For this project, where you have 120GB of customer data, and thirty requests a second for ~8k objects (0.25MB/s object reads), you’d seem to be able to 100x the throughput vertically scaling on one machine with a file system and an SSD and never thinking about object storage. Would love to see why the complexity
> Instead of taking a stab in the dark, Leanstral rolled up its sleeves. It successfully built test code to recreate the failing environment and diagnosed the underlying issue with definitional equality. The model correctly identified that because def creates a rigid definition requiring explicit unfolding, it was actively blocking the rw tactic from seeing the underlying structure it needed to match.
I'm using Sonnet with 1M Context Window at work, just stuffing everything in a window (it works fine for now), and I'm hoping to investigate Recursive Language Models with DSPy when I'm using local models with Ollama
Apache Arrow is trying to do something similar, using Flatbuffer to serialize with zero-copy and zero-parse semantics, and an index structure built on top of that.
My threshold for “does not need to be smaller” is “can this run on a Raspberry Pi”. This is a helpful benchmark for maximum likely useful optimization.
Curious about comparisons with Apache Arrow, which uses flatbuffers to avoid memory copying during deserialization, which is well supported by the Pandas ecosystem, and which allows users to serialize arrays as lists of numbers that have hardware support from a GPU (int8-64, float)