Best AI tools for< Steel Worker >

Opinionate is an AI-powered tool designed to enhance decision-making and strengthen arguments by steel manning different viewpoints. The platform allows users to engage in debates, generate topics, and challenge ideas with the help of artificial intelligence. With features like topic generation, daily debates, and personalized debates, Opinionate aims to facilitate constructive discussions and critical thinking. The tool is developed by Ross McMillan and is committed to ensuring user privacy and adhering to strict terms of service.

Slideworks is a website that offers strategy templates created by ex-McKinsey consultants. The platform provides high-end PowerPoint templates and toolkits for business strategy, market analysis, consulting proposals, and more. Users can access templates designed by top-tier consultants to create world-class strategy presentations, complete with proven frameworks, slide layouts, figures, and graphs. Slideworks aims to streamline the process of creating professional presentations by offering customizable templates that can be easily tailored to suit individual needs and company branding.

Truebase is a sales intelligence platform that uses AI to automate prospecting tasks. It can find, qualify, and engage with prospects, and let you know when someone is interested in a demo. Truebase is designed to be an all-in-one solution for prospecting, replacing various tools and saving you time and effort. It is also highly customizable, so you can train the AI to represent your brand accurately.

DeepIP is an AI assistant designed by Patent Professionals for Patent Professionals to enhance workflow efficiency and deliver high-quality work to clients. It automates repetitive tasks, improves accuracy, and ensures compliance with USPTO requirements. DeepIP supports various document formats for patent drafting and offers features such as patent preparation, drafting assistance, iteration & review, and office actions' answer. The application prioritizes data privacy and security, with data not stored or used for retraining purposes. DeepIP is user-friendly and suitable for patent practitioners with varying technical expertise levels, with seamless integration into Microsoft Word for easy adoption within teams.

DreamGen is an AI role-play and story generator platform that allows users to interact with AI characters, create immersive scenarios, and craft unique stories across various genres. Users can define the world, steer the plot, and let the AI bring their imagination to life. With a sleek chat-like interface, DreamGen offers a creative space where users can engage with multiple characters, explore fantasy realms, and unleash their storytelling potential. The platform provides a generous free tier with no strings attached, making it accessible for users to dive into immersive worlds and narratives.

Vectr is a free online AI vector graphics editor and AI generator that allows users to design and edit vector graphics online without a steep learning curve. It offers features such as effortless background removal, text-to-image conversion, easy image vectorization, a low learning curve, real-time sharing, and blur-free resizing. Vectr is powered by AI, making graphic creation faster and simpler with time-saver tools and features. It is free to use forever and provides a chat feature for communication within the editor.

Construction-Hazard-Detection is an AI-driven tool focused on improving safety at construction sites by utilizing the YOLOv8 model for object detection. The system identifies potential hazards like overhead heavy loads and steel pipes, providing real-time analysis and warnings. Users can configure the system via a YAML file and run it using Docker. The primary dataset used for training is the Construction Site Safety Image Dataset enriched with additional annotations. The system logs are accessible within the Docker container for debugging, and notifications are sent through the LINE messaging API when hazards are detected.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

llm-swarm is a tool designed to manage scalable open LLM inference endpoints in Slurm clusters. It allows users to generate synthetic datasets for pretraining or fine-tuning using local LLMs or Inference Endpoints on the Hugging Face Hub. The tool integrates with huggingface/text-generation-inference and vLLM to generate text at scale. It manages inference endpoint lifetime by automatically spinning up instances via `sbatch`, checking if they are created or connected, performing the generation job, and auto-terminating the inference endpoints to prevent idling. Additionally, it provides load balancing between multiple endpoints using a simple nginx docker for scalability. Users can create slurm files based on default configurations and inspect logs for further analysis. For users without a Slurm cluster, hosted inference endpoints are available for testing with usage limits based on registration status.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

AIMr is an AI aimbot tool written in Python that leverages modern technologies to achieve an undetected system with a pleasing appearance. It works on any game that uses human-shaped models. To optimize its performance, users should build OpenCV with CUDA. For Valorant, additional perks in the Discord and an Arduino Leonardo R3 are required.

