I think there’s a misunderstanding of mantic.sh architecture...
The “weights” as you described in Mantic.sh are not neural network weights. They’re deterministic ranking heuristics, similar to what IDE file pickers use. For example, extension weights, path depth penalties, and filename matches. You can see them directly in brain-scorer.ts (EXTENSION_WEIGHTS).
const EXTENSION_WEIGHTS: Record<string, number> = {
'.ts': 20, '.tsx': 20, '.js': 15, '.jsx': 15,
'.rs': 20, '.go': 20, '.py': 15,
'.prisma': 15, '.graphql': 10,
'.css': 5, '.json': 5, '.md': 2
};
There’s no LLM involved in the actual search or scoring, it’s a static heuristic engine, not a learned model.
I'd love to have the implementation critiqued on its merits!
The “weights” as you described in Mantic.sh are not neural network weights. They’re deterministic ranking heuristics, similar to what IDE file pickers use. For example, extension weights, path depth penalties, and filename matches. You can see them directly in brain-scorer.ts (EXTENSION_WEIGHTS).
const EXTENSION_WEIGHTS: Record<string, number> = { '.ts': 20, '.tsx': 20, '.js': 15, '.jsx': 15, '.rs': 20, '.go': 20, '.py': 15, '.prisma': 15, '.graphql': 10, '.css': 5, '.json': 5, '.md': 2 };
There’s no LLM involved in the actual search or scoring, it’s a static heuristic engine, not a learned model.
I'd love to have the implementation critiqued on its merits!