1/2 We kept hearing about @LanceDB from companies like MidJourney that were using its open-source Lance table format for AI data. They saw orders of magnitude improvement in latency, storage costs, and more. A quick experiment confirms why: https://t.co/Ojndl1kEvk.
@mim_djo Ah yes, I had son some of the discussions happening around ownership which is something being added to v3 https://t.co/tguHY1OiIh.
@dennylee does delta have an ownership model or when do you check for a table collision?
GPT is not always the best model. Mistral is better at legal reasoning, Llama is better at stories.
@LeptonAI : ML engineering = Vercel : web dev. With a couple lines, you can deploy and fine tune any model, w/ metrics, monitoring, autoscaling in place: https://t.co/FNdqR9tFjg
@anna__geller I feel like this could be quickly misinterpreted in the cutthroat capitalistic machine we’re in.
“Jessica was taking a snaky approach and I just couldn’t follow.”
“He’s very snaky so I still check his indentation or anything else isn’t off.”
@ananthdurai Nah, you have to relate yourself to the ai boom, like say you integrate with a vector store, or have a function that asks ChatGPT something, and call it AI, or create a new query language on LLMs and call it English
If you haven't followed @anna__geller yet, she has an amazing writing style that lends itself to quickly ramping up on a variety of topics. As opposed to my goofy style, she keeps things interesting with story-telling, while keeping the lessons learned so clear.
This blog covers two topics near and dear to my heart.
I'm a huge @trinodb and @ApacheIceberg fan. Anna shows the most common implementations of Trino in the wild, @AWS Athena, querying Iceberg with a #AWSGlue.
Of course, all this can be orchestrated by @kestra_io. 😉