scottcodie

They're definitely not the only one. I've been building on relational transformers, which does prediction and classification over relational data (it handles numeric types better). It's validating to see that these small models that do prediction tasks are so useful to the community, but also stings a little that it was so hard for me to communicate how game changing they are.

You can also mine attention from image models, it's a lot of fun and very interesting.

I did took a native c++ approach when writing a relational transformers engine (RelativeDB). My journey was pytorch -> c++ -> Triton (lang). While C++ was more performant than Triton, I couldn't afford to optimize on every gpu. I just accepted the ~15% throughput loss for my cloud service, which honestly wasn't bad for the amount of flexibility I got out of it.

But the cpp port of vllm looks great, that'd be great if you'll maintain that. I hit the same limitations with vllm.

The whole graph is walked at inference time to create the context. There is an attention feature, called 'neighbor attention', that propagate signals along primary-foreign key relationships. The task table includes the foreign keys themselves.

If this was a claude plugin with a hook on my dep files, I'd be in.

I'm not sure a game needs transactions. It seems to me that entity streaming concepts are a better fit: reading from an event queue and consistently reconciling the state over time. Like the sorted top-n in the example, if it was a game and the query is generally known, it would be better to just materialize it as a fast rank, which goes beyond the IVM-style materialization they suggested.

Have you taken a look at the various test harnesses for video encoding/decoding, or consider publishing a standalone one yourself? I get a bit-for-bit rewrite can have some advantage but it's hard to know if the AI implementation actually covers all of the edges cases.

I have a legit laminar flow hood (airclean 600, 32 inch) in my home for plant tissue culture. I gotta say, I thought it was going to be easy street to sterile culture- it was not. Sterile protocol matters so much that I think it would have been just as easy to do what I needed to do in a glove box with all the extra precautions I had to take. It's honestly hard to beat an enclosed container with zero airflow.

One thing the other blog post missed and this post misses too is that you don't need Kafka to use Debezium with Postgres. This gives you a pretty seamless onramp to event streaming tools as you scale.

I've spent my entire career developing databases (oracle, cassandra, my own database startup). Knowing if your workload is read or write heavy is one of the first questions when evaluating database choice, and is critical for tuning options. I would give this article hate just because it feels partially written by AI and the title needs a possessive 'your' in it, but its core ideas are sound and frame the issue correctly.

Stream processing/materialization engine written in rust that can be compiled to wasm.

Graffiti art.