Search Results for "answer" - Page 3
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Showing 386 open source projects for "answer"
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$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
...The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using contextual information from private or enterprise datasets. Kernel Memory can ingest documents in multiple formats, process them into embeddings, and store them in searchable indexes. Applications can then query these indexed data sources to retrieve relevant information and include it as context for AI responses. -
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Personal AI Infrastructure
Agentic AI Infrastructure for magnifying HUMAN capabilities
...Unlike once-stateless chatbots, this platform captures context, memory, goals, preferences, and feedback to enable an AI that understands you and improves over time, using a full agentic stack rather than simple question-answer loops. PAI blends tools like browsing, code editing, execution, and more into a continuous Observe → Think → Plan → Execute → Verify → Learn cycle, letting the system refine its behavior with each use. Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator. -
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Tracearr
Real-time monitoring for Plex, Jellyfin, and Emby servers
Tracearr is an open-source streaming access manager designed to help media server owners understand and control who is accessing their content across platforms like Plex, Jellyfin, and Emby. It centralizes access statistics and provides tools for filtering and visualizing usage, making it easier to answer questions about viewer behavior and resource consumption, such as who is watching what and when. By monitoring requests in real time, the system can differentiate between unique users, bots, and unauthorized clients, helping administrators tighten access control and improve server performance. The project features a rule system that lets users define alerts and actions based on specific access patterns or thresholds, which is useful for proactive monitoring and automated responses to unusual activity. ... -
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Rybbit
Open-source and privacy-friendly alternative to Google Analytics
Rybbit is an open-source, privacy-friendly web and product analytics platform positioned as a modern alternative to heavyweight tracking suites. It focuses on a clean UI and intuitive metrics so non-analysts can answer everyday questions without wading through dozens of reports. The data model is event-driven, enabling funnels, retention views, and simple user journeys without complex configuration. Because privacy is a first-class goal, it aims to minimize personal data collection and offers a self-host path so you can keep analytics in your own infrastructure. ... -
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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Transformer Debugger
Tool for exploring and debugging transformer model behaviors
...It combines automated interpretability methods with sparse autoencoders, enabling researchers to analyze how specific neurons, attention heads, and latent features contribute to a model’s outputs. TDB allows users to intervene directly in the forward pass of a model and observe how such interventions change predictions, making it possible to answer questions like why a token was selected or why an attention head focused on a certain input. It automatically identifies and explains the most influential components, highlights activation patterns, and maps relationships across circuits within the model. The tool includes both a React-based neuron viewer for exploring model components and a backend activation server for running inferences and serving data. -
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Claude Code Video Vision
Give Claude the ability to watch and understand videos
...Instead of attempting to directly interpret raw video streams, the system extracts key frames using tools like ffmpeg and processes audio through transcription engines, converting both visual and auditory signals into structured inputs for the model. The result is a perception layer that feeds images and timestamped transcripts into Claude, allowing it to analyze events, answer questions, and summarize content with contextual awareness. The system dynamically adapts how much data it extracts based on the user’s query, adjusting frame rate, resolution, and time windows to optimize both performance and token efficiency. It supports multiple backends for audio processing, including local and cloud-based options, enabling flexible deployment depending on privacy or performance requirements. -
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Dexter
An autonomous agent for deep financial research
...It uses a multi-agent architecture with components such as a planning agent (to decompose queries), an action agent (to run tasks & fetch data), and self-validation mechanisms: after getting results, Dexter checks its own outputs and refines them until it is confident about its answer. This means it's more than a simple script — it’s a research assistant that loops through analysis steps until convergence. -
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
...It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. Case studies draw from public sources, including health-related datasets, to connect abstract concepts with realistic analysis. Each chapter has a notebook containing examples and exercises, along with a separate version that includes solutions. The repository also provides reusable thinkstats2 and thinkplot packages, homework material, workshops, and book-building files. ... -
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DeepSpec
A full-stack codebase for training and evaluating speculative decoding
