Best AI tools for< Observe Wildlife >

Observe.AI is a conversation intelligence software designed for contact centers, offering a suite of AI-powered tools to enhance customer interactions, support agents in real-time, and improve overall customer satisfaction. The platform combines advanced analytics, automation, and real-time assistance to drive continuous improvement and boost operational efficiency. Trusted by over 350 enterprises worldwide, Observe.AI helps businesses transform customer conversations into actionable insights, leading to measurable results such as increased sales conversions, improved compliance adherence, and enhanced customer sentiment.

Wallaroo.AI is an AI inference platform that offers production-grade AI inference microservices optimized on OpenVINO for cloud and Edge AI application deployments on CPUs and GPUs. It provides hassle-free AI inferencing for any model, any hardware, anywhere, with ultrafast turnkey inference microservices. The platform enables users to deploy, manage, observe, and scale AI models effortlessly, reducing deployment costs and time-to-value significantly.

Spreadsite is an AI-powered platform that turns spreadsheets into interactive web dashboards without the need for coding. It utilizes AI to transform data into visually appealing and interactive dashboards, offering features like agent-powered workflows, interactive data visualization, seamless sharing, and endless possibilities for data exploration. Spreadsite caters to various industries such as finance, marketing, and energy, providing users with the ability to create custom websites from their spreadsheet data effortlessly.

Radicalbit is an MLOps and AI Observability platform that helps businesses deploy, serve, observe, and explain their AI models. It provides a range of features to help data teams maintain full control over the entire data lifecycle, including real-time data exploration, outlier and drift detection, and model monitoring in production. Radicalbit can be seamlessly integrated into any ML stack, whether SaaS or on-prem, and can be used to run AI applications in minutes.

Sanctuary Cognitive Systems Corporation is a company that develops and manufactures general-purpose robots. Their flagship product, Phoenix™, is the world's first humanoid general-purpose robot powered by Carbon™, their pioneering AI control system. Phoenix™ is designed for work and is the only general-purpose robot featured in TIME's Best Inventions 2023. Sanctuary's robots are remotely piloted or supervised by people and are designed to both train and work alongside them. When instructed to do so, their robots will use their own built-in autonomous control system to observe, assess, and act on tasks in an efficient and prosperous manner.

Camel AGI is a groundbreaking platform that revolutionizes the way artificial intelligence is utilized to solve complex tasks by employing a unique role-playing method inspired by loop architecture, similar to that of BabyAGI and AutoGPT. At its core, CamelAGI facilitates the collaboration between two autonomous AI agents, each assigned specific roles, to work synergistically towards accomplishing a designated task. This innovative approach allows users to observe as the agents, equipped with distinct capabilities and perspectives, engage in a dynamic and context-aware dialogue, effectively mirroring the collaborative efforts seen in human interactions.

Observer is a news and media website that covers a wide range of topics, including business, finance, technology, media, lifestyle, arts, entertainment, and power lists. The website features articles, reviews, interviews, and videos from a team of experienced journalists and critics.

Privacy Observer is an AI-powered tool that makes privacy accessible by scanning and analyzing privacy policies of websites. It helps users understand when websites request excessive personal information without the need to read lengthy policies. The tool provides a detailed score for each website, ensuring users can make informed decisions about their online privacy. With features like unlimited background scans, anonymous checks by humans, and a user-friendly browser extension, Privacy Observer aims to empower users to protect their privacy online.

Revisor is an AI-enabled monitoring system based on neural networks, designed for counting actual voters and monitoring electoral procedures. It offers fast, reliable, and cost-effective election observation with high precision in voter counting. The system can detect ballot boxes, identify violations, and compare turnout data to detect falsified results. Revisor is trainable for different voting procedures and elections, providing immediate or long-term results based on video recordings.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

Transformer Explainer is an interactive visualization tool to help users learn how Transformer-based models like GPT work. It allows users to experiment with text and observe how internal components of the Transformer predict next tokens in real time. The tool runs a live GPT-2 model in the browser, providing an educational experience on text-generative models.

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.

