Best AI tools for< Speculate On The Future Of Mind Uploading >

Meltface Typeface is a book about the future of design in the age of AI agents, spatial computing, and ambient UX. It is written by Casey Fictum, a designer and philosopher who has been thinking about the future of technology for over 20 years. The book is divided into nine chapters, each of which explores a different aspect of the future of design. Chapter 1, "The Dawn of Ambient Intelligence," discusses the rise of AI agents and their potential to change the way we live and work. Chapter 2, "Artificial - This Thing Isn't Human," explores the challenges of designing AI agents that are both useful and ethical. Chapter 3, "Spatial - Around My Reality," discusses the potential of spatial computing to create new and immersive experiences. Chapter 4, "Ambient - There, But Not," explores the concept of ambient UX and how it can be used to create more seamless and intuitive experiences. Chapter 5, "Actioned - Do Things on Our Behalf," discusses the potential of AI agents to automate tasks and help us get things done. Chapter 6, "Philosophy for AI Agent Design," provides a philosophical framework for designing AI agents that are both ethical and effective. Chapter 7, "Frameworks for the Future of Design," provides a set of frameworks for thinking about the future of design. Chapter 8, "Guessing the Future of UX Design," speculates on what the future of UX design might look like. Chapter 9, "Finding Meaning & Purpose in the Future of Design," discusses the challenges and opportunities of designing for a future that is increasingly shaped by AI.

TLDR This is an online article summarizer tool that helps users quickly understand the essence of lengthy content. It uses AI to analyze any piece of text and summarize it automatically, in a way that makes it easy to read, understand, and act on. TLDR This also extracts essential metadata such as author and date information, related images, and the title. Additionally, it estimates the reading time for news articles and blog posts, ensuring users have all the necessary information consolidated in one place for efficient reading. TLDR This is designed for students, writers, teachers, institutions, journalists, and any internet user who needs to quickly understand the essence of lengthy content.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

The Awesome-System2-Reasoning-LLM repository is dedicated to a survey paper titled 'From System 1 to System 2: A Survey of Reasoning Large Language Models'. It explores the development of reasoning Large Language Models (LLMs), their foundational technologies, benchmarks, and future directions. The repository provides resources and updates related to the research, tracking the latest developments in the field of reasoning LLMs.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

This repository focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities of large language models.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

This repository implements speculative sampling for large language model (LLM) decoding, utilizing two models - a target model and an approximation model. The approximation model generates token guesses, corrected by the target model, resulting in improved efficiency. It includes implementations of Google's and Deepmind's versions of speculative sampling, supporting models like llama-7B and llama-1B. The tool is designed for fast inference from transformers via speculative decoding.

ExLlamaV2 is an inference library designed for running local LLMs on modern consumer GPUs. The library supports paged attention via Flash Attention 2.5.7+, offers a new dynamic generator with features like dynamic batching, smart prompt caching, and K/V cache deduplication. It also provides an API for local or remote inference using TabbyAPI, with extended features like HF model downloading and support for HF Jinja2 chat templates. ExLlamaV2 aims to optimize performance and speed across different GPU models, with potential future optimizations and variations in speeds. The tool can be integrated with TabbyAPI for OpenAI-style web API compatibility and supports a standalone web UI called ExUI for single-user interaction with chat and notebook modes. ExLlamaV2 also offers support for text-generation-webui and lollms-webui through specific loaders and bindings.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

LLM-as-a-Judge is a repository that includes papers discussed in a survey paper titled 'A Survey on LLM-as-a-Judge'. The repository covers various aspects of using Large Language Models (LLMs) as judges for tasks such as evaluation, reasoning, and decision-making. It provides insights into evaluation pipelines, improvement strategies, and specific tasks related to LLMs. The papers included in the repository explore different methodologies, applications, and future research directions for leveraging LLMs as evaluators in various domains.

Explores mind uploading and consciousness transition.

Experto en análisis de Ley Contrato 406, formal y accesible, evita especulaciones.

Speculates on rare events, not a financial advisor

Engaging exploration of alternate history and realities

Explores speculative physics in alternate universes

The Multiversal Prediction Matrix (MPM) leverages the speculative nature of multiverse theories to create a predictive framework. By simulating parallel universes with varied parameters, MPM explores a multitude of potential outcomes for different events and phenomena.

Writes Personalized Cover Letters for Advertised Positions; For Speculative Inquiries Writes Personalized Letters of Interest

I craft and visualize 'what if' histories.

Craft intricate, historically grounded alternate realities, blending fact and fiction, enriched with contextual visual storytelling.