If you use Claude daily — you know this pain. You're deep in a conversation. Claude knows your project inside out. Architecture. Requirements. Every decision. Then — ⚠️ Usage limit reached. And now you have two terrible options: → Wait for the limit to reset and lose all momentum → Switch to ChatGPT and re-explain everything from scratch I got tired of it. So I built ContextShift — a Chrome extension that carries your full conversation context to any LLM the moment you hit the limit. Here's what it does: → Auto detects Claude, ChatGPT and Gemini → Captures your full chat session in one click → Saves it to your personal context library → Transfers it to any LLM instantly → Never lose your context again I built this while learning Claude Code. The tool that caused the frustration — helped me build the solution. Watch the full demo here 👇 https://youtu.be/2vKo1q6THNo?si=HPsq4Pv5Tgflgk-k Not on the Chrome Web Store yet. DM me if you want early access. 🚀

Most consulting/business proposals still take HOURS (sometimes days) of manual work… So I built an AI workflow that can generate enterprise-style proposals in minutes using multi-agent automation. ⚡ What it does: • Researches companies automatically • Analyzes industry pain points • Generates multiple solution approaches • Creates consulting-grade proposals • Supports Human-in-the-Loop editing• Exports polished HTML/PDF proposals 🛠️ Stack: n8n + Gemini + Claude + Lovable + Jina AI The coolest part? The workflow uses multiple AI agents working together for: → research→ industry analysis→ solution generation→ proposal drafting→ AI editing This project pushed me deep into:Agentic AI • Workflow Automation • Prompt Engineering • Multi-Agent Systems Dropping the demo below 👇 Curious:What other business workflows do you think AI agents will completely automate next?

Expressions → Triggers → Error Handling → AI Agents → Shortcuts. All in one deck. Drop a comment if you want the full breakdown on any of these.

Most people build n8n workflows. Very few know how to productnize them for real clients. The difference shows up the moment workflows hit production 👇 • Workflows become too large to debug • AI agents start failing randomly • API rate limits kick in • One failed node breaks the entire flow • Loops destroy performance • Clients expect reliability, not “it worked in testing” 😅 A few things that changed everything for me while scaling n8n systems: → Breaking large automations into sub-workflows → Using queues + async processing instead of long-running executions → Treating n8n as an orchestration layer, not the database → Separating Main / Webhook / Worker processes for throughput → Adding proper error handling + logging everywhere → Using AI only inside bounded tasks (classification, extraction, summaries) One underrated lesson:AI can help scaffold workflows, but deterministic automation still wins in production. Curious — what’s the biggest scaling or reliability issue you’ve faced with n8n so far? I’ll share how I usually solve it.

Here's what's actually draining your limit: ✗ Uploading raw PDFs (1 page = 3,000 tokens) ✗ Sending separate messages instead of batching tasks in one shot ✗ Leaving search & connectors on by default ✗ Using Opus for a grammar check ✗ Re-uploading the same files every chat None of this is Claude's fault. It's fixable in 10 minutes. The 5 habits that changed it → swipe through. Share this before someone you know wastes $80/month.