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For the past few months, I've been building a product "Linden". Its a low latency deterministic layer for ai agents. Linden helps businesses verify AI-generated outputs before they lead to real-world actions.
For my personal retrieval-augmented generation (RAG) study, I needed a local model to answer questions from documents. My laptop has an RTX 3060 Laptop GPU with 6 GiB of VRAM, so memory use mattered alongside speed. I compared qwen3:4b and qwen3:0.
This is from MV2's open notebook on Kakeya-type problems ( https://github. com/munimtechnologies/kakeya-small-fields ). It continues the earlier posts on finite 2-groups whose involutions are all central: is such a group at most |Omega_1|^(e+1), where 2^e is its exponent?
2 points, 0 comments on Hacker News
The page says chunking has degraded retrieval quality in the internal knowledge-base bot. What that actually means is explained by the trace: on call sees a plausible response, a successful vector query, and citations that point to the right handbook but the wrong passage. Nothing is down.