Search Results for "dashboard"
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Showing 18 open source projects for "dashboard"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Omnara
Talk to Your AI Agents from Anywhere
...Omnara transforms your AI agents (Claude Code, Codex CLI, n8n, and more) from silent workers into communicative teammates. Get real-time visibility into what your agents are doing, and respond to their questions instantly from a single dashboard on web and mobile. The primary way to use CLI coding agents (Claude Code, Codex CLI) with Omnara. -
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OpenBB
Investment Research for Everyone, Everywhere
...Whether it’s a CSV file, a private endpoint, an RSS feed, or even embed an SEC filing directly. Chat with financial data using large language models. Don’t waste time reading, create summaries in seconds and ask how that impacts investments. Create your dashboard with your favorite widgets. Create charts directly from raw data in seconds. Create charts directly from raw data in seconds. Customize your dashboards to build your dream terminal, integrate with your private datasets and bring your own fine-tuned AI copilots. -
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Evidently
Evaluate and monitor ML models from validation to production
Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings. -
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MedgeClaw
Open-source AI research assistant for biomedicine
...The system connects conversational interfaces with computational environments, allowing users to initiate research tasks through messaging platforms while the backend executes analyses using tools like R and Python. It includes a real-time dashboard that displays progress, generated code, and outputs, providing transparency throughout the research process. MedgeClaw also supports reproducibility by generating structured reports and maintaining consistent environments through containerization. Its architecture combines conversational AI, automated pipelines, and scientific tooling into a unified workflow. -
Try Google Cloud Risk-Free With $300 in CreditUse your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
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Acontext
Context data platform for building observable, self-learning AI agents
...Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable. -
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Loki Mode
Multi-agent autonomous startup system for Claude Code
Loki Mode is a multi-agent autonomous execution system designed to take structured product requirements or specifications and autonomously drive the creation, testing, deployment, and scaling of complex software projects using a large team of specialized AI agents. It orchestrates dozens of agent types across swarms that handle designated roles — such as architecture, coding, QA, deployment, and business workflows — running in parallel to cover both engineering and operational tasks without... -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely... -
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Aden Hive
Outcome driven agent development framework that evolves
Hive is an open-source agent development framework that helps developers build autonomous, reliable, self-improving AI agents by letting them describe goals in ordinary natural language instead of hand-coding detailed workflows. Rather than manually defining execution graphs, Hive’s coding agent generates the agent graph, connection code, and test cases based on your high-level objectives, enabling outcome-driven agent creation that fits real business processes. Once deployed, agents can... -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
...Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard. Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights. Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. ... -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated... -
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OpenHome Abilities
Open-source abilities for OpenHome agents
OpenHome Abilities is an open-source repository of modular voice AI plugins created for OpenHome agents, giving developers a lightweight way to extend what an agent can do through spoken triggers. Each ability is intentionally simple in structure, centering on a single main.py file that contains the core Python logic, which lowers the barrier to building and sharing custom behaviors. The system is meant to support a wide range of voice-driven actions, from API calls and media playback to... -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before... -
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AI-Codereview-Gitlab
GitLab automatic code review tool based on large models
AI-Codereview-Gitlab is an open-source automation tool that integrates large language models into the GitLab development workflow to perform automated code reviews. The system monitors GitLab repositories and analyzes commits or merge requests using AI models to identify potential issues, coding mistakes, and quality improvements before the code is merged. By leveraging multiple large language model providers—including OpenAI, DeepSeek, ZhipuAI, or local models through Ollama—the platform... -
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PC_Workman_HCK
AI-powered PC monitoring that explains. Not shows numbers/spikes.
PC_Workman is what 680 hours of coding after warehouse shifts looks like. Built on a laptop hitting 94°C, this AI-powered monitoring tool does what Task Manager can't: it understands your system, not just measures it. Features: - Time travel monitoring - debug issues from hours ago - AI diagnostics with HCK_GPT - Custom fan curves with profiles - Floating always-on-top widget - 2D system map - Cross-GPU support (NVIDIA/AMD/Intel) Four complete rebuilds. 29 features killed.... -
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Weather Cast
A desktop weather app powered by AI
Weather app is a desktop weather app for Windows OS that shows detailed weather information for the searched city. The dashboard shows the current temperature of the city, description of temperature, pressure, wind, humidity, dew point, uv index, local time, air pollution index. -
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langchain-prefect
Tools for using Langchain with Prefect
Large Language Models (LLMs) are interesting and useful - building apps that use them responsibly feels like a no-brainer. Tools like Langchain make it easier to build apps using LLMs. We need to know details about how our apps work, even when we want to use tools with convenient abstractions that may obfuscate those details. Prefect is built to help data people build, run, and observe event-driven workflows wherever they want. It provides a framework for creating deployments on a whole... -
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gpt-j-api
API for the GPT-J language mode. Including a FastAPI backend
An API to interact with the GPT-J language model and variants! You can use and test the model in two different ways. These are the endpoints of the public API and require no authentication. Just SSH into a TPU VM. This code was tested on both the v2-8 and v3-8 variants. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
...For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. The frames were captured by the IP camera and they were submitted into the AI engine. The output from the AI engine will be shown in the dashboard.
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