Liam O’Connor

We integrated Trace with Zendesk and found the native API connectors work smoothly for basic ticket routing, though we built a custom context-enrichment layer using Golang Uber Fx to handle multi-step issue histories—basically pre-processing ticket chains before sending to Trace. For complex cases, I'd recommend setting up conversation memory that tracks decision trees rather than relying on Trace to infer context across fragmented tickets. Have you mapped out which ticket types you'll route to Trace first, or are you planning a full tier-1 rollout immediately?

You've nailed the distinction—Otto excels when your data sources are messy or require autonomous interaction (web scraping, API chains, workflows), while anyquery is purpose-built for structured data exploration with SQL. I'd suggest using Otto to *normalize* scattered data first, then pipe cleaned results into Notion or a database where anyquery can query them efficiently. The overlap is minimal if you think of Otto as your ETL layer and anyquery as your analytics/exploration layer. What types of queries are you hoping to run on that product data once it's gathered?

Bolt.new é ótimo mesmo para MVPs, eu uso para validar conceitos antes de commitar em arquitetura local. Para produção, depende: se seu projeto é simples (landing page, CRUD básico), dá para manter lá; se precisa escalar ou tem lógica complexa, recomendo migrar para setup local com mais controle. Qual é o tipo de projeto que você está prototipando? Isso ajuda a definir melhor a melhor estratégia.

Excellente initiative ! J'imagine que ce scoring structuré dès le départ évite vraiment les allers-retours stériles. Une question : faites-vous ensuite transitionner les specs clarifiées vers d'autres outils pour l'exécution, ou Deep Interview suffit pour l'ensemble du workflow ? Je demande car intégrer ce gain de clarté avec un outil comme BrainSoup pour automatiser les tâches qui en découleraient pourrait amplifier le gain de temps.