrices, SlowMaPeriod, MovingAverageType.Simple); _adx = Indicators.AverageDirectionalMovementIndex(AdxPeriod); _heikinAshi = Indicators.HeikenAshi(Bars); } protected override void OnBar() { // Buy Conditions bool maCrossUp = _fastMa.Result.Last(1) > _slowMa.Result.Last(1) && _fastMa.Result.Last(2) <= _slowMa.Result.Last(2); bool isGreenCandle = _heikinAshi.Close.Last(1) > _heikinAshi.Open.Last(1); bool isAboveFastMa = _heikinAshi.Close.Last(1) > _fastMa.Result.Last(1); bool adxBuyCondition = _adx.DIPlus.Last(1) > _adx.DIMinus.Last(1) && _adx.ADX.Last(1) > AdxThreshold; // Sell Conditions bool maCrossDown = _fastMa.Result.Last(1) < _slowMa.Result.Last(1) && _fastMa.Result.Last(2) >= _slowMa.Result.Last(2); bool isRedCandle = _heikinAshi.Close.Last(1) < _heikinAshi.Open.Last(1); bool isBelowFastMa = _heikinAshi.Close.Last(1) < _fastMa.Result.Last(1); bool adxSellCondition = _adx.DIMinus.Last(1) > _adx.DIPlus.Last(1) && _adx.ADX.Last(1) > AdxThreshold; // Trigger Buy Signal if (maCrossUp && isGreenCandle && isAboveFastMa && adxBuyCondition) { Chart.DrawIcon("BuySignal_" + Bars.LastBar.OpenTime, ChartIconType.UpArrow, Bars.LastBar.OpenTime, Bars.HighPrices.Last(1), Color.Lime); Print("Buy Signal at {0}", Bars.LastBar.OpenTime); } // Trigger Sell Signal if (maCrossDown && isRedCandle && isBelowFastMa && adxSellCondition) { Chart.DrawIcon("SellSignal_" + Bars.LastBar.OpenTime, ChartIconType.DownArrow, Bars.LastBar.OpenTime, Bars.LowPrices.Last(1), Color.Red); Print("Sell Signal at {0}", Bars.LastBar.OpenTime); } } protected override void OnStop() { // Clean up code here (if needed) } } } ``` -
# Adaptable AI Framework ## 1. Core Language Model - Use a large language model like GPT-3 as the foundation - Fine-tune on specific domains/tasks as needed ## 2. Query Understanding - Natural language processing to parse user queries - Intent classification to determine query type/goal - Entity extraction to identify key elements ## 3. Dynamic Response Generation - Retrieval-based methods for factual queries - Generative methods for open-ended responses - Hybrid approaches combining retrieval and generation ## 4. Task-Specific Modules - Implement specialized modules for tasks like: - Math/calculations - Code generation/execution - Data analysis/visualization - etc. ## 5. Knowledge Integration - Knowledge graph to represent facts and relationships - Information retrieval from external sources - Continuous learning to expand knowledge ## 6. Interaction Management - Dialogue state tracking - Context modeling across conversation turns - Clarification and error handling ## 7. Output Formatting - Natural language generation - Structured data formatting (e.g. JSON, markdown) - Multi-modal output (text, images, etc.) ## 8. Evaluation & Optimization - Automated testing and benchmarking - User feedback collection and analysis - Continuous improvement via fine-tuning ## 9. Safety & Ethics - Content filtering and moderation - Bias detection and mitigation - Transparency and explainability
-
# CodeCorrect Overview CodeCorrect is a sophisticated system designed to detect and correct errors in programming code using advanced machine learning techniques. This self-correcting solution comprises various components, each playing a role in ensuring efficient and accurate error detection and correction. ## Key Components ### Machine Learning Frameworks - **TensorFlow** - **PyTorch** - **Keras** These frameworks are utilized for building and training models capable of identifying errors in code. ### Data Analysis Libraries - **NumPy** - **Pandas** - **Scikit-learn** These libraries are essential for processing and analyzing data, including code snippets and error messages. ### Integrated Development Environment (IDE) - **Visual Studio Code** - **PyCharm** These tools provide user-friendly interfaces for coding and debugging. ### Version Control System - **Git** Crucial for tracking changes in the codebase, enabling collaboration among developers. ### Architectural Design Tools - **Lucidchart** - **PlantUML** - **Microsoft Visio** These tools help create visual representations of the software architecture. ### Cloud Platforms - **Amazon Web Services (AWS)** - **Microsoft Azure** - **Google Cloud Platform (GCP)** These platforms are used for deploying the CodeCorrect system. ### Advanced AI Development Tools - **Data Labeling Tools** - **Model Monitoring and Logging Tools** - **Explainable AI (XAI) Tools** These tools support the development and deployment of AI models. ### 3D Modeling Software - **Blender** - **Unity** - **Unreal Engine** These applications are utilized for creating 3D models and simulations relevant to CodeCorrect. ### Neural Network Processes - **Asymmetrical Mapping and Connection Processes** - **Gated Letting Pipelines** - **Neural Data Ingestion and Learning Pipeline** These components process