Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 66
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19415 projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" with 1 filter applied:
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Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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The AI-powered unified PSA-RMM platform for modern MSPs.SuperOps.ai is the only PSA-RMM platform powered by intelligent automation and thoughtfully crafted for the new-age MSP. The platform also helps MSPs manage their projects, clients, and IT documents from a single place.
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Prompt Poet
Streamlines and simplifies prompt design for both developers
...By separating prompt structure from program logic, Prompt Poet encourages iterative prompt design and experimentation without requiring constant changes to application code. The framework supports dynamic prompts that adapt to runtime data, allowing developers to inject variables, context, and examples directly into templates. This approach is particularly useful in production environments where prompt consistency, maintainability, and versioning are important. -
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AgentEvolver
Towards Efficient Self-Evolving Agent System
...These mechanisms enable agents to continuously improve their capabilities while interacting with complex environments and tools. AgentEvolver also integrates environment sandboxes, experience management systems, and modular data pipelines to support large-scale experimentation. -
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DriveLM
Driving with Graph Visual Question Answering
...Instead of treating autonomous driving as a purely sensor-driven pipeline, DriveLM frames it as a reasoning problem where models answer structured questions about the environment to guide decision making. The system includes DriveLM-Data, a dataset built on driving environments such as nuScenes and CARLA, where human-written reasoning steps connect different layers of driving tasks. This design allows models to learn relationships between objects, behaviors, and navigation decisions through graph-structured logic. -
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BuildingAI
Build your own AI application system for free
...The platform aims to bridge the gap between natural language interfaces and building design tools by allowing AI systems to interpret user instructions and convert them into structured architectural operations. By combining generative AI capabilities with building data models, the system can assist with tasks such as design generation, spatial reasoning, and building component creation. The project is intended for architects, engineers, and developers exploring how AI can automate or augment design workflows in the architecture, engineering, and construction industries. It supports interactions where users describe building features, layouts, or modifications in natural language and the AI translates those instructions into actionable design operations. -
The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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PKU Beaver
Constrained Value Alignment via Safe Reinforcement Learning
PKU Beaver is an open-source research project focused on improving the safety alignment of large language models through reinforcement learning from human feedback under explicit safety constraints. The framework introduces techniques that separate helpfulness and harmlessness signals during training, allowing models to optimize for useful responses while minimizing harmful behavior. To support this process, the project provides datasets containing human-labeled examples that encode both... -
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WebGLM
An Efficient Web-enhanced Question Answering System
...The system is based on the General Language Model architecture and was designed to enable language models to interact directly with web information during the question-answering process. Instead of relying solely on knowledge stored in the model’s training data, the system retrieves relevant web content and integrates it into the reasoning process. WebGLM introduces several components that coordinate this process, including a retrieval module that selects relevant web documents, a generator that produces answers, and a scoring system that evaluates the quality of generated responses. The architecture aims to improve the reliability and usefulness of AI systems that answer questions about current or external knowledge sources. -
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code-act
Official Repo for ICML 2024 paper
...This approach helps unify reasoning and action planning within large language model agents by using code as the primary interface between the model and the external world. The framework also includes training data, models, and evaluation tools designed to study how language models can become more capable autonomous agents. -
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LongWriter
Unleashing 10,000+ Word Generation from Long Context LLMs
LongWriter is an open-source framework and set of large language models designed to enable ultra-long text generation that can exceed 10,000 words while maintaining coherence and structure. Traditional large language models can process large inputs but often struggle to generate long outputs due to limitations in training data and alignment strategies. LongWriter addresses this challenge by introducing a specialized dataset and training approach that encourages models to produce longer responses. The system uses an agent-based pipeline called AgentWrite that decomposes large writing tasks into smaller subtasks, allowing the model to produce long documents section by section. ... -
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LlamaGen
Autoregressive Model Beats Diffusion
LlamaGen is an open-source research project that introduces a new approach to image generation by applying the autoregressive next-token prediction paradigm used in large language models to visual generation tasks. Instead of relying on diffusion models, the framework treats images as sequences of tokens that can be generated progressively using transformer architectures similar to those used for text generation. The project explores how scaling autoregressive models and improving image... -
Network Management Software and Tools for Businesses and Organizations | Auvik NetworksReduce IT headaches and save time with a proven solution for automated network discovery, documentation, and performance monitoring. Choose Auvik because you'll see value in minutes, and stay with us to improve your IT for years to come.
