Search Results for "optimization" - Page 15
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Showing 1232 open source projects for "optimization"
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Ultralytics
Ultralytics YOLO
...The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. Its architecture emphasizes performance optimization, balancing speed and accuracy to support real-time applications across industries. Ultralytics also provides pretrained models and flexible configuration options, allowing users to adapt the system to different datasets and use cases with minimal effort. -
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Numba CUDA Target
The CUDA target for Numba
...It is also used as a foundation for accelerating higher-level libraries such as RAPIDS, where custom user-defined GPU functions are required. The repository represents the continuation of CUDA support after its deprecation in core Numba, ensuring ongoing development and optimization under NVIDIA’s ecosystem. -
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BigQuery Utils
Useful scripts, udfs, views, and other utilities for migration
...The repository is especially useful for organizations that need practical building blocks for migration from other database systems, since it includes compatibility-oriented utilities and migration-focused UDFs that mimic behavior from platforms such as Oracle, Redshift, and Netezza. It also supports day-to-day operational work by offering optimization scripts, billing queries, performance testing examples, and dashboards built on INFORMATION_SCHEMA metadata so teams can better understand slot usage, reservations, job execution, and errors. -
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VoxelMorph
Unsupervised Learning for Image Registration
VoxelMorph is an open-source deep learning framework designed for medical image registration, a process that aligns multiple medical scans into a common spatial coordinate system. Traditional image registration techniques typically rely on optimization procedures that must be executed separately for each pair of images, which can be computationally expensive and slow. VoxelMorph approaches the problem using neural networks that learn to predict deformation fields that transform one image so that it aligns with another. Once the model has been trained, it can rapidly compute the transformation required to register new image pairs, significantly reducing computational time compared to classical registration algorithms. ... -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
...The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. The framework also includes monitoring mechanisms that track agent trajectories and ensure that tasks remain aligned with overall objectives. -
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LLMs-Zero-to-Hero
From nobody to big model (LLM) hero
LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement... -
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LMOps
General technology for enabling AI capabilities w/ LLMs and MLLMs
LMOps is a research initiative and open-source toolkit focused on the development and operational management of AI applications built with large language models and generative AI systems. The project explores the technologies and methodologies required to move foundation models from research environments into production-grade AI products. It includes experimental tools and frameworks that help developers optimize prompts, design workflows for generative models, and manage the lifecycle of... -
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LatentMAS
Latent Collaboration in Multi-Agent Systems
LatentMAS is an advanced framework for multi-agent reinforcement learning (MARL) that uses latent variable modeling to bridge perception and decision-making in environments where agents must coordinate under uncertainty. It provides mechanisms for agents to learn high-level latent representations of states, which simplifies complex sensory inputs into compact, actionable embeddings that facilitate both individual policy learning and inter-agent coordination. Using this latent space, the... -
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Bubble Lab
Open source workflow automation platform built for developers
...Execution tracing, cost tracking, observability dashboards, and logs give engineers full visibility into performance, errors, and dependencies so debugging and optimization are straightforward. -
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Markdown Site
An open-source publishing framework built for AI agents and developers
...The framework supports an efficient authoring experience via a simple terminal sync command that keeps content synchronized across connected browsers, AI tools, and discovery engines. By leveraging Convex for backend real-time data management and Netlify for static hosting, markdown-site enables rich publishing features like SEO optimization, full-text search, analytics dashboards, and AI-indexed content tailored to LLM workflows. -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant... -
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better-all
Better Promise.all with automatic dependency optimization
better-all is a TypeScript library that reinvents the familiar Promise.all construct by automatically analyzing and optimizing dependency graphs between asynchronous tasks, enabling maximal parallelization without manual orchestration. It addresses a common limitation where developers must manually refactor their promise chains to achieve efficient concurrency when some tasks depend on others, which can be error-prone and hard to maintain. With an object-based API, each task is declared as... -
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Go Katas
A collection of daily coding challenges
