Search Results for "reason" - Page 2
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Showing 297 open source projects for "reason"
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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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Khoj
An AI personal assistant for your digital brain
...It is an offline-first, open-source AI personal assistant that is accessible from Emacs, Obsidian or your Web browser. Khoj is a thinking tool that is transparent, fun, and easy to engage with. You can build faster and better by using Khoj to search and reason across all your data sources. Khoj learns from your notes and documents to function as an extension of your brain. So that you can stay focused on doing what matters. Khoj started with the founding principle that a personal assistant be understandable, accessible and hackable. This means you can always customize and self-host your Khoj on your own machines. -
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InsForge
InsForge is the backend built for AI-assisted development
InsForge is an open-source backend development platform designed specifically for AI-assisted or agent-driven application development, positioning itself as an agent-native alternative to tools like Supabase by exposing backend primitives (auth, database, storage, serverless functions, and AI integrations) in a way that intelligent agents can understand, reason about, and act upon directly. Rather than forcing developers to manually cobble together authentication flows, database schemas, storage buckets, and cloud functions, InsForge provides a semantic layer and toolchain that let agents configured with Model Context Protocol (MCP) understand the backend state, available operations, and how to manipulate these resources end to end. ... -
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Qwen3.6
Qwen3.6 is the large language model series developed by Qwen team
...The repository serves as a central hub for documentation, community discussion, and access to the latest model releases, rather than a standalone application. One of its defining goals is to enhance “agentic coding,” enabling the model to reason across entire codebases, handle multi-step development tasks, and assist with complex software engineering workflows. The architecture incorporates modern techniques such as mixture-of-experts and hybrid attention mechanisms, allowing it to scale efficiently while maintaining strong performance. -
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AGI
The first distributed AGI system
AGI project is an experimental framework focused on building components and infrastructure for artificial general intelligence systems, emphasizing modularity, autonomy, and scalable intelligence pipelines. It aims to provide a foundation for creating agents that can reason, plan, and execute tasks across diverse domains by integrating multiple AI capabilities into a unified system. The project typically explores concepts such as agent orchestration, memory systems, task decomposition, and decision-making loops, enabling the development of more generalized and adaptive AI behaviors. It is designed to be extensible, allowing developers to plug in different models, tools, and data sources to enhance agent performance. ... -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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Flow-Like
Strongly Typed Enterprise Scale Workflows
Flow-Like is a lightweight framework for building flow-based, reactive user interfaces that lets developers structure applications around declarative data flows and event pipelines instead of imperative state management. The core idea is that UI components react to changes in upstream flows, making it easier to reason about how data moves through the system and how user interactions propagate changes throughout the interface. This approach contrasts with traditional two-way binding or manual update patterns by treating flows as the single source of truth, simplifying complex interactions and reducing bugs caused by scattered state logic. Developers can compose and combine flows, establish transformations, and orchestrate asynchronous events using intuitive primitives, resulting in code that is easier to test, maintain, and scale. -
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Supermemory
Memory engine and app that is extremely fast, scalable
Supermemory is an ambitious and extensible AI-powered personal knowledge management system that aims to help users capture, organize, retrieve, and reason over information in a manner that mimics human memory structures. The platform allows individuals to ingest text, documents, and other content forms, then uses advanced retrieval and embedding techniques to index and relate information intelligently so that users can recall relevant knowledge in context rather than just by keyword match. ... -
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Brokk
Brokk brings code intelligence to AI
...It helps bridge the gap between LLMs and real-world engineering code by offering tooling to index, analyze, query, and augment code context, so that AI can meaningfully reason about existing code, suggest edits, and navigate across projects. Modular build tasks (run, test, build, shadowJar, tidy, etc.) to support development workflows. Integration of front-end + back-end layers (web UI + CLI + internal services). -
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n8n-MCP
A MCP for Claude Desktop / Claude Code / Windsurf / Cursor
n8n-mcp is a Model Context Protocol (MCP) server that turns the n8n workflow platform into a set of first-class, typed tools an AI assistant can understand and operate. It exposes structured knowledge of n8n nodes and operations so an agent can reason about workflows, parameters, and executions without scraping docs or guessing API shapes. The server focuses on making Claude Desktop (and other MCP-capable clients) “n8n-literate,” enabling tasks such as inspecting existing workflows, proposing node chains, and validating configuration before runs. It ships with organized resources and tool definitions that map cleanly to n8n’s ecosystem, improving reliability compared with ad-hoc prompt patterns. ... -
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Claude BugHunter
A Claude Code skill bundle for bug hunting
Claude-BugHunter is a Claude Code skill bundle focused on bug hunting, security testing, and external red-team research workflows. It packages a large collection of specialized skills, slash commands, and disclosed-report patterns so Claude Code can reason through common vulnerability classes more systematically. The project is meant to help authorized testers structure reconnaissance, triage, hypothesis building, exploitation reasoning, and reporting. It includes curated patterns from public vulnerability reports, making it useful as a learning and workflow reference. Because it supports security testing, it should only be used on systems where the user has permission to test. ... -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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Napkin Math
Techniques and numbers for estimating system's performance
...The repository is especially useful for system design interviews, architecture planning, capacity estimation, and infrastructure cost discussions. It encourages engineers to practice estimation as a skill so they can reason about systems before building or benchmarking them. -
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
The Mathematics Dataset, developed by Google DeepMind, is a synthetic dataset designed to evaluate and train machine learning models on mathematical reasoning and symbolic manipulation. It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. Each question is programmatically generated with structured templates to ensure clear logic and reproducibility. The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. ... -
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Modern C++ Programming
Modern C++ Programming Course
...It walks through core language topics like RAII, move semantics, templates and metaprogramming, lambdas, and smart pointers with an eye toward real-world patterns. Concurrency and performance enter the picture via threads, atomics, futures, and memory considerations, helping learners reason about correctness and speed. The materials highlight best practices—value semantics, const-correctness, and zero-cost abstractions—while cautioning against common pitfalls inherited from older C++ idioms. Build and tooling conventions (e.g., CMake, testing frameworks, static analyzers) are woven into examples so projects are reproducible and portable. ... -
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Erigon
Ethereum implementation on the efficiency frontier
...Same true about: JSON RPC layer (RPCDaemon), p2p layer (Sentry), history download layer (Downloader), consensus. Don't start services as separated processes unless you have clear reason for it: resource limiting, scale, replace by your own implementation, security. How to start Erigon's services as separated processes, see in docker-compose.yml. -
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UI-TARS
UI-TARS-desktop version that can operate on your local personal device
UI-TARS is an open-source multimodal “GUI agent” created by ByteDance: a model designed to perceive raw screenshots (or rendered UI frames), reason about what needs to be done, and then perform real interactions with graphical user interfaces (GUIs) — like clicking, typing, navigating menus — across desktop, browser, mobile, or game environments. Rather than relying on rigid, manually scripted UI automation, UI-TARS uses a unified vision-language model (VLM) that integrates perception, reasoning, grounding, and action into one end-to-end framework: it “thinks before acting,” enabling flexible, general-purpose automation. ... -
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System Prompts and Models of AI Tools
Full System Prompts, Internal Tools & AI Models
...The repository serves as a valuable resource for developers, researchers, and AI enthusiasts interested in understanding prompt engineering and agent behavior. By exposing these system-level instructions, it highlights how AI tools are designed to reason, act, and interact with users. It also emphasizes transparency and security awareness, especially around prompt leaks and vulnerabilities. Overall, it acts as a comprehensive knowledge base for studying and experimenting with real-world AI system prompts. -
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Specification
Base class with tests for adding specifications to a DDD model
