Search Results for "translation image"
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Showing 2 open source projects for "translation image"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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Loan management software that makes it easy.Bryt Software is ideal for lending professionals who are looking for a feature rich loan management system that is intuitive and easy to use. We are 100% cloud-based, software as a service. We believe in providing our customers with fair and honest pricing. Our monthly fees are based on your number of users and we have a minimal implementation charge.
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Lad
Lad is the best Node.js framework
Lad is the best Node.js framework. Made by a former Express TC and Koa team member. Lad boasts dozens of features and is extremely configurable. These microservices are preconfigured for security, performance, and graceful reloading. Redis, sessions, flash toast, and modal SweetAlert2 messages (uses ioredis which has support for Cluster, Sentinel, and more). Koa-based web app and API servers. Pagination built-in (using ctx-paginate). RESTful API with BasicAuth and versioning. Automated job... -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages.
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