Steel-LLM is a project to pre-train a large Chinese language model from scratch using over 1T of data to achieve a parameter size of around 1B, similar to TinyLlama. The project aims to share the entire process including data collection, data processing, pre-training framework selection, model design, and open-source all the code. The goal is to enable reproducibility of the work even with limited resources. The name 'Steel' is inspired by a band '万能青年旅店' and signifies the desire to create a strong model despite limited conditions. The project involves continuous data collection of various cultural elements, trivia, lyrics, niche literature, and personal secrets to train the LLM. The ultimate aim is to fill the model with diverse data and leave room for individual input, fostering collaboration among users.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

LLM Steer is a Python module designed to steer Large Language Models (LLMs) towards specific topics or subjects by adding steer vectors to different layers of the model. It enhances the model's capabilities, such as providing correct responses to logical puzzles. The tool should be used in conjunction with the transformers library. Users can add steering vectors to specific layers of the model with coefficients and text, retrieve applied steering vectors, and reset all steering vectors to the initial model. Advanced usage involves changing default parameters, but it may lead to the model outputting gibberish in most cases. The tool is meant for experimentation and can be used to enhance role-play characteristics in LLMs.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

TypeChat is a library that simplifies the creation of natural language interfaces using types. Traditionally, building natural language interfaces has been challenging, often relying on complex decision trees to determine intent and gather necessary inputs for action. Large language models (LLMs) have simplified this process by allowing us to accept natural language input from users and match it to intent. However, this has introduced new challenges, such as the need to constrain the model's response for safety, structure responses from the model for further processing, and ensure the validity of the model's response. Prompt engineering aims to address these issues, but it comes with a steep learning curve and increased fragility as the prompt grows in size.

ChatDBG is an AI-based debugging assistant for C/C++/Python/Rust code that integrates large language models into a standard debugger (`pdb`, `lldb`, `gdb`, and `windbg`) to help debug your code. With ChatDBG, you can engage in a dialog with your debugger, asking open-ended questions about your program, like `why is x null?`. ChatDBG will _take the wheel_ and steer the debugger to answer your queries. ChatDBG can provide error diagnoses and suggest fixes. As far as we are aware, ChatDBG is the _first_ debugger to automatically perform root cause analysis and to provide suggested fixes.

AIRAVAT is a multifunctional Android Remote Access Tool (RAT) with a GUI-based Web Panel that does not require port forwarding. It allows users to access various features on the victim's device, such as reading files, downloading media, retrieving system information, managing applications, SMS, call logs, contacts, notifications, keylogging, admin permissions, phishing, audio recording, music playback, device control (vibration, torch light, wallpaper), executing shell commands, clipboard text retrieval, URL launching, and background operation. The tool requires a Firebase account and tools like ApkEasy Tool or ApkTool M for building. Users can set up Firebase, host the web panel, modify Instagram.apk for RAT functionality, and connect the victim's device to the web panel. The tool is intended for educational purposes only, and users are solely responsible for its use.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

NeMo Guardrails is an open-source toolkit for easily adding _programmable guardrails_ to LLM-based conversational applications. Guardrails (or "rails" for short) are specific ways of controlling the output of a large language model, such as not talking about politics, responding in a particular way to specific user requests, following a predefined dialog path, using a particular language style, extracting structured data, and more.

IW5 Bot Warfare is a GSC mod for the PlutoniumIW5 project that adds playable AI to the multiplayer games of Modern Warfare 3. It features a Waypoint Editor for creating and modifying bot's waypoints, a customizable menu for editing bot DVARs, compatibility with other mods, AI clients that simulate real players, and various bot behaviors such as capturing objectives, using killstreaks, targeting equipment, and more. The mod aims to provide a comprehensive Combat Training experience for MW3 multiplayer games.

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