Surfkit is a versatile toolkit designed for building and sharing AI agents that can operate on various devices. Users can create multimodal agents, share them with the community, run them locally or in the cloud, manage agent tasks at scale, and track and observe agent actions. The toolkit provides functionalities for creating agents, devices, solving tasks, managing devices, tracking tasks, and publishing agents. It also offers integrations with libraries like MLLM, Taskara, Skillpacks, and Threadmem. Surfkit aims to simplify the development and deployment of AI agents across different environments.

Langfuse Python SDK is a software development kit that provides tools and functionalities for integrating with Langfuse's language processing services. It offers decorators for observing code behavior, low-level SDK for tracing, and wrappers for accessing Langfuse's public API. The SDK was recently rewritten in version 2, released on December 17, 2023, with detailed documentation available on the official website. It also supports integrations with OpenAI SDK, LlamaIndex, and LangChain for enhanced language processing capabilities.

OpenSSA is an open-source framework for creating efficient, domain-specific AI agents. It enables the development of Small Specialist Agents (SSAs) that solve complex problems in specific domains. SSAs tackle multi-step problems that require planning and reasoning beyond traditional language models. They apply OODA for deliberative reasoning (OODAR) and iterative, hierarchical task planning (HTP). This "System-2 Intelligence" breaks down complex tasks into manageable steps. SSAs make informed decisions based on domain-specific knowledge. With OpenSSA, users can create agents that process, generate, and reason about information, making them more effective and efficient in solving real-world challenges.

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs. Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis. **Aim's mission is to democratize AI dev tools 🎯**

aiocoap is a Python library that implements the Constrained Application Protocol (CoAP) using native asyncio methods in Python 3. It supports various CoAP standards such as RFC7252, RFC7641, RFC7959, RFC8323, RFC7967, RFC8132, RFC9176, RFC8613, and draft-ietf-core-oscore-groupcomm-17. The library provides features for clients and servers, including multicast support, blockwise transfer, CoAP over TCP, TLS, and WebSockets, No-Response, PATCH/FETCH, OSCORE, and Group OSCORE. It offers an easy-to-use interface for concurrent operations and is suitable for IoT applications.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

The Azure Open AI Application Landing Zone Solution Accelerator aims to assist in setting up development and production environments for Generative AI solutions using Azure Open AI and Azure Services. It provides deployment templates for common Gen AI solution patterns and offers customization options. The solution accelerator also offers best practices for technology usage in various scenarios.

This repository contains the training code of LLM-QAT for large language models. The work investigates quantization-aware training for LLMs, including quantizing weights, activations, and the KV cache. Experiments were conducted on LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. Significant improvements were observed when quantizing weight, activations, and kv cache to 4-bit, 8-bit, and 4-bit, respectively.

LionAGI is a powerful intelligent workflow automation framework that introduces advanced ML models into any existing workflows and data infrastructure. It can interact with almost any model, run interactions in parallel for most models, produce structured pydantic outputs with flexible usage, automate workflow via graph based agents, use advanced prompting techniques, and more. LionAGI aims to provide a centralized agent-managed framework for "ML-powered tools coordination" and to dramatically lower the barrier of entries for creating use-case/domain specific tools. It is designed to be asynchronous only and requires Python 3.10 or higher.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

Pearl is a production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta. It enables researchers and practitioners to develop Reinforcement Learning AI agents that prioritize cumulative long-term feedback over immediate feedback and can adapt to environments with limited observability, sparse feedback, and high stochasticity. Pearl offers a diverse set of unique features for production environments, including dynamic action spaces, offline learning, intelligent neural exploration, safe decision making, history summarization, and data augmentation.

Deaddit is a project showcasing an AI-filled internet platform similar to Reddit. All content, including subdeaddits, posts, and comments, is generated by AI algorithms. Users can interact with AI-generated content and explore a simulated social media experience. The project provides a demonstration of how AI can be used to create online content and simulate user interactions in a virtual community.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

Guides on outdoor activities, with an informative and adventure-oriented tone.

上帝视角观察、探索和影响一个末日丧尸灾难后的人类社会。Observe, explore and influence human society after the apocalyptic zombie disaster from a God's perspective. Sponsor：小红书“ ItsJoe就出行 ”

Non-interactive, silent GPT.

Monitoring tools expert with brief responses and guiding hints.