Right now, AI enthusiasts can’t stop talking about Clawdbot - a "digital agent" that supposedly manages your life 24/7, controlled with natural language. Sounds amazing. But here’s the reality: THE CLAWDBOT HYPE 🤖 Most "brand-new" launches are basically public beta tests. And "beta" is just a nicer word for: ✅ exciting ❌ unstable ❌ unclear limits ❌ unpredictable outcomes If something breaks, leaks, or messes up your setup… you deal with the consequences. WHEN YOU SHOULD TEST ON DAY 1 ✅ If you’re: - a creator - a news channel - a true power user (time + technical depth + patience) Then yes - testing early can make sense. Everyone else? You’re spending time for very little upside. WHY WAITING WINS 🧠 Give it a few weeks and you'll get: - clearer info on what it actually does - real use cases (not just demos) - fewer bugs and broken flows - honest reviews For most people, I see zero advantage in installing Clawdbot today (or this week).

People are reporting Anthropic warnings/bans when using Claude OAuth (Pro/Max) through third-party tools like Clawdbot (instead of API access). I'm dropping the few links below - anyone here hit by this, and what workaround are you using (API key / different model / different tool)? https://x.com/elijahsystems/status/2016201425273958453

I'll go first: Claude Code. Why (my POV): - It feels like the most reliable coding partner when you use it correctly: clear task framing, tight scopes, and constraints. - I’m treating my dev work like a product: versioned releases on Git, plus a personal learning.md for decisions + "memory context". - I started this product as vibe coding, but now it’s turning into a production product - and Claude Code helps me keep structure while still shipping fast. Tradeoff / reality check: Even with good hygiene, I’m seeing 50K context usage out of a 200K window just for it to scan and understand files sometimes. Worth it for speed, but the context budget is real. My take: Claude Code wins when you don’t treat it like magic—you treat it like an engineer: - give it a mini-PRD, - curate context (learning.md, changelog, release notes), - force small, testable steps. Now your turn 👇Pick ONE tool and defend it. If nothing comes to your mind just pick from these: what's your bottleneck - context budget, accuracy, refactors, tests, or review quality?

https://www.youtube.com/watch?v=LqGWk25F7uw

1. NEVER one shot anything. Ask AI to make incremental changes. 2. Commit often and Use branches and work trees generously. 3. Start with the backend first. Use Supabase always. 4. Create a backend script to create generic tables and auth setup via sql for new projects so you save time. 5. When starting a new project, use premium models that can set up everything right. I use up to 50% of freemium models for the month before I start rationing. 6. Use free models for minor edits only. 7. Use console logs to as a way to debug. Share it with the AI for faster results. 8. Use Claude OPUS for large audits and ask it to create a detailed .md file with its suggestions. Then make updates to the code fixing one suggestion at a time. Do not ask the AI to fix everything at the same time. That is how you break the code. These are hard won lessons, I learnt building Evallo.app, Anntho.com, Quebeam.com and pulsehud.com. I’m not trying to show off, but I’m trying to showcase what is possible if you put your head down and focus. I am a product manager, turned founder, and if I can do it, you can do it too. What else would you add to this list?