...The library works especially well with Entity Framework Core, enabling developers to centralize query definitions instead of scattering LINQ expressions throughout the codebase. By defining specifications as classes, teams can build complex query rules that remain testable and easy to reason about. The project is used in Microsoft’s reference application eShopOnWeb and aligns closely with clean architecture principles. Overall, Specification provides a structured approach to query reuse and domain-focused data access in modern .NET applications. -
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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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Deep Learning Is Nothing
Deep learning concepts in an approachable style
...Practical sections cover data pipelines, regularization, and evaluation, emphasizing reproducibility and debugging techniques. The goal is to replace buzzwords with intuition so learners can reason about architectures and training dynamics with confidence. -
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Meta Agents Research Environments (ARE)
Meta Agents Research Environments is a comprehensive platform
...It is designed to evaluate AI agents in dynamic, evolving, multi-step tasks. Unlike static benchmarks, ARE supports environments where agents must adapt to changes over time and reason over sequences of actions. It interacts with applications and faces uncertainty. The included Gaia2 benchmark offers 800 scenarios across multiple “universes”. It can test reasoning, memory, tool use, and adaptability. Integration with simulated applications/agent APIs (email, file system, etc.). Support for multiple AI model backends/providers. -
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Linkedin Scraper
A library that scrapes Linkedin for user data
Linkedin Scraper is a library that scrapes Linkedin for user data. Version 2.0.0 and before is called linkedin_user_scraper and can be installed via pip3 install --user linkedin_user_scraper. The reason is that LinkedIn has recently blocked people from viewing certain profiles without having previously signed in. So by setting scrape=False, it doesn't automatically scrape the profile, but Chrome will open the linkedin page anyways. You can login and logout, and the cookie will stay in the browser and it won't affect your profile views. ... -
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Claude Code Video Vision
Give Claude the ability to watch and understand videos
Claude Video Vision is a plugin designed for Claude Code that enables large language models to process and understand video content by transforming it into multimodal inputs the model can reason over. 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. ... -
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Orleans
Orleans is a cross-platform framework for building distributed apps
...As such, it helps developers experienced with single-server applications transition to building resilient, scalable cloud services and other distributed applications. For this reason, Orleans has often been referred to as "Distributed .NET". It was created by Microsoft Research and introduced the Virtual Actor Model as a novel approach to building a new generation of distributed systems for the Cloud era. The core contribution of Orleans is its programming model which tames the complexity inherent to highly-parallel distributed systems without restricting capabilities or imposing onerous constraints. -
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opensrc
Fetch source code for npm packages
...This gives AI coding assistants richer context about functions, internal logic, and architectural patterns used within external packages. The tool is designed to integrate into AI-driven developer workflows where coding agents explore repositories, inspect dependencies, and reason about how to use libraries correctly. -
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GLM-OCR
Accurate × Fast × Comprehensive
...Designed to handle text recognition, table parsing, formula extraction, and general information retrieval from documents containing mixed content, GLM-OCR excels across major benchmarks while remaining highly efficient with a relatively compact parameter size (~0.9B), enabling deployment in high-concurrency services and edge environments. The model’s multimodal capabilities allow it to reason across image and text content holistically, capturing structured and unstructured information from pages that include dense tables, seals, code snippets, and varied document graphics. GLM-OCR integrates a comprehensive SDK and inference toolchain that makes it easy for developers to install, invoke, and embed into production pipelines with simple commands or APIs. -
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Google APIs Client Library for Go
Auto-generated Google APIs for Go
...Due to the auto-generated nature of this collection of libraries they may contain breaking changes from one release to the next. The generator itself and the code it produces are considered beta for this reason. These client libraries are officially supported by Google. However, the libraries are considered complete and are in maintenance mode. This means that we will address critical bugs and security issues but will not add any new features. If you're working with Google Cloud Platform APIs such as Datastore or Pub/Sub, please use the Cloud Client Libraries for Go instead. ...