...It mirrors the kata practice tradition from martial arts—repetitive, thoughtful practice where each exercise reinforces technique, discipline, and problem-solving approach. Each kata prompt focuses on a precise aspect of Go, such as concurrency patterns, memory management, interfaces, error handling, or performance optimization, giving learners structured practice opportunities that go beyond syntax. Implementations can be tested locally, graded automatically with included test suites, and iterated on so that learners get rapid feedback and measurable progress. Because Go is widely used for backend services, cloud tooling, and systems programming, this repository helps participants build confidence in writing reliable, idiomatic, and maintainable code in real environments. -
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UI Skills
A growing set of skills to polish interfaces built by agents
UI Skills is a curated collection of skills designed to help AI agents and tools automatically improve user interfaces by applying best-practice adjustments and fixes, making frontend workflows more reliable and performant. It provides a suite of modular routines that can be added to an AI agent’s pipeline so that interfaces are polished for accessibility, metadata correctness, performance, and visual consistency without requiring manual tweaking for each change. The project includes skills... -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
Anthropic's Original Performance repository contains the publicly released version of a performance challenge originally used by Anthropic as part of their technical interview process, offering developers the opportunity to optimize and benchmark low-level code against simulated models. The project sets up a baseline performance problem where participants work to reduce simulated “clock cycles” required to run a given workload, effectively challenging them to engineer faster code under... -
16
ngraph.path
Path finding in a graph
...It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. It can be integrated with visualization libraries like VivaGraphJS to animate or highlight computed paths in a rendered graph, enabling interactive routing features. ... -
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DeployStack
Centralized credential vault, governance, and token optimization
DeployStack is an open-source framework that helps developers and teams define and deploy production infrastructure stacks using modular, reusable templates, often with IaC (infrastructure as code) principles. It provides a structured way to compose resources such as cloud networking, compute, and managed services into coherent deployment blueprints that can be versioned and reused across projects. By abstracting common deployment patterns and capturing them as templates, Deploystack reduces... -
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OpenOps
The batteries-included, No-Code FinOps automation platform
OpenOps is an open-source, no-code FinOps automation platform designed to help organizations manage and optimize cloud financial operations with minimal manual effort. At its core, OpenOps provides a visual workflow builder that lets teams construct automated processes for cloud cost optimization, budgeting, tagging, allocation, forecasting, and anomaly management without writing code, making complex financial workflows approachable for both technical and financial users. The platform includes an integrated spreadsheet-like database called OpenOps Tables and built-in analytics for tracking metrics and visualizing cost trends, enabling teams to consolidate disparate cloud cost data into actionable dashboards. ... -
19
llm.c
LLM training in simple, raw C/CUDA
llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and... -
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OpenNext for Cloudflare
Open Next.js adapter for Cloudflare
This sibling project targets Cloudflare’s edge platform, adapting Next.js apps to run on Workers and related services. It converts server components, API routes, and middleware into Worker scripts, while placing static assets in globally distributed storage for low-latency delivery. The adapter accounts for edge constraints—limited runtime APIs, streaming semantics, and per-request cold starts—so Next.js features remain functional at the perimeter. It integrates with platform primitives like... -
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NVIDIA AgentIQ
The NVIDIA AgentIQ toolkit is an open-source library
...It provides a framework-agnostic platform that integrates seamlessly with various data sources and tools, enabling developers to build composable and reusable agentic workflows. By treating agents, tools, and workflows as simple function calls, AgentIQ facilitates rapid development and optimization of AI-driven applications, enhancing collaboration and efficiency in complex tasks. -
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SaaS Boilerplate
SaaS Boilerplate built with Next.js + Tailwind CSS + Shadcn UI
...Packed with essential features like built-in Authentication, Multi-Tenancy with Team support, Role & Permission, Database, I18n (internationalization), Landing Page, User Dashboard, Form handling, SEO optimization, Logging, Error reporting with Sentry, Testing, Deployment, Monitoring, and User Impersonation, this SaaS template provides everything you need to get started. Designed with developers in mind, this Next.js Starter Kit uses TypeScript for type safety and integrates ESLint to maintain code quality, along with Prettier for consistent code formatting. ... -
23
TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
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Laravel Page Speed
Package to optimize your site automatically
Simple package to minify HTML output on demand which results in a 35%+ optimization. Laravel Page Speed was created by Renato Marinho, and currently maintained by João Roberto P. Borges, Lucas Mesquita Borges and Renato Marinho. The RemoveQuotes::class filter eliminates unnecessary quotation marks from HTML attributes. While required by the various HTML specifications, browsers permit their omission when the value of an attribute is composed of a certain subset of characters (alphanumerics and some punctuation characters). ... -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API.
