🦞 Auditable AI Greenhouse
Good Morning, OpenClaw Owners!
The next wave is not just asking AI better questions. It is giving AI better jobs.
Auditable AI Greenhouse
TL;DR: Verdify uses OpenClaw agent Iris to help run a real 367 sq ft greenhouse in Longmont, Colorado. Sensors feed climate, forecast, equipment, and resource data to Iris, which proposes safe climate tactics; a dispatcher checks them, and ESP32 firmware controls relays. The significance is a real physical agent loop with safety boundaries and public audit data. Read more →
HTML Returns?
TL;DR: OpenClaw style agents now prefer HTML over Markdown for complex agent outputs. As specs, plans, PR reviews, and research reports grow harder to read in plain Markdown, HTML offers richer layout, visuals, and interaction fortemporary human-review interfaces rather than simple editable text files, sparking viral debate over whether agents need visualization enough to justify the added token cost. Read more →
Agent Costs Must Fall
TL;DR: Marc Andreessen and Elon Musk argued that powerful OpenClaw-style agent experiences are already possible, but model costs still need to fall much further. Andreessen said frontier-model workflows can cost hundreds or thousands per day, while Musk said one friend still spends about $200 daily even with local models handling easier tasks. Read more →
Reverse Prompting Skills
TL;DR: Alex Finn argued that the best AI skill is “reverse prompting”— giving an agent your career context, goals, and ambitions, then having it ask what else it needs to know and what tasks it can do for you right now. The idea reframes agents from passive responders into proactive operators that uncover leverage and help move work forward. Read more →
LinkedIn Agent Playbook
TL;DR: Trigify founder Max Mitcham shared how he grew LinkedIn to 30,000+ followers by replacing the old manual content grind with an OpenClaw agent system. Instead of hand-picking topics, writing from scratch, and reviewing results alone, agents scan signals, learn from performance, draft posts, surface angles, and repurpose winners with human approval. Read more →
Agentic AI Needs More
TL;DR: Nvidia CEO Jensen Huang said that agentic AI could demand 1,000% more compute than earlier generative AI because agents must reason, use tools, act across systems, and generate many more tokens in real time. He framed the shift as enterprise AI moving from answers to execution, reshaping infrastructure demand. Read more →
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