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What entails the LLM Completion are you talking sequence of prompts with files / mcp servers. Could you share a bit more, cause I have spent some time with this and have something that might be precisely what you are asking for...

This is awesome. Love seeing more teams investing early in observability and evals instead of treating them as an afterthought.

Your setup (LLM-assessed complexity, semantic success metrics, tool-level telemetry) hits what a lot of orgs miss, tying evaluation and observability together. Most teams stop at traces and latency, but without semantic evals, you can’t really explain or improve behavior.

We’ve seen the same pattern across production agent systems: once you layer in LLM-as-judge evals, distributed tracing, and data quality signals, debugging turns from “black box” to “explainable system.” That’s when scaling becomes viable.

Would love to hear how you’re handling drift or regression detection across those metrics. With CoAgent, we’ve been exploring automated L2–L4 eval loops (semantic, behavioral, business-value levels) and it’s been eye-opening.

Fluvio is streaming transport. And we built Stateful DataFlow on top of that for Stream Processing.

Arroyo is SQL first stream processing. Fluvio is streaming transport which can send data to Arroyo and there is an integration.

Stateful DataFlow and Arroyo are similar in the stream processing pattern and the use of Apache Arrow.

The interfaces are different. Fluvio and Stateful DataFlow support for SQL is the same dialect as columnar SQL supported by Polars. The Fluvio and Stateful DataFlow paradigm is more intricate more expressive and the platform is broader and deeper.

We have had folks over the years asking us about the Kafka wire compatibility. We had a project 3 years ago which we archived. I think we have a case for reviving it in the near future.

This is a question we asked 6 years ago.

What if we wrote it in Rust. And leveraged and WASM.

We have been at it for the past 6 years. https://github.com/infinyon/fluvio

For the past 2 years we have also been building Flink using Rust and WASM. https://github.com/infinyon/stateful-dataflow-examples/

The search on HN is weird.

Isn't the Show HN: a different filter?

Agree with you 100%. We are working on more elaborate benchmarking on bare metal instances. This was just an initial run to utilize the benchmarking tool which is usable by all fluvio users.

We will do a full setup and benchmarks comparing Kafka, Pulsar, RedPanda using a real dataset on barmetal servers soon.

Really cool project! Look forward to trying this out. I have been using the copilot extensions with local docs toRAG augmentation. This seems to be a step up.

That's a really good suggestion. I am trying to build some tutorial videos now. I feel the same way about listicles but they get a lot of impressions. People seem to love lists.

Have been to a few pods. a few meetups.

Conferences has not been that great. Tried a couple.

The course option is very interesting. Have not tried something there.

Does it make sense to be part of some existing course? Or Should I create one?

Isn't it interesting that plays like the thing industries cant offer beyond QoS 0? Why do you say the gap is?

LOL. AI generated the image. But as far as I understand AJ wrote the rest. But it's a dry subject. I am curious what makes you say it is an AI generated Ad?

That sounds like an interesting approach.

I am curious about the details of how you do it.

No you don't. The open source project is here. You can self host and try it easy - https://www.fluvio.io/docs/fluvio/quickstart

Written by Wade Chambers, Chief Engineering Officer @ Amplitude