> Maybe you mentioned it in your demo and I missed it, but how does this differ pasting the log messages to ChatGPT / Claude / another LLM? Is it mainly that yours can iterate over a large logfile without blowing up the context window?
We do quite a bit of aggregation over the log file, and generate summary stats and choose what bits to stuff in the LLM. Plan to support more platforms than just spark.
> Does it suffer from the same issue as other LLMs, where it will always identify potential optimizations or improvements even if none are truly needed?
Funnily enough, instructing sonnet-3.7 to not suggest unnecessary optimisations seems to have done the trick!
thanks for the feedback! the first version had a lot more detailed code but decided to go with linking to our GitHub than copying all the code. Wanted to illustrate the core touch points involved in extending DF.
DataFusion is primarily a batch OLAP system, so we should be able to support hybrid workloads as well. And definitely agree with you re: Polars dev exp. That is something we are aiming for with our forthcoming Python sdk.
> It'd also be important/useful to support Python udfs (think numpy/jax/etc.).
Yep that's our longterm gameplan.
> It'd be very cool if you could collaborate with or even tap into the polars frontend. If you could execute polars logical plans but with a streaming source, that would be huge.
Are there examples of project that do this? I'd be very much interested in looking into this.
we absolutely do, the library itself is designed to be extensible. we are currently working on adding webhooks as one of our sources. are there are any specific connectors/sources you'd be interested in?
while haven't checked out Fluvio yet, we are fans of Arroyo. regarding latter my understanding is that the team is going for a SQL first complete replacement for Flink. Denormalized is meant to be an embeddable engine you can import within your project. Our plan is to focus on the developer experience for users building with Python and Typescript in particular.
thanks @ztratar. would love to hear about your workloads at embra would be very helpful vis-a-vis the direction of our typescript experience. feel free to drop us an email: [email protected]
thanks for the encouraging words @ethegwo. Tonbo looks very cool and potentially something we could use for our state backend (currently using RocksDB which we aren't that happy about). Would love to chat about how we can work together. Feel free to reach out to me - [email protected]
lmao... figured it was radius reduction or somesuch. i ended up working on Winterfell (the service that managed automated radius reduction) at some point during my time there.
even for 2015 (when this was likely taken) Uber Eats would most certainly not have run from a single laptop. Core services like dispatch, matching, pricing etc in any case weren't geography specific. If i had to guess, this was some Ops person running a local driver incentive, payout query + email workflow from their laptop.
Have been playing around with this a bit lately and the OS integration is just really well done. Very nifty interface for shoving context from across your apps into ChatGPT.
pre-IPO RSUs usually come with a double trigger. meaning you vest them monthly or quarterly etc but a liquidity event such as an IPO, is needed to actually "vest" them in a taxable sense.