Hi HN,I’ve been exploring whether pandas can be used as a computation description, rather than a runtime.The idea is to write data logic in pandas / NumPy, then freeze that logic into a static compute graph and execute it in pure C++, without embedding Python.This is not about reimplementing pandas or speeding up Python. It’s about situations where pandas-style logic is useful, but Python...
In Mneme, updates are intentionally asymmetric: – Facts are append-only and explicitly curated (they’re meant to be boring and stable). – Task state is rewritten as work progresses. – Context is disposable and aggressively compacted or dropped.
The idea is that only a small subset of information deserves long-term durability; everything else should be easy to overwrite or forget.
This reduces the need for heavy retrieval logic in the first place, since the model is usually operating over a much smaller, more explicit working set.
Instead of retrieval or embeddings, it treats memory as an explicit, structured artifact and separates: – stable facts – task state – ephemeral context
The goal is to make memory boring, inspectable, and durable across sessions.
Happy to answer questions or hear why this is a bad idea
Sbox is intentionally "zero intelligence": - no code analysis - no allow/deny rules - no LLM involvement
It only provides strong isolation primitives, and assumes all code is hostile.
I’m using it as a building block for long-running agents and automation systems. Would love feedback from people who’ve built sandboxes or secure runtimes.