Builder Nate Herkelman just shared a test of the Jev AI model, which is built for structured outputs like yes/no, categories and scores. He reports processing 1,000 emails against seven decision rules in 70 seconds for just $0.09 For comparison he notes the same task took about five minutes using Luna. My take is that models like this aren't for the main conversational loop. Their value is as a fast, cheap pre-processing or routing layer. For a law firm intake agent, the first step is always classification: is this an existing client? Is it a new lead? What practice area does it fall into? You could burn a lot of money and latency having GPT-4o figure that out. Or, you could have a model like Jev do that initial classification instantly and for a fraction of a cent per query. Then you pass the categorized request to the expensive, heavyweight model. This splits the logic cleanly and optimizes cost Herkelman also described a six-step process for "Codex skills," which he calls "a recipe for your agent" to capture processes and examples This sounds like a solid framework for building more reliable, repeatable system prompts and tool definitions instead of just tweaking them endlessly. The big unknown is the complexity of the task Jev actually performed. The post doesn't detail the seven decision rules from the email test, so we can't tell if it was simple keyword spotting or something requiring more nuance Until we see the actual rules or a similar example, it's hard to know where the line is for using a specialized model like this in a client build.

What is Entitlement in n8n, and why does it matter? In n8n, an entitlement is a permission that gives an n8n instance access to a plan-restricted feature for a specific period of time. A floating entitlement can be reassigned between different n8n instances. ————— [Example] A company runs several n8n instances for different teams. Its plan includes access to a premium feature, but that feature requires an entitlement to be assigned to an n8n instance. The company has 3 floating entitlements. Think of each entitlement as a permission pass that allows an n8n instance to use the premium feature included in the company's plan. The company assigns the 3 entitlements to its most important n8n instances. Those instances can now use the premium feature because they have the required permission. Later, one instance no longer needs the premium feature, while another instance does. The company reassigns the floating entitlement from the old instance to the new one. The company doesn't need to add another entitlement. It can simply reassign an existing floating entitlement from one n8n instance to another. This flexibility is what makes it a floating entitlement.

Help me test my website 👀 (30 seconds, no signup!) Someone in this community gave me a great idea: instead of just guessing which version is better, actually test how quickly people understand the offer and what to do next. I have two versions of my website. I didn't code either one myself. I had both made with AI tools, and I'll reveal which is which later 😂 Version A: https://bhartiaiautomationcom.netlify.app/ Version B: https://bhartiaiautomation.netlify.app/ Take about 30 seconds on each, and please don't scroll back or re-read. Then comment: 1. In one sentence, what do you think I do? 2. Who do you think this is for? 3. What problem do you think I solve? 4. Without scrolling, what would you click next if you were interested? 5. How clear was each version from 1 to 10, and about how many seconds did it take you to understand it? 6. Where did you feel confused or hesitant on A and on B? (Write anything, no wrong answers.) Please answer for A and for B separately. Honest answers help me the most, even if they're not flattering! I'm curious to see whether the real answers match everyone's guesses 👀 I'll share the results once enough of you have answered. Thank you so much, and thanks to the members whose comments made this test better 🙏

I’m currently building an AI Website Conversion System that analyzes websites, identifies conversion problems, and helps businesses improve their website to generate better results. I’m building the entire system from scratch as a standalone software product. The plan is simple: Build → Launch → Sell I’m looking for people who might be interested in acquiring the finished software once it’s ready. If you’re interested in potentially buying it or want to follow the build, DM me. 👀 More details soon.

Hey everyone! My name is Anas, and I just finished learning the core fundamentals of AI automations (Make.com, Voice Agents with Vapi, APIs, and PandaDoc). I’m super excited to transition from theory to hands-on building and start creating my first real automation workflows. If you have any beginner tips or cool workflow ideas for someone just starting out, I’d love to hear them. Let's build!