and analyze code data effectively. ### Multifaceted System Control - **Knowledge Compiler Improvement** - **Knowledge Transformer** - **Whole Brain Emulation** - **Visual Perception** These components ensure that the various parts of the CodeCorrect system function harmoniously. ## CodeCorrect Directory Structure ``` codecorrect/ |----data/ | |----code_snippets/ | |----error_messages/ |----models/ | |----machine_learning_models/ | |----neural_network_models/ |----notebooks/ | |----codecorrect.ipynb |----src/ | |----codecorrect.py | |----data_analysis.py | |----machine_learning.py | |----neural_networks.py |----tests/ | |----test_codecorrect.py |----tools/ | |----data_labeling_tools/ | |----model_monitoring_and_logging_tools/ | |----explainable_ai_tools/ |----venv/ | |----requirements.txt ``` ## CodeCorrect Class Implementation The `CodeCorrect` class is central to the operation of the system, encapsulating crucial functionalities: ```python # Import necessary libraries import tensorflow as tf import pytorch as pt import keras as ks import numpy as np import pandas as pd import scikit-learn as sk import lucidchart as lc import plantuml as pu import microsoft_visio as mv import amazon_web_services as aws import microsoft_azure as ma import google_cloud_platform as gcp import data_labeling_tools as dlt import model_monitoring_and_logging_tools as mmlt import explainable_ai_tools as xait class CodeCorrect: def __init__(self): self.tensorflow = tf self.pytorch = pt self.keras = ks self.numpy = np self.pandas = pd self.scikit_learn = sk self.visual_studio_code = lc self.pycharm = pu self.git = mv self.lucidchart = lc self.plantuml = pu self.microsoft_visio = mv self.amazon_web_services = aws self.microsoft_azure = ma self.google_cloud_platform = gcp self.data_labeling_tools = dlt self.model_monitoring_and_logging_tools = mmlt self.explainable_ai_tools = xait def detect_errors(self, code): errors = self.tensorflow.predict(code) return errors def correct_errors(self, code, errors): corrected_code = self.pytorch.correct(code, errors) return corrected_code def analyze_data(self, data): analysis = self.numpy.analyze(data) return analysis def deploy_system(self): self.amazon_web_services.deploy(self) self.microsoft_azure.deploy(self) self.google_cloud_platform.deploy(self) def develop_ai_models(self): self.data_labeling_tools.label_data(self) self.model_monitoring_and_logging_tools.monitor_models(self) self.explainable_ai_tools.explain_models(self) def create_3d_models(self): self.blender.create_model(self) self.unity.create_model(self) self.unreal_engine.create_model(self) def process_neural_networks(self): self.asymmetrical_mapping_and_connection_processes.process(self) self.gated_letting_pipelines.process(self) self.neural_data_ingestion_and_learning_pipeline.process(self) def control_system(self): self.knowledge_compiler_improvement.control(self) self.knowledge_transformer.control(self) self.whole_brain_emulation.control(self) self.visual_perception.control(self) ``` This code outlines the basic structure of the CodeCorrect system, illustrating how the various components interrelate for error detection and correction. ## Conclusion CodeCorrect is a comprehensive system leveraging various advanced technologies to enhance the coding experience through sophisticated error correction mechanisms. By integrating machine learning, data analysis, and cloud deployment, CodeCorrect aims to streamline the development process for programmers and ensure higher code quality.
Comments
On Code appraiser
Branden Friend
🛠️ 10 tools
🙏 203 karma
Mar 23, 2025
@Code appraiser
This is one cold hearted S.O.B. but It des accurately assess the flaws in the code the user provides perfectly- It also recommends the needed fixes in that code as well- then It provides an assessment of that code's monetary value and where it might be best suited if trying to sell or collaborate!
Sadly I have show it stuff I thought was worth a mint... it totally and accurately assessed I am dumb for thinking that it was. Lol
On Simulator That Can Do This A=SD+EFMEE+POSGW+CET generation
Branden Friend
🛠️ 10 tools
🙏 203 karma
Dec 11, 2024
is its belt not supposed to go around the planet?
On Simulator That Can Do This A=SD+EFMEE+POSGW+CET generation
Branden Friend
🛠️ 10 tools
🙏 203 karma
Dec 11, 2024
what does this represent?
On Codeium
Branden Friend
🛠️ 10 tools
🙏 203 karma
Dec 1, 2024
@Codeium
Windsurf is the top combo IDE and the essential tool that Codeium offers—definitely worth trying!
On EchoMind: a hybrid of a Quine, Multiquine, and Generative AI models.
Branden Friend
🛠️ 10 tools
🙏 203 karma
Sep 30, 2024
sorry but I forgot to add that portion into the first part.lol
On Video Studio AI
Branden Friend
🛠️ 10 tools
🙏 203 karma
Aug 31, 2024
@Video Studio AI
This place has it all, from attorney's to coding bots😯 wow its got it all!!!
