An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
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Writer
Score 9.1 out of 10
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Writer is a full-stack generative AI platform. It consists of Writer-built LLMs, a graph-based RAG, AI guardrails, and a flexible application layer. Writer boasts users at enterprises like L’Oreal, Vanguard, and Accenture.
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
Carrying out the quality control process allows me to carry out the organization, details [are] written instantly, and progress of the same as well as personalize them. Regarding the interface, I would change it for a more graphic one. On the other hand[,] writer navigates my content of complex documents and thus be able to have shortcuts[.]
There is some setup required to get your word lists in there, adjust your editing style, and set up typical errors, but otherwise it works similarly to other programs (but worth it in my opinion).
Their web editor loses formatting, which several team members have complained about. However, formatting is properly retained in GDocs, Word, and the Chrome extension. There is some setup required to get your word lists in there, adjust your editing style, and set up typical errors, but otherwise it works similarly to other programs (but worth it in my opinion).
I wish it were easy to provide solutions to complex problems rather than simply highlighting them.
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
Grammarly is very advanced, but I use Writer as It offers me additional features like word count, a cleaner interface, and a word processor for taking notes.
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
