- I've seen neural nets using int8 for matrix multiplication
to reduce memory size [1]. Do you think something similar could be useful in the ANN space?
- Do you know of any studies using Faiss looking at speed/cost tradeoffs of RAM vs flash vs Disk for storage?
- Are there recommended ways to update Faiss index with streaming data, e.g. updating the vectors continuously?
Seems like more and more use cases for Faiss as neural nets become more and more core to workflows. Would like to try and figure out the configurations that are optimized to minimize carbon usage in addition to latency and recall metrics.
Definitely not unusual. I think it is pretty common for executive team in addition to founders. My feeling is that VCs and founders need to find a way to partially cash out rank and file employees along the way if they want start up model to succeed long term. Many senior engineers are reluctant to to join startups at this point as even if startup is successful it can be a long time before they have the money in their pocket. Employees at Reddit, Stripe, Instacart, Databricks, and many others have been waiting over a decade for company success to hit their wallet.
Sometimes the executive team gets stock options rather than RSUs so they own the stock and can sell to secondary parties. VCs and founders would like them to sell to known parties rather than sell on private market (Facebook crossing 500 investors threshold was one important reason for IPO timing [1]).
I agree, but as aleksiy123 suggests there is an additional complexity burden and it is a long journey to teach users to make use of a new technology. I think a lot of "advanced" features get de-prioritized as not many people use them and it seems like resources could be better spent helping the masses. I think that the importance of "advanced" features is often under rated by traditional engagement models. Wikipedia is a great example of where less than 1% of users click on the edit button, but that 1% adds all the value for the other 99%.
Do you think part of this is that Netflix has assumed zero effort from user model? My experience has been that Netflix does an ok job of recommendations, but fails at overall discovery experience. There is no way for me to drive or view content from different angles easily. I end up googling for expert opinions or hitting up rotten tomatoes to get better reviews. Netflix knows a ton about me and their content, but seems to do a poor job of making their content browseable/discoverable overall. I do like their "more like this" feature where I can see similar titles.
Sam has already helped you indirectly by investing in reddit which has all sorts of community sourced help. Check out PeaceH guide to getting discipline[1]
Could you comment on why start ups with remote teams anecdotally do poorly but many open source projects with very remote teams succeed? Do you see any tools on the horizon to help people coordinate remotely at a new level?