Hey everyone 👋 Question for those of you building AI chatbots for clients (or your own product): When you deploy a chatbot on a website, are you actually GDPR-compliant — or are you hoping nobody notices? I’m based in Austria, building AI automation solutions for European SMBs, and the #1 question I get asked isn’t “how smart is your bot?” — it’s: “Where is the data going? Who processes it? Do you have an AVV?” Most US-built chatbots fail this conversation immediately because: • No signed Data Processing Agreement (Art. 28 GDPR requirement) • Subprocessors (OpenAI, Pinecone, Vercel) not disclosed • No Datenschutzerklärung (privacy notice) in the local language • EU AI Act transparency requirements ignored (mandatory from August 2026) Curious — how are you handling this for European clients? Or are you avoiding the European market entirely because it feels too complicated? Happy to share what I’ve built (a 10-point GDPR transparency package) if anyone wants to dig in 🙌

Most Claude Code users create an AGENTS.md or CLAUDE.md file. A thousand lines is not unusual. They fill it with everything they want Claude to remember: - tech stack - coding conventions - tone of voice - folder structure - review checklist - deployment process - project gotchas The problem? That file loads into the context window on every single turn. If it’s 7,000 tokens, you pay 7,000 tokens just to ask Claude what file to edit. You pay it again when you ask a follow-up. You pay it again when Claude replies. By turn twenty, you may have burned 100,000+ tokens on instructions that were only relevant for maybe three of those turns. Two things break: 1. CostTokens are not free. You’re paying for context you are not using. 2. QualityYou hit the context-fill danger zone sooner. The model has less room for the actual task, and performance starts to degrade. My hot take: Stop putting everything in one giant always-loaded instruction file. Use skills instead. I tested one skill file: - Full skill body: 944 tokens - Name + description only: 53 tokens That’s an 18x difference. And that difference compounds on every turn, every session, for every user on your team. The better pattern is simple: Keep the always-loaded context small. Load the detailed instructions only when the task actually needs them. Curious: how big is your AGENTS.md or CLAUDE.md file right now?