Collected AI Tools
Estimated Reading Time: 8 minutes
Last updated on 5th April 2026
When we mention AI tools, we are referring to software applications and platforms that leverage artificial intelligence technologies to perform tasks traditionally requiring human intelligence. As such, they encompass a wide range of functionalities, from natural language processing and computer vision to decision-making and predictive analytics.
Thus, AI systems are increasingly being integrated into various industries such as healthcare, finance, marketing and manufacturing to enhance efficiency, accuracy and productivity. So far, they are automating repetitive tasks, provide insights from large datasets and facilitate personalised user experiences.
As they continue to evolve, these tools are transforming how businesses operate and innovate. However, their adoption also raises important considerations around ethics, privacy and the future of work. It is likely that very few will be unaffected by what is an ongoing revolution. Software engineers have borne some of the brunt of this so far, and they are not the only ones.
All in all, the upheaval that AI is bringing upon us is akin to the personal computing one from around a generation ago. Even the mobile revolution within the last ten or fifteen years does not compare so strongly in extent or depth. Since this episode is changing everything for humanity, that is why my own dalliances with AI tooling proceed with a certain amount of impetus.
The number of options is already many, which is why I have now subdivided what once was a single collection in numerous articles that you find listed on the right-hand side. That leaves a remnant here that does not fit in any other category, though I cannot rule out any further divisions in the future.
Though I tend not to use it as a source of AI empowerment, Hugging Face is a leading machine learning platform founded in 2016 by Clément Delangue, Julien Chaumond and Thomas Wolf. Known for its Transformers library, the company simplifies access to pre-trained models for natural language processing tasks like text classification, summarisation, translation and the answering of questions. With over 900,000 models and 200,000 datasets available on the Hugging Face Hub, users can share, and experiment with, various machine learning applications.
The platform fosters a strong community and offers extensive documentation, tutorials and an Inference API for both free prototyping and paid production workloads. By democratising access to machine learning tools, Hugging Face continues driving advancements in artificial intelligence technologies.
This open-source desktop application enables users to run large language models directly on their own machines without requiring an internet connection or incurring API costs. The programme provides a polished graphical interface similar to ChatGPT, offering features such as chat history management, multiple conversation threads, model switching capabilities and streaming responses with markdown rendering. It supports execution of various models including Llama, Mistral, Gemma and others through inference engines like llama.cpp or via integration with Ollama, whilst also allowing connection to remote APIs such as OpenAI or custom endpoints for hybrid workflows. The software prioritises privacy by ensuring no conversation data leaves the user's machine and exposes a local API server with OpenAI-compatible endpoints for integration with scripts, tools and automation pipelines. It functions effectively as a desktop interface layer that complements backend engines like Ollama, making it particularly suitable for local drafting, private document analysis, code generation and evaluating different models quickly, though it remains less ideal for automated pipelines or headless server environments compared to command-line alternatives.
This open-source enhanced ChatGPT clone provides comprehensive artificial intelligence conversation capabilities across multiple platforms and models. The platform supports integration with numerous AI providers including Anthropic Claude, OpenAI, Azure OpenAI, Google, AWS Bedrock and various custom endpoints without requiring proxy configurations. Key functionality includes a secure code interpreter for multiple programming languages, customisable agents and tools integration through the Model Context Protocol, web search capabilities with content re-ranking and generative user interface features supporting React, HTML and Mermaid diagram creation.
The system offers multimodal interactions allowing users to upload and analyse images whilst chatting with files, supports over 30 languages and includes advanced conversation management through presets, branching and sharing capabilities. Additional features encompass speech-to-text and text-to-speech functionality, comprehensive import and export options for conversations, multi-user authentication with OAuth2 and LDAP support and flexible deployment options for both local and cloud environments. The project maintains active community development with regular updates and welcomes contributions for translations and feature enhancements.
LiteLLM is a Python-based Large Language Model (LLM) inference and serving framework designed for high performance and scalability. It features efficient GPU utilization through tri-process asynchronous collaboration, efficient handling of requests with large length disparities using Nopad attention operations, dynamic batch scheduling and faster inference and reduced GPU memory usage through FlashAttention integration. Additionally, it offers multi-GPU token parallelism, optimised GPU memory management via Token Attention, high-performance Router for system throughput optimization and an Int8KV Cache for increased token capacity (Llama models only).
LiteLLM is based on open-source implementations like FasterTransformer, TGI, vLLM and FlashAttention. It's lightweight and scalable, making it suitable for deploying and serving large language models efficiently. Benchmarks show competitive performance with other frameworks on various platforms. The use of OpenAI Triton for kernel implementation and its relatively compact codebase make it easier for developers to optimise the framework. Instructions to launch and evaluate LightLLM's performance are provided in its documentation.
LM Studio is a desktop application that enables users to run large language models locally on their computers without needing technical expertise or coding skills. It offers a user-friendly interface for discovering, downloading and interacting with various pre-trained models from open-source repositories such as Hugging Face. Key features include offline operation for enhanced data privacy, a built-in chat interface and a document chat functionality that allows interaction with local documents using Retrieval Augmented Generation. It also supports an OpenAI-compatible local server. The application facilitates model discovery through a Discover page and supports various model architectures. Designed to make language models accessible for personal experimentation while preserving data sovereignty, LM Studio is free for personal use but requires a business licence for commercial applications. Though the application itself is not open-source, it aids in the distribution and use of available AI models.
Microsoft 365 Copilot is an AI-integrated productivity tool within Microsoft Office Suite that uses advanced capabilities like GPT and DALL-E for task assistance across various applications, including Word, Excel and PowerPoint. The user-friendly interface allows interaction via voice commands or chat interfaces, enhancing the experience while automating routine tasks to save time. Copilot is available on multiple platforms such as Windows and Microsoft Edge for versatility in various work environments. It aims to boost productivity by providing intelligent suggestions, allowing users to focus on more strategic aspects of their jobs. The integration within familiar applications simplifies usage and access to AI capabilities. In conclusion, Copilot is a valuable addition to the modern workplace that empowers individuals with enhanced capabilities and streamlined workflows.
Ollama is an open-source tool designed for running large language models (LLMs) locally on personal devices, catering to developers, researchers and businesses seeking enhanced control over their AI applications while addressing privacy concerns. Key features include local execution of various LLMs, ensuring data security by keeping sensitive information on the user's machine, model customisation, extensive library access, a user-friendly interface with command line functionality and seamless integration with programming languages and frameworks. Ollama is versatile, applicable in numerous scenarios such as chatbots, summarisation tools, creative writing aids and more. It offers significant advantages through its robust solution for local AI model execution, focusing on providing a user-friendly environment for managing large language models.
PromptHub is a platform designed for teams working with large language models (LLMs), offering a comprehensive solution for prompt management. It centralises the organisation, versioning, testing and deployment of prompts, providing both community sharing and enterprise-grade collaboration features. This platform serves prompt engineers, ML engineers, software developers and product managers, with support for public prompt sharing and private team workspaces. Pricing plans range from free to custom enterprise solutions, varying in feature availability and limits.
TLDR This is an AI-powered summarisation tool designed to help users quickly digest lengthy articles, documents and other text-based content by condensing key points into concise summaries using advanced natural language processing algorithms. Users can customise the length of their summaries, making it adaptable for various needs, and access it as a web application or browser extension to simplify information gathering from favourite websites. The tool's applications include academic research, professional efficiency enhancement and staying informed about current topics. In short, TLDR This offers automatic summarisation capabilities and API access for developers.