JS7 JobScheduler
JS7 JobScheduler, an Open Source Workload Automation System, is designed for performance and resilience. JS7 implements state-of-the-art security standards. It offers unlimited performance for parallel executions of jobs and workflows.
JS7 provides cross-platform job execution and managed file transfer. It supports complex dependencies without the need for coding. The JS7 REST-API allows automation of inventory management and job control.
JS7 can operate thousands of Agents across any platform in parallel.
Platforms
- Cloud scheduling for Docker®, OpenShift®, Kubernetes® etc.
- True multi-platform scheduling on premises, for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid cloud and on-premises use
User Interface
- Modern GUI with no-code approach for inventory management, monitoring, and control using web browsers
- Near-real-time information provides immediate visibility to status changes, log outputs of jobs and workflows.
- Multi-client functionality, role-based access management
- OIDC authentication and LDAP integration
High Availability
- Redundancy & Resilience based on asynchronous design and autonomous Agents
- Clustering of all JS7 Products, automatic fail-over and manual switch-over
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Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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bolt.diy
bolt.diy is an open-source platform that empowers developers to effortlessly create, run, modify, and deploy comprehensive web applications utilizing a variety of large language models (LLMs). It encompasses a diverse selection of models, such as OpenAI, Anthropic, Ollama, OpenRouter, Gemini, LMStudio, Mistral, xAI, HuggingFace, DeepSeek, and Groq. The platform facilitates smooth integration via the Vercel AI SDK, enabling users to tailor and enhance their applications with their preferred LLMs. With an intuitive user interface, bolt.diy streamlines AI development workflows, making it an excellent resource for both experimentation and production-ready solutions. Furthermore, its versatility ensures that developers of all skill levels can harness the power of AI in their projects efficiently.
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16x Prompt
Optimize the management of source code context and generate effective prompts efficiently. Ship alongside ChatGPT and Claude, the 16x Prompt tool enables developers to oversee source code context and prompts for tackling intricate coding challenges within existing codebases. By inputting your personal API key, you gain access to APIs from OpenAI, Anthropic, Azure OpenAI, OpenRouter, and other third-party services compatible with the OpenAI API, such as Ollama and OxyAPI. Utilizing these APIs ensures that your code remains secure, preventing it from being exposed to the training datasets of OpenAI or Anthropic. You can also evaluate the code outputs from various LLM models, such as GPT-4o and Claude 3.5 Sonnet, side by side, to determine the most suitable option for your specific requirements. Additionally, you can create and store your most effective prompts as task instructions or custom guidelines to apply across diverse tech stacks like Next.js, Python, and SQL. Enhance your prompting strategy by experimenting with different optimization settings for optimal results. Furthermore, you can organize your source code context through designated workspaces, allowing for the efficient management of multiple repositories and projects, facilitating seamless transitions between them. This comprehensive approach not only streamlines development but also fosters a more collaborative coding environment.
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