I'm currently using AI for my SEO consultancy: client check-ins, site audits and visibility reports for local businesses, including how visible they are in AI assistants like ChatGPT. Right now I'm at the building structured systems stage. I run my work through a local setup where each recurring job is its own small pipeline with its own files and instructions. One thing that caught my attention from this lesson is the idea that the folder structure itself is the agent's architecture: each stage only reads the context it needs, and you can follow what happened by looking at the files. This matters to my work because I handle several clients at once, and their data has to stay separate and traceable. When a report says something, I want to see exactly which input it came from. I'm excited to learn how to turn my SEO workflow into something my coachees can run themselves, step by step. I also want to see how structured approaches to AI can help me build more reliable systems with less guesswork. That includes a tool I'm building that sits between a user and an LLM and masks personal data before anything leaves the machine.

Serious question for everyone running Claude, ChatGPT, AI agents, or automated workflows: **What if I could help you reduce your token consumption by 30–70% without sacrificing output quality or productivity?** Not by simply using fewer features, but by analyzing and optimizing your actual system architecture, agent orchestration, context management, model routing, and workflows. I'm developing something called **THE GERMAN STANDARD — German Agentic Systems Assurance (GASA)**. A structured technical audit and optimization framework based on established engineering principles and aligned with relevant international ISO standards. The idea is simple: 1. Audit your existing AI setup. 2. Identify inefficiencies, bottlenecks, and unnecessary token consumption. 3. Establish a measurable performance baseline. 4. Optimize your system. 5. Run identical benchmark tasks before and after. 6. Document the actual savings and verify that