Search Results for "engineering" - Page 7
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10096 projects for "engineering" with 1 filter applied:
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Figma Code Connect
A tool for connecting your design system components
...The system supports multiple frameworks and languages, enabling teams to generate usable code snippets, style values, and component metadata from visual designs without manual translation. This approach reduces miscommunication and repetitive tasks that traditionally occur between design and engineering teams, speeding up iteration cycles while preserving fidelity to the original design intent. It can be integrated into development environments or build tooling so design updates automatically propagate into code patterns or UI specs. -
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csghub-server
csghub-server is the backend server for CSGHub
...The server acts as a centralized management layer that allows teams to store, organize, and operate AI assets such as models, datasets, and machine learning applications in a manner similar to artifact repositories used in software engineering. Built primarily in the Go programming language, the system enables organizations to run model inference, training, and fine-tuning tasks within a unified platform. It integrates capabilities similar to model repositories like Hugging Face while allowing enterprises to host and manage their AI assets internally for security and compliance purposes. -
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codemaxxed
CLI tool purpose-built to generate the maximum possible lines of code
...The project intentionally produces nonsensical or low-quality code, including exaggerated naming patterns and redundant structures, to highlight the absurdity of equating quantity with quality in software engineering. It serves more as a conceptual or artistic experiment than a practical tool, illustrating how metrics can be gamed in modern development environments. -
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
ai-notes is a curated repository of notes focused on the evolving landscape of artificial intelligence, particularly generative models and large language models. It is designed to help software engineers quickly understand modern AI concepts, tools, and developments through structured documentation and research notes. It functions as a living knowledge base composed of numerous markdown files that organize topics such as text generation, image generation, AI infrastructure, and code... -
Forever Free Full-Stack Observability | 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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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual... -
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RuVector
Self-Learning, Vector Graph Neural Network, and Database built in Rust
RuVector is part of the broader rUv ecosystem of AI engineering tools and focuses on enabling advanced vector-based processing and intelligent system development within agentic and AI-driven pipelines. The project fits into a larger vision of modular, composable AI infrastructure designed to support autonomous agents, data retrieval, and intelligent automation workflows. It emphasizes extensibility and interoperability with modern AI stacks, allowing developers to integrate vector operations into search, reasoning, or generative systems. ... -
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System Prompts Leaks
Collection of extracted System Prompts from popular chatbots
System Prompts Leaks is a curated repository that collects known leaked or publicly exposed system prompts used by large language models, organized so researchers, developers, and AI safety advocates can analyze them in one place. The project highlights how system prompts — instructions that strongly influence model behavior — have been inadvertently shared in forums, datasets, and open repositories, illustrating common patterns and potential vulnerabilities in prompt design and deployment.... -
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Akamai Application Platform (for LKE)
App Platform for Linode Kubernetes Engine
apl-core is the open source heart of Akamai’s App Platform for Linode Kubernetes Engine, packaging a curated set of Kubernetes building blocks into a turnkey “platform engineering” layer. Instead of assembling ingress, certificates, GitOps, secrets, and observability by hand, you install a single Helm chart and get a consistent, production-oriented baseline on LKE or any conformant Kubernetes cluster. The project provides a guided path after installation, post-install setup, and hands-on labs, so teams can go from a fresh cluster to shipping services with guardrails in place. ... -
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C3
The goal of CLAIMED is to enable low-code/no-code rapid prototyping
...The system emphasizes reproducibility and scalability, allowing researchers and engineers to reuse existing components and integrate them into larger scientific or data engineering workflows. It also aims to support trusted and explainable AI systems by integrating tools for fairness analysis, explainability, and adversarial robustness. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Machine Learning for Software Engineers
A complete daily plan for studying to become a machine learning engine
...It aggregates a wide range of resources including books, online courses, Kaggle competitions, podcasts, conferences, and community learning opportunities. The repository is structured to help learners gradually build the skills required for machine learning engineering positions while maintaining a focus on real-world application development. -
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AI Data Science Team
An AI-powered data science team of agents
...It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. The project includes ready-to-use applications that showcase these agents in action, such as an exploratory data analysis copilot that generates reports, a pandas data analyst that combines wrangling and plotting, and SQL database agents that can query business databases and output results directly. -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. ... -
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Roadmap To Learn Generative AI In 2025
Basic Machine Learning Natural Language Processing Roadmap
Roadmap To Learn Generative AI In 2025 is a curated learning path focused on contemporary generative AI — covering large language models (LLMs), diffusion-based image generation, prompt engineering, multi-modal AI, fine-tuning techniques, and the practical considerations for deploying generative models. It’s aimed at learners and developers who already have some programming or ML basics and wish to specialize in generative AI, offering a modern, structured plan that reflects the state of the art as of 2025. The roadmap outlines recommended topics, sequential steps, and associated resources (tutorials, notebooks, project ideas) to build competence in generative modeling from conceptual understanding to implementation and deployment. ... -
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mlforecast
Scalable machine learning for time series forecasting
...It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. ... -
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Yandex Music API
Non-official Python library for works with API service Index
...In addition to implementing a clean API, this library has a number of — high-level wrapping classes in order to make the development of customers and scripts simple and understandable. All documentation was written from scratch based on logical analysis during reverse development (reverse engineering) API. -
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Chili3D
A browser-based 3D CAD application for online model design
...Chili3D includes a comprehensive set of modeling capabilities, such as parametric shape creation, boolean operations, extrusion, and surface manipulation, making it suitable for engineering, prototyping, and design workflows. It also features an intuitive interface with snapping, tracking, and hierarchical object management, which enhances precision and usability during complex modeling tasks. -
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AgentHub
GitHub is for humans. AgentHub is for agents
...The platform treats code repositories not just as storage for files but as active environments where agents can propose modifications, review changes, and exchange structured messages. This approach reflects emerging research in multi-agent software development where groups of AI systems collaborate on complex engineering problems. -
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Karpathy
An agentic Machine Learning Engineer
...It is intended primarily for research and experimentation with autonomous ML workflows rather than as a polished production platform. Overall, karpathy represents an early step toward fully automated machine learning engineering driven by agentic AI systems. -
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L1B3RT45
Harmless liberation prompts
L1B3RT4S is a large prompt collection project focused on adversarial and “liberation-style” prompt engineering experiments for large language models. The repository gathers creative prompt patterns intended to explore model behavior boundaries, roleplay scenarios, and red-teaming techniques. It is positioned more as a prompt experimentation archive than a traditional software library, emphasizing the study of how instruction phrasing can influence AI outputs. -
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Liveblocks
Liveblocks gives you the building blocks and infrastructure
...The platform is designed to work seamlessly with modern frameworks, offering pre-built components and integration guides that reduce complexity and accelerate development of features that would otherwise require substantial real-time engineering. -
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Easy-Vibe
Tutorial on Product Prototype, AI Capability Integration
...The learning path is divided into progressive stages that cover beginner concepts, full-stack development, and advanced multi-platform application development. Throughout the curriculum, learners explore topics such as prompt engineering, AI tool integration, product prototyping, and deployment strategies for AI-enabled applications. -
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Flutter Agent Skills
Agent skills for Flutter, maintained by the Flutter team
...It covers topics such as UI construction, state management, animations, asynchronous programming, architecture patterns, and testing workflows within Flutter applications. The repository is designed for developers at different experience levels, offering incremental progression from foundational concepts to more advanced mobile engineering techniques. It emphasizes clean coding practices, reusable components, and modern Flutter development patterns. Overall, Flutter Skills acts as a curated training environment for mastering cross-platform application development using Flutter. -
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Spring AI
An Application Framework for AI Engineering
Spring AI is a framework within the Spring ecosystem that simplifies the integration of artificial intelligence capabilities into Java applications by providing abstractions over various AI services and models. It is designed to align with familiar Spring development patterns, allowing developers to incorporate AI features such as chat, embeddings, and prompt handling using consistent APIs. The framework supports multiple providers, including OpenAI, Azure OpenAI, and other compatible... -
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CS-Ebook
Curated list of classic, high-quality computer science books
...It focuses on depth over volume, selecting only well-regarded titles that support structured learning and long-term skill development. It spans core areas such as computer fundamentals, programming languages, software engineering, mathematics, data science, and artificial intelligence, making it suitable for learners at different stages. Rather than hosting files, the project serves as a discovery guide, helping users identify essential reading materials and build a strong technical foundation. CS-Ebook is actively maintained and updated to reflect relevant and modern resources while preserving foundational texts. ... -
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Hello-Agents
Building an Intelligent Agent from Scratch
...The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems.
