Level up your n8n AI agents effortlessly! In this video, discover how to utilize OpenAI's Responses API as a powerful chat model to enhance your agents. I show you the quicker, easier method to bake in essential tools like web search and file search directly into your agents, bypassing the need for connecting other tools, messing with prompts, and building data pipelines for you knowledge base. Learn exactly why this new approach is a significant upgrade and how to set it up in n8n without writing any code, unlocking a new level of power and capability for your AI agents.

In this video I'll be giving you guys a full guide on how to price AI workflows in 2026. Pricing your AI workflows is one of the most important parts of your business, because if you price too high, you miss out on a lot of money...and if you price too low, you miss out on a lot of money. So here's my full guide to help make sure that doesn't happen to you! Hope you enjoy!

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The Real Reason You’re Working Too Much for Too Little Understanding how much it costs you to acquire a client and how much profit that client generates can mean the difference between thriving and burning out. If you’re an agency owner or freelancer offering AI and automation services, you’re likely obsessed with optimizing client workflows, deploying cutting-edge tools, and automating for scale. But when was the last time you optimized your own profit model? That’s where unit economics comes in. What Are Unit Economics? Unit economics refers to the direct revenue and costs associated with delivering one unit of your product or service. In your case, the “unit” is often a client or project. At the core, two metrics matter most: • Customer Acquisition Cost (CAC): How much you spend to acquire a client. • Customer Lifetime Value (LTV): How much profit a client generates for your business over the life of the relationship, not revenue (as often misused), but actual take-home profit after all delivery costs. If your LTV isn’t significantly higher than your CAC, you’re not building a business; you’re building a treadmill. Why It Matters in the AI & Automation Space Most AI/automation service providers face similar challenges: • Custom scopes and deliverables • Heavy pre-sales consulting • Long lead nurturing cycles • High variability in project complexity • Temptation to overdeliver These dynamics make it easy to burn through time and money, without realizing how little is left on the bottom line. Unit economics gives you the financial clarity to see exactly where you’re winning, and where you’re bleeding out. Key Benefits for Your Agency or Freelance Business 1. Confident Pricing When you know how much it actually costs to deliver a service, you can price your retainers or projects accordingly, without guesswork or emotional discounting. Example: If an N8N automation takes 10 hours and your total delivery cost (time + tools + support) is $1,800, and your CAC is $300, then charging $2,800 gives you a $700 profit.

Worth reading and keeping an eye on.. Introducing Nested Learning: A new ML paradigm for continual learning We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. The last decade has seen incredible progress in machine learning (ML), primarily driven by powerful neural network architectures and the algorithms used to train them. However, despite the success of large language models (LLMs), a few fundamental challenges persist, especially around continual learning, the ability for a model to actively acquire new knowledge and skills over time without forgetting old ones. When it comes to continual learning and self-improvement, the human brain is the gold standard. It adapts through neuroplasticity — the remarkable capacity to change its structure in response to new experiences, memories, and learning. Without this ability, a person is limited to immediate context (like anterograde amnesia). We see a similar limitation in current LLMs: their knowledge is confined to either the immediate context of their input window or the static information that they learn during pre-training. The simple approach, continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.