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Magicoder
Empowering Code Generation with OSS-Instruct
...This technique uses open-source code repositories as a foundation for generating more realistic and diverse instruction datasets for training language models. By grounding training data in real open-source examples, Magicoder aims to reduce bias and improve the reliability of code generation results compared to models trained solely on synthetic instructions. The project includes model implementations, training resources, and evaluation benchmarks that demonstrate how the approach improves instruction-following and code synthesis capabilities. ... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
...By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the ability of recurrent networks to capture long-range dependencies in sequential data. The architecture aims to provide competitive performance with transformer-based models while maintaining advantages such as linear computational scaling and efficient memory usage for long sequences. Researchers have demonstrated that xLSTM models can scale to billions of parameters and large training datasets while maintaining efficient inference speeds. -
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Lagent
A lightweight framework for building LLM-based agents
...The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. Its design emphasizes simplicity and flexibility so that developers can experiment with different agent workflows without needing a complex infrastructure setup. ... -
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OmAgent
Build multimodal language agents for fast prototype and production
OmAgent is an open-source Python framework designed to simplify the development of multimodal language agents that can reason, plan, and interact with different types of data sources. The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. ... -
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vLLM Semantic Router
System Level Intelligent Router for Mixture-of-Models at Cloud
...This approach allows developers to combine multiple models with different strengths, such as lightweight models for simple queries and more advanced reasoning models for complex tasks. The router operates as an intelligent layer between users and model infrastructure, capturing signals from prompts, responses, and contextual data to improve decision-making. It can also integrate safety and monitoring mechanisms that detect issues such as jailbreak attempts, hallucinations, or sensitive information exposure. -
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ai-cookbook
Examples and tutorials to help developers build AI systems
...The repository contains examples that demonstrate how to build AI workflows using modern tools such as large language models, autonomous agents, and external APIs. Developers can learn how to construct applications like intelligent assistants, automation pipelines, and AI-powered data analysis tools through step-by-step tutorials and ready-to-run scripts. The code examples are designed to emphasize practical architecture patterns that are commonly used in production environments, helping developers understand how to integrate AI services into software products. -
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RunAnywhere
Production ready toolkit to run AI locally
...The toolkit allows developers to integrate language models, speech recognition, and voice synthesis capabilities into mobile or desktop applications while keeping all computation local. By running models entirely on device, the platform eliminates network latency and protects user data because information does not leave the device. The SDK supports popular open-source models such as Llama, Mistral, and Qwen, enabling developers to build AI-powered features such as chat interfaces and voice assistants with minimal external dependencies. It also includes integrated pipelines that combine speech-to-text, large language models, and text-to-speech into a complete conversational system. -
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Ling-V2
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI
...It introduces highly sparse architectures where only a fraction of the model’s parameters are activated per input token, enabling models like Ling-mini-2.0 to achieve reasoning and instruction-following capabilities on par with much larger dense models while remaining significantly more computationally efficient. Trained on more than 20 trillion tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-V2’s models demonstrate strong general reasoning, mathematical problem-solving, coding understanding, and knowledge-intensive task performance. -
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Jesse
An advanced crypto trading bot written in Python
...It acts as an agent manager where you can define tasks, contexts, and tool integrations so that AI reasoning is reliably connected to deterministic procedures like API calls, data retrieval, and task execution. Jesse emphasizes structured outputs, safety boundaries, and explainable reasoning, helping users design agents that don’t just hallucinate responses but can coordinate steps, validate conditions, and interact with systems predictably. It also includes built-in support for evaluation, logging, and orchestration so developers can monitor, test, and improve agent behavior over time. ... -
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VibeKit
Run Claude Code, Gemini, Codex in a clean, isolated sandbox
...It provides a set of abstractions and utilities that let developers connect generative models to UI frameworks, sensors, event streams, and external services without having to build plumbing from scratch. Instead of treating AI models as black boxes behind simple prompts, Vibekit encourages developers to define declarative behaviors, reactive rules, and data flows that make the outputs of models part of living application logic. This can include things like dynamic content generation, live adaptation based on user interaction, and connectors to external APIs for enriched grounding. The toolkit also supports testing and local iteration, with utilities that simulate event streams and mock model responses to make development predictable. -
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c15t
The Developer-First Cookie Banner
...Rather than relying on heavy third-party scripts or services, it offers a flexible, headless engine that web teams can integrate directly into their front-end and edge stacks with minimal overhead, giving full control over user experience and data handling. c15t runs entirely in the front end or at the edge, blocking or allowing scripts based on user decisions and tracking those states for observability and auditing. It provides framework-agnostic core logic as well as adapters and components for popular frameworks like React and Next.js, making it easy to scaffold banner UIs, preference centers, and consent dialogs while keeping performance high and bundle sizes low. -
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Nano Events
Simple and tiny (107 bytes) event emitter library for JavaScript
...Rather than offering many complex features, nanoevents focuses on the core primitives: creating an emitter, subscribing to named events, emitting events with arbitrary data, and unsubscribing. Because of its minimal API surface and implementation, it’s very easy to integrate into frontend or backend JS/TypeScript projects without introducing significant dependencies or weight. The simplicity reduces cognitive load: developers don’t need to read long docs or worry about advanced event semantics. -
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Underscore.js
JavaScript's utility belt
Underscore.js is a JavaScript utility-library created by Jeremy Ashkenas that provides a broad set of functions for working with arrays, objects, functions, and other data types — essentially a “utility belt” for functional programming in JS. Instead of extending built-in objects or modifying prototypes, Underscore provides its helpers in a single _ namespace, enabling cross-browser support and consistent behaviour across environments. It offers map/filter/reduce, deep-cloning, templating, object traversal, function binding, and more, which makes it easier to write concise, expressive code rather than verbose loops and conditional logic. ... -
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Google API JavaScript Client
Google APIs Client Library for browser JavaScript, aka gapi
This library (often used via gapi) provides a browser-friendly way to call Google APIs using OAuth 2.0 and generated discovery documents. It abstracts discovery, auth, and HTTP details so developers can focus on domain calls like Drive file operations, Calendar events, or YouTube data. The client can load APIs dynamically at runtime, which keeps bundles small and allows late-binding to specific services and versions. It includes helpers for incremental auth, token refresh, and scopes so apps can request only what they need when they need it. Responses are returned as promise-based calls with typed parameters derived from service definitions, simplifying error handling and pagination. ... -
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BTree implementation for Go
BTree provides a simple, ordered, in-memory data structure for Go
This package is a high-performance, in-memory B-tree for Go that implements an ordered set/map with efficient insert, delete, and range iteration. It’s parameterized by tree degree so callers can tune cache behavior and memory overhead for their workload. Instead of relying on Go’s built-in maps—which are hash-based and unordered—btree preserves sorted order and provides rich traversal APIs like ascending, descending, and range scans. The implementation favors minimal allocations and... -
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LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and validation via automatically generated interactive HTML visualizations. LangExtract supports a wide range of models, including Google Gemini, OpenAI GPT, and local LLMs via Ollama, making it adaptable to different deployment environments and compliance needs. ...
