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19 contributions to AI Money Lab
Stop scrolling through AI news and start building.
Stop just scrolling through AI news and start building. If you want to learn how to deploy AI agents, automate your business, and actually monetise this technology, the AI Profit Boardroom is the best community I’ve found. Join here: https://www.skool.com/ai-profit-lab-7462/about?ref=15da19f1de654588828c305a603dc74e
2 likes • 1h
@Julian Goldie The AI Profit Boardroom is an absolute goldmine of strategies you can apply immediately!
1 like • 59m
@Julian Goldie Experiment, fail, iterate, learn, build and repeat.
Accomplish AI with Hunter Alpha🔥
I am using Accomplish AI with Hunter Alpha, a 1 trillion parameter, 1 million token context frontier intelligence model built for agentic use with a free API via OpenRouter, and it feels like cheating. If you are not using these tools, you are competing with one hand tied behind your back. https://www.skool.com/ai-profit-lab-7462/about?ref=15da19f1de654588828c305a603dc74e
2 likes • 2d
Alpha hunter has ben removed now but it was great while it lasted!
2 likes • 1d
@Julian Goldie competitor analysis, SEO audits, new website design using the live browser agent to search top competitor websites and more….!
3 Takeaways — Manus Desktop Agent Launch
1️⃣ Interface Navigation: OS-level agents like Manus can automate software that doesn't have an API, effectively "piloting" the user interface just like a human. 2️⃣ Native Workflow Integration: By interacting directly with local files and applications, agents can eliminate the manual "handoffs" between software. 3️⃣ Accessibility Leap: This technology brings complex automation to non-technical users, allowing anyone to "train" an agent by simply showing it how to do a task on their screen. Join here: https://www.skool.com/ai-profit-lab-7462/about?ref=15da19f1de654588828c305a603dc74e Got to love these YouTube thumbnails 👇
NVIDIA NemoClaw — 3 Takeaways
1️⃣ Governance over Intelligence: NVIDIA is pivoting to provide the "Rules of Engagement" for AI, recognising that security is the only thing standing between agents and mass enterprise adoption. 2️⃣ OpenClaw Integration: NemoClaw is built specifically to wrap around the OpenClaw open-source framework, cementing it as the industry standard for autonomous agents. 3️⃣ Action-Level Filtering: The technology moves security from "Text Filtering" (checking what the AI says) to "Action Filtering" (checking what the AI actually does in your software). Join here: https://www.skool.com/ai-profit-lab-7462/about?ref=15da19f1de654588828c305a603dc74e
NVIDIA OpenShell Toolkit 3 Takeaways
1️⃣ Self-Evolution: Agents built on OpenShell can autonomously refine their own "claws" and code based on real-world execution failures. 2️⃣ Standardized Safety: The Secure-by-Design AI Blueprint embeds protection directly into the agent architecture, making it safe for high-stakes enterprise data. 3️⃣ Industry Alignment: With Adobe, Atlassian, and ServiceNow as day-one partners, OpenShell is set to become the standard "Operating System" for B2B agent swarms. If you want to learn how to deploy AI agents, automate your business, and actually monetise this technology, the AI Profit Boardroom is the best community I’ve found. Join here: https://www.skool.com/ai-profit-lab-7462/about?ref=15da19f1de654588828c305a603dc74e
NVIDIA OpenShell Toolkit 3 Takeaways
1 like • 3d
@Julian Goldie You are spot on about the human in the loop requirement. Letting an evolving agent run totally unsupervised is how you end up with policy drift, hallucinations, and a massive corporate liability. The goal isn't to replace human judgment; it's to replace the tedious grunt work leading up to that judgment. If I had to deploy one real workflow today, I’d tackle continuous compliance auditing. Because it is a secure by design framework (like OpenShell), I’d deploy an agent that lives entirely inside their secure database. It sits in the background 24/7, cross-checking every transaction against compliance rules. The data never leaves their servers. The AI does all the tedious heavy lifting and just flags the weird edge cases for a human to review and approve. Instead of a painful 3-month manual audit, they stay 100% audit-ready year-round, dodge fines, and keep humans in total control of the final call. For the weird edge cases, the human in the loop flagged item, corrects it, and makes the final call. The IA does not need a dev to hard code the update, the AI simply learns and uplates its self from the updates. The next time a similar weird contract pops up, the agent handles it perfectly on its own. The system gets sharper and the human workload shrinks every single month, simply by having the human do their normal job
1-10 of 19
Raman Sharma
3
15points to level up
@raman-sharma-6561
Learning to vibe code

Active 21m ago
Joined Mar 8, 2026
United Kingdom
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