lyjackal

Wish this was clarified. One thought would be to choose a stable component to in turn hash, such as the source coordinate hexagon, but then why not just put that in the hashtag

While I’m usually one to also get annoyed at uncurated slop, this doesn’t strike me in any way as LLM text, and likely something the author meant in earnest. I reads lot of LLM text. What part about it appeared generated to you?

From my perspective the piece was well structured, paced and thought through (and yes, a bit long)

I saw this for Luna and then looked at the uptime and it said 85%. My interpretation is that this is just a gimmick where they serve the OpenAI flex tier at the same discount OpenAI provides for flex and then fall back to azure

They're an Evals company. Its right up there in the top nav under Evals > Vending Bench 2

You’re quoting the batch pricing. On demand is 1.5 per input and 9 per M output. This is effectively comparable cost to Gemini 2.5 Pro in a flash tier model

I think the larger part implied is the design will be crappy, because the problem was unexplored

I want to build a shared postgres db with hundreds of small apps (OLTP) accessing shared tables using a RLS model model against well defined tables.

What are other limitations and mitigations folks have used or encountered to support stability and security? Things like

  - Query timeouts to prevent noisy neighbors
  - connection pooling (e.g. pgbouncer) also for noisy neighbors
  - client schema compatibility (e.g. some applications running older versions, have certain assumptions about the schema that may change over time)

it's Friday, and I'm avoiding doing work

I notice that the python versions and typescript versions are pretty different. Python is sort of class based, with python magic decorators

    class MyWorkflow(Workflow):
        @step
        async def start(self, ctx: Context, ev: StartEvent) -> MyEvent:
            num_runs = await ctx.get("num_runs", default=0)

whereas TS is sort of builder/function based

    import { createWorkflow } from "@llamaindex/workflow-core";
    
    const convertEvent = workflowEvent();
    
    const workflow = createWorkflow();
    
    workflow.handle([startEvent], (start) => {
      return convertEvent.with(Number.parseInt(start.data, 10));

Is there reason for this? });

Agree, thanks for the link. I was wondering what actually changed. The resource links and elicitation look like useful functionality.

I've been curious to see the process for selecting relevant context from a long conversation. has anyone reverse engineered what that looks like? how is the conversion history pruned, and how is the latest state of a file represented?