...It provides the components needed to prepare data, train draft models, and measure acceptance behavior against target models. The workflow starts with data preparation, including prompt download, target answer regeneration, and target cache construction. It then trains a draft model using configuration files for different algorithms and target model setups. The evaluation pipeline measures speculative decoding performance across benchmark tasks such as math, coding, instruction-following, and chat-style datasets. Overall, it is useful for researchers and engineers studying faster language model inference through speculative decoding methods. -
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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Duel Agents
CLI, SDK, and IDE plugins for Duel Agents
...It provides CLI tooling, an SDK, and IDE integrations that send model traffic to a hosted endpoint using a Duel API key. The system is positioned as a routing layer that compares outputs from multiple models and selects a cost-conscious answer that meets quality expectations. It supports installation for Claude Code, Cursor, Codex CLI, OpenClaw, and all supported tools through a single installer. Developers can also build on top of it with the SDK and OpenAI-compatible or Anthropic-compatible request patterns. It is useful for users who want a centralized model-routing layer across coding tools, scripts, and agent workflows. -
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Deep Learning Interviews book
Hundreds of fully solved job interview questions
The interviews.ai repository hosts the open materials for the book Deep Learning Interviews, a comprehensive collection of technical questions and fully solved problems covering many aspects of artificial intelligence. The project was created to help students, researchers, and engineers prepare for machine learning and deep learning interviews by providing structured explanations of key concepts. The repository organizes problems across topics such as neural networks, optimization,... -
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Clawra
Openclaw as your girlfriend
...Rather than being a static chatbot tied to a corporate ecosystem, Clawra runs locally or on a private server, giving users full control over the software and data that back her behavior. She is designed not just to answer questions but to maintain a persistent character with memory, backstory, and the ability to present visual outputs like generated selfies through integrated image tools, blending conversational AI with a playful persona. Clawra has captured attention as an experimental project showcasing how far open-source agents can be pushed in creating engaging and personalized interactions, with community interest spiking around her capabilities. -
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VideoRAG
"VideoRAG: Chat with Your Videos
VideoRAG is a retrieval-augmented generation (RAG) framework tailored for video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. When a user query is received, VideoRAG locates semantically relevant moments in the video using the embedding index, retrieves associated clips or transcripts, and feeds them to a generative model to produce accurate, grounded answers or summaries. ... -
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SYSADMIN Test Questions
A collection of Linux Sysadmin Test Questions and Answers
...It spans fundamentals like filesystems, permissions, processes, and networking, then ventures into security, performance, virtualization, containers, and databases. The format encourages quick drills: pick a topic, answer from memory, then compare against concise explanations. Many questions highlight real-world troubleshooting patterns rather than purely theoretical trivia, reflecting what admins face daily. As an interview prep resource, it helps candidates assess breadth and identify weak spots before deeper study. For teams, it doubles as a training checklist to standardize baseline knowledge across junior and mid-level engineers. -
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AWESOME DATA SCIENCE
Awesome Data Science repository to learn and apply for real world
An open source Data Science repository to learn and apply towards solving real world problems. This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?" Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. ... -
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iOS Interview Questions
iOS interview questions
...Topics range from language fundamentals (memory management, ARC, value vs. reference types), to UIKit/SwiftUI, concurrency, Grand Central Dispatch, background execution, networking, design patterns, system frameworks, and architecture decisions. Each question is usually backed by sample explanations or code snippets, helping candidates understand not just what to answer but why it matters and how to reason about tradeoffs. Because the repo is maintained, it evolves to reflect changes in the iOS ecosystem: for example, newer Swift concurrency, Combine, SwiftUI, structured concurrency, or recent platform APIs. Many interviewers and candidates use it as a structured prep tool: reviewing categories, doing mock answers, or drilling tricky concepts. -
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syslog-ng
Log management solution that improves the performance of SIEM
syslog-ng is the log management solution that improves the performance of your SIEM solution by reducing the amount and improving the quality of data feeding your SIEM. With syslog-ng Store Box, you can find the answer. Search billions of logs in seconds using full text queries with Boolean operators to pinpoint critical logs. syslog-ng Store Box provides secure, tamper-proof storage and custom reporting to demonstrate compliance. syslog-ng can deliver data from a wide variety of sources to Hadoop, Elasticsearch, MongoDB, and Kafka as well as many others. syslog-ng flexibly routes log data from X sources to Y destinations. ... -
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TypeAgent Python
Structured RAG: ingest, index, query
TypeAgent Python is an experimental Python implementation of Microsoft’s TypeAgent architecture designed to explore how large language models can interact with structured software systems. The project focuses on implementing structured Retrieval-Augmented Generation workflows that allow agents to ingest information, index it in structured form, and answer queries using language models. Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language interactions into structured representations that can be processed by deterministic software components. This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. ... -
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VLMEvalKit
Open-source evaluation toolkit of large multi-modality models (LMMs)
...VLMEvalKit supports generation-based evaluation methods, allowing models to produce textual responses to visual inputs while measuring performance through techniques such as exact matching or language-model-assisted answer extraction. -
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LangBot
Production-grade platform for building agentic IM bots
LangBot is an open source platform designed to build and deploy AI-powered chatbots across multiple instant messaging ecosystems. The system allows developers to integrate large language models into messaging platforms so that bots can perform tasks, answer questions, and automate workflows directly within everyday communication tools. It supports numerous messaging services including Discord, Slack, Telegram, WeChat, and other enterprise communication systems, making it a flexible solution for both personal projects and organizational deployments. LangBot combines LLM capabilities with agent logic, knowledge base orchestration, and plugin infrastructure so that bots can perform complex tasks rather than simple conversational responses. ... -
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AI PDF Chatbot LangChain
AI PDF chatbot agent built with LangChain & LangGraph
AI PDF Chatbot LangChain is a full-stack template for building conversational agents that can ingest and answer questions about PDF documents. The project demonstrates how to combine LangChain and LangGraph with a vector database to enable retrieval-augmented question answering over user-provided files. It includes both frontend and backend components, making it suitable as a production starting point rather than just a minimal demo. The system parses uploaded PDFs into document chunks, generates embeddings, and stores them for semantic retrieval during chat interactions. ... -
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MiniRAG
Making RAG Simpler with Small and Open-Sourced Language Models
...It extracts text from documents, codes, or other structured inputs and converts them into embeddings using efficient models, then stores these vectors for fast nearest-neighbor search without requiring huge databases or separate vector servers. When a query is issued, MiniRAG retrieves the most relevant contexts and feeds them into a generative model to produce an answer that is grounded in the source material rather than hallucinated. Its minimal footprint makes it suitable for local research assistants, chatbots, help desks, or knowledge bases embedded in applications with limited resources. Despite its simplicity, it includes features such as chunking logic, configurable embedding models, and optional caching to balance performance and accuracy. -
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Quint Code
Structured reasoning framework for Claude Code, Gemini, and Cursor
...It implements the First Principles Framework (FPF) to guide users and AI tools through hypothesis generation, logical verification, evidence gathering, and documented decision making, reducing reliance on ad hoc or “vibe” coding. Instead of accepting the first plausible answer generated by an AI assistant, Quint Code encourages generating multiple competing hypotheses, verifying them, and validating them against real evidence stored in a structured “knowledge base” within your project. It supports a cycle of abduction, deduction, and induction backed by CLI commands (like /q1-hypothesize, /q2-verify, /q3-validate, etc.) that create a persisting audit trail in a .quint/ directory. -
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nanocode
Minimal Claude Code alternative. Single Python file, zero dependencies
nanocode is a minimalist coding agent implementation designed as a compact alternative to Claude Code, packaged in a single Python file with no external dependencies and totaling around 250 lines of code. It implements a full agentic loop where the model can reason, decide when to use tools, execute those tools, and iterate until producing a final answer, making it useful for simple AI-assisted coding workflows. It includes a set of integrated tools such as read, write, edit, glob, grep, and bash that let the agent interact with the file system and shell commands directly from the terminal, and it keeps a conversation history with colored terminal output for readability. The project exemplifies how lightweight architectures can still support practical agent workflows without complex infrastructure, making it suitable for developers exploring agent frameworks or building custom coding assistants. -
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Kord
Idiomatic Kotlin Wrapper for The Discord API
Kord is still in an experimental stage, as such we can't guarantee API stability between releases. While we'd love for you to try out our library, we don't recommend you use this in production just yet. Kord was created as an answer to the frustrations of writing Discord bots with other JVM libraries, which either use thread-blocking code or verbose and scope-restrictive reactive systems. We believe an API written from the ground up in Kotlin with coroutines can give you the best of both worlds: The conciseness of imperative code with the concurrency of reactive code. ...
