Show HN: NimbleBrain – We killed our agents; users now just describe workflows(studio.nimblebrain.ai)
1 pointby barefootsanders15 days ago1 comment
  • jackfranklyn15 days ago
    This resonates. Building in accounting automation and hit the exact same wall.

    Started with "configure your coding rules" - let users set up how transactions should be categorised. Made sense architecturally. Nobody used it. They'd stare at the config screen and give up.

    What actually worked: learn from their existing data. Pull their historical GL, reverse-engineer how they already code things, then just do it that way. No configuration, no "agent" concept. Upload bank statement, get coded transactions back.

    The insight that killed our complexity was realising bookkeepers don't think in rules. They think in outcomes. "This should be coded like the last time we saw a payment from Stripe" not "if merchant contains 'stripe' and amount > 100 then category = payment processing fees".

    Curious about your MCP orchestration layer - how do you handle cases where the described workflow is ambiguous? In accounting there's often multiple valid ways to code something.

    • barefootsanders15 days ago
      "Outcomes not rules" is the whole game. On ambiguity: we don't try to solve it upfront. Get it 80% right, let them refine the rest in seconds. When something's wrong, they fix it in natural language, not by going back to eng to change logic or mess with config. The unlock is really iteration without a ticket.

      Would love to swap notes on the accounting side. Similar problems, different domain constraints.

      • jackfranklyn11 days ago
        The 80% approach really resonates. We're building transaction coding tools for accountants and the key insight was similar - get the obvious stuff right automatically, surface the ambiguous cases for human decision.

        The domain-specific wrinkle for accounting: every firm has their own coding style. What one bookkeeper calls 'Office Supplies' another calls 'General Admin'. So we had to build pattern learning at the individual level - watch how each person codes, learn their preferences, then apply that to new transactions.

        The tax rules (VAT in the UK) add another layer - sometimes the same merchant gets different VAT treatment depending on what you bought. Gets messy fast.

        Would be happy to swap notes - feel free to reach out. Always interested in how other domains handle the 'close enough to be useful, flexible enough for edge cases' problem.