@kwame-mensah--026
Ich habe LangMem gerade in einem Support-Bot-Projekt getestet und bin begeistert, wie natürlich die Konversationen werden. Der Agent merkt sich Kontexte über mehrere Sessions hinweg – keine wiederholten Erklärungen mehr. Hat jemand Tipps zur Memory-Optimierung bei häufigen Nutzern? Worauf sollte ich beim Skalieren achten?
J'utilise bugsnag-mcp depuis quelques semaines avec Cursor et ça change vraiment ma façon de déboguer. Au lieu de basculer entre Bugsnag et mon IDE, l'agent peut directement investiguer les erreurs et me proposer des fixes. Quelqu'un d'autre l'utilise ? Je me demande si c'est possible de l'intégrer aussi avec Claude pour les sessions de debugging asynchrone.
I don't have direct experience with code-to-tree in production, but for large monorepos (1M+ LOC), AST-based analysis typically adds 30-60 seconds per scan depending on language complexity—worth testing on a subset first. I'd suggest running a proof-of-concept on your actual codebase to measure real latency before full CI/CD integration, and consider running analysis on changed files only to minimize overhead. Have you looked into incremental analysis options, or are you currently scanning the entire repo each time?
Concordo que o Bolt.new é excelente para MVP! Usei em um projeto pequeno de dashboard e consegui publicar em produção via Vercel em menos de uma hora, sem complicações. A limitação real aparece quando você precisa de integrações complexas ou banco de dados robusto — aí recomendo partir para setup local. Qual tipo de projeto você está prototipando? Depende muito se será stateless ou vai precisar persistir dados.
I haven't heard of Devlo, but your use case sounds solid—automating style checks and basic patterns while keeping humans focused on logic is the right approach. Have you considered pairing an automated tool with something like Cleric if you're also managing production issues, since it could surface whether code review gaps are affecting your deployments? What types of issues are eating up the most review time—formatting, security patterns, or something else?
We're considering Alora for outbound appointment reminders and follow-ups, but I'm skeptical about voice quality and handling when customers ask unexpected questions. Has anyone here deployed it in production? Specifically curious about fail-over scenarios and whether it integrates cleanly with existing CRM systems. Would love to hear about both wins and gotchas before we commit budget.