I’m trying to get this flow consistently fast (aiming for sub-10s per run). Current setup: - Webhook → Code (preprocess) - AI Agent (OpenAI Chat) with Pinecone Vector Store + OpenAI Embeddings - BuildPayload → ElevenLabs (TTS) → rename → Supabase Storage upload - In parallel: Supabase DB update + Analytics (Mixpanel) - Final Respond to Webhook returning audio URL Questions for the pros: - Best way to parallelize TTS/upload/analytics—Queue Mode + workers or sub-workflows? - Any wins from HTTP keep-alive, batching, or reducing hops (replace multiple Set/If with one Function)? - Tips for caching/pre-warming (tokens, Pinecone lookups) and passing binary data to avoid base64 bloat? - Anyone using streaming (LLM partials → start TTS early) to overlap steps? - Execution-data/DB settings you tweak to lower overhead? Short node patterns or screenshots would be awesome—thanks! 🙏

I’m building live transcription for a mobile AI coach and aiming for sub-200 ms latency. If you’ve shipped this, what worked best for you? - Capture → Stream: AVAudioEngine (iOS) / Oboe (Android) + VAD? - Transport: WebRTC vs WebSocket; Opus vs raw PCM; ideal chunk size for partials? - Latency control: jitter buffers, endpointing, punctuation without lag. - Accuracy extras: word-timestamps, diarization, noise suppression/AGC/AEC. - Resilience: packet loss, reconnect, FEC; buffering on shaky networks. - Privacy & cost: on-device vs cloud redaction; pricing gotchas. Short code snippets, architectures, or repo links would be amazing—thanks!

Hey Guys! I’m a software engineer working in a large enterprise monorepo with a diverse stack: .NET (legacy and modern), multiple Vue.js front-end apps, a custom E2E testing framework, and a lot of ASP.NET code. Our team has historically been split between front-end and back-end devs. So far, we’ve been using Claude Code mainly on the front end, where we have some, commands, and sub-agents configured. We also have a few CLAUDE.md files scattered in places like the Database/ and API/ folders, but nothing consistent or centralized yet. We’re now starting to explore Claude for back-end development too, and we want to professionalize our setup across the entire monorepo. We're experimenting with spec-driven development and trying to get the most out of Claude’s agentic features—but we have some practical questions: Where should CLAUDE.md files live—monorepo root, per app, per package? How should .claude/ directories be structured when working across multiple apps and shared libraries? Should developers always open Claude from the root of the repo, or are there smarter ways to organize entry points? How are you making Claude-related files (rules, commands, specs) more discoverable for human developers? Any tips for managing full-stack feature work that spans front-end and back-end? How are you using Claude to modernize legacy code or shift toward more full-stack collaboration? We’re at the stage of turning a promising experiment into a solid, maintainable setup—and would love to hear how others are doing this at scale. Thanks in advance!

Quick newbie Q: how do you make n8n workflows faster? - Safest way to run steps in parallel without hitting rate limits? - Split In Batches vs sub-workflows — which is quicker in practice? - Any easy HTTP wins (keep-alive, batching, retries)? - Fewer nodes vs one Function node — real speedup? Would love beginner-friendly tips or tiny snippets.