Clawdbot (moltbot) showed what we all want A personal AI assistant with persistent memory And access to all the tools and our machines So that we can just chill and scroll X
@godofprompt
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New video: complete MCP and AI Agents setup guide
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MCP & AI Agents 101: full setup guide New YT video dropped
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Nori organizes family schedule, meals, and tasks seamlessly
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In my house, I remembered details.
— God of Prompt (@godofprompt) 28 janvier 2026
My partner remembered “meeting at 3.”
Now Nori talks to my whole family in plain English. Week organized. Calendars synced. Tasks assigned. Nothing forgotten.
Friday 5pm used to mean: “What’s for dinner?”
Now Nori says: “Week planned. Meals… pic.twitter.com/rJoluDuIfRIn my house, I remembered details. My partner remembered “meeting at 3.” Now Nori talks to my whole family in plain English. Week organized. Calendars synced. Tasks assigned. Nothing forgotten. Friday 5pm used to mean: “What’s for dinner?” Now Nori says: “Week planned. Meals
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Bad vs Good: Specifying Role and Seniority in Prompts
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1. SPECIFIC ROLE + SENIORITY Bad: "Act as a developer" Good: "Act as a Senior Backend Engineer with 8 years specializing in distributed systems" Bad: "Act as a writer" Good: "Act as a Technical Content Writer who translates complex SaaS features for non-technical buyers"
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Specific roles matter: avoid vague expert prompts for LLMs
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Here's what most people do: "Act as an expert marketing strategist and help me with my campaign." The LLM has no idea what kind of expert. B2B or B2C?
Digital or traditional?
Startup or enterprise?
Data-driven or creative-first? Garbage in → garbage out. -
Specific personas boost LLM output quality to 94%
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Telling an LLM to "act as an expert" is lazy and doesn't work. I tested 47 persona configurations across Claude, GPT-4, and Gemini. Generic personas = 60% quality
Specific personas = 94% quality Here's how to actually get expert-level outputs: -
LobeHub: multi-model, direct API, agent marketplace, 70k GitHub stars
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Claude Cowork: $200/month. One model. macOS only.
Manus: VM-based browsing. Slow. Unstable. Expensive. LobeHub:
→ Multi-model support (switch freely)
→ Direct API access (faster, more accurate)
→ Agent marketplace to discover and remix
→ 70k GitHub stars foundation More -
Remixing LobeHub agent to extract arXiv paper authors
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Stop searching and pasting prompts from all over the internet.
— God of Prompt (@godofprompt) 27 janvier 2026
LobeHub has an agent marketplace.
I found a People Search agent in the community, then leveled it up by remixing the prompt and swapping tools.
Now it:
→ Extracts all authors from an arXiv paper
→ Finds their… pic.twitter.com/XS5PeNVzyYStop searching and pasting prompts from all over the internet.
LobeHub has an agent marketplace. I found a People Search agent in the community, then leveled it up by remixing the prompt and swapping tools. Now it: → Extracts all authors from an arXiv paper
→ Finds their -
Build a Twitter growth pipeline with multi-agent system in 10 minutes
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Built a Twitter growth pipeline in 10 minutes:
→ Agent 1: monitors trends
→ Agent 2: researches sources
→ Agent 3: drafts threads
→ Agent 4: prepares posts Supervisor orchestrates. Agents work in parallel. Human approves before publish.
One instruction. Full content -
L3 and L4 Agent Levels: Hand-holding vs Supervisor Orchestration
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Nobody's explaining this:
— God of Prompt (@godofprompt) 27 janvier 2026
L3 (Manus, Cowork): Agent asks for guidance constantly. Heavy hand-holding. User maintains active role throughout.
L4 (LobeHub): Supervisor handles orchestration. Agents work in parallel. Human only approves output.
The Knight Institute literally… pic.twitter.com/DIC8sAuWnNNobody's explaining this: L3 (Manus, Cowork): Agent asks for guidance constantly. Heavy hand-holding. User maintains active role throughout. L4 (LobeHub): Supervisor handles orchestration. Agents work in parallel. Human only approves output. The Knight Institute literally