Google Open Source Reviews

Jupyter Reviews

Google Open Source mentions (25)

• Sustainable Funding for Open Source: Navigating Challenges and Emerging Innovations

  Many companies that depend on OSS contribute financially so that the projects remain robust. Examples like Google and Microsoft have shown that corporate sponsorship is not only beneficial for maintainers but also for companies that rely on reliable software. The corporate sponsorship model moves away from traditional ad-based revenue generation, fostering a direct relationship between the sponsor and the... - Source: dev.to / 7 days ago

• Revolutionizing Blockchain and Open Source Funding: Microfunding and Project Funding Alternatives – A Comprehensive Guide

  Similarly, open source projects, which are the backbone of digital infrastructure, have long struggled to achieve sustainable funding. Crowdfunding platforms such as Kickstarter, Opencollective, and corporate sponsorships from technology giants like Google’s open source initiatives and Microsoft’s commitment to open source are now offering viable alternatives. Innovators have begun to integrate Non-Fungible Tokens... - Source: dev.to / 10 days ago

• Funding Open Source Innovation: Empowering Sustainable Maintenance and Development

  Governments, academic institutions, and major tech companies like Microsoft and Google have recognized the importance of financial support. Funding models have evolved to include corporate sponsorships, grants (e.g., Mozilla's Open Source Support Program), and community-driven donations through platforms like GitHub Sponsors and Open Collective. - Source: dev.to / 10 days ago

• Revolutionizing Blockchain and Open Source Funding: Microfunding and Project Funding Alternatives

  Sponsorship Programs: Platforms such as GitHub Sponsors and offerings from tech giants like Google Open Source and Microsoft Open Source provide recurring support while maintaining community values. - Source: dev.to / about 1 month ago

• Funding Open Source Software: Sustaining the Backbone of Modern Digital Innovation

  As digital economies matured, the limitations of relying solely on volunteer support became apparent. Numerous OSS projects found that a lack of steady revenue streams led to developer burnout, limited maintenance, and even stagnation. Today, the OSS landscape has evolved to incorporate a blend of funding methods that include individual donations for open source projects, crowdfunding via platforms like GitHub... - Source: dev.to / about 1 month ago

Jupyter mentions (216)

• The 3 Best Python Frameworks To Build UIs for AI Apps

  Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago

• LangChain: From Chains to Threads

  LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago

• Applied Artificial Intelligence & its role in an AGI World

  Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago

• Jupyter Notebook for Java

  Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago

• JIRA Analytics with Pandas

  One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago