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SuperDuperDB fernando@superduperdb.com via sendinblue.com
and "Fernando" is Fernando Guerra, their "Business, Marketing and Growth"
not cool and immediately put me off
like others who are complaining here about spam from this project I too just got a spam email from them to my personal email... SuperDuperDB fernando@superduperdb.com via sendinblue.com and "Fernando" is Fernando Guerra, their "Business, Marketing and Growth" not cool and immediately put me off
like others who are complaining here about spam from this project I too just got a spam email from them to my personal email... SuperDuperDB fernando@superduperdb.com via sendinblue.com and "Fernando" is Fernando Guerra, their "Business, Marketing and Growth" not cool and immediately put me off
Fernando already replied to this thread acting like he has no connections. https://news.ycombinator.com/item?id=38530381
like others who are complaining here about spam from this project I too just got a spam email from them to my personal email... SuperDuperDB fernando@superduperdb.com via sendinblue.com and "Fernando" is Fernando Guerra, their "Business, Marketing and Growth" not cool and immediately put me off
like others who are complaining here about spam from this project I too just got a spam email from them to my personal email... SuperDuperDB fernando@superduperdb.com via sendinblue.com and "Fernando" is Fernando Guerra, their "Business, Marketing and Growth" not cool and immediately put me off
We are really sorry for the inconvenience. The assumption for contacting was around thinking you would be interested in the project. Again, we are very sorry for bothering you, this will never happen again.
Earlier quoted context omitted.
All things aside, it's a framework for building data workflows in Python. Like taking the data from that source (e.g., SQL), processing them (e.g., pytorch or openai), and storing the results somewhere (e.g., data on Mongo metadata on SQL). It actually consists of the following: 1. nifty abstractions for Data (e.g., sources, encoders, listeners), Metadata (e.g., vector indexes), Compute (e.g., sync, async, parallel).…
Ah, it’s a langchain competitor, possibly with better DB support. One of the nice things about langchain is the code examples, making it easy to get simple services up and running. And because it’s a toolkit I can take what I need and leave the rest. However, the ecosystem around langchain is really exploding, is there some way you can retool what you have to extend langchain with better DB support, rather than build…
However, achieving goals like "training your LLM" or enabling "real-time inference" requires more than just pipelines. For that, we have invested in enhancing compatibility with databases and facilitating parallel computing.
About your last point, I 'm not sure I fully understand. Do you mean to write a guide for moving lang-chain models to superduper? Or to create superduper wrappers for langchain ? Or to move the core functionalities of superduper to langchain ?
The guide, is something have in our immediate plans. The wrappers are under discussion. The latter I don't think it's possible due to architectural differences. For example, superduper is designed with multi-node environments in mind.