1 points, 0 comments on Hacker News
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.
Most smart products already have a name. The hard part is making the device hear that name reliably without sending every conversation to the cloud. This is the product question behind a custom wake word: can a device recognize one short phrase locally, then start the next interaction?
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.