An AI LLM neural browser workflow that follows the evidence
Move from a bounded question to inspected excerpts, supported claims, and a draft that makes its uncertainties visible.
· 7 min read↗
Neural Browser Lab / tag / 2 field notes
Vector search.
Treat similarity as a discovery signal, not a truth score. Explore passage boundaries, source metadata, retrieval evaluation, and the handoff from a candidate result to reviewed evidence.
Read across the boundaries.
The vector search thread connects 2 field notes that approach a shared question from different angles. Start with the article closest to the task you are planning, then compare its assumptions with another implementation context. The articles are not interchangeable instructions: a browser extension, a research interface, and a spatial workspace each need their own access and review decisions.
Use this collection as a small reading route rather than a checklist of features to add. Write down the input, the person making the decision, the evidence available, and the behavior that remains unresolved. As you follow the related topic guides, keep those four questions consistent. The result should be a clearer project conversation, including an explicit account of what the sources and the proposed design do not establish.
Design a vector neural browser for useful semantic search
Design passage-level retrieval with meaningful metadata, honest relevance signals, and a baseline you can evaluate.
· 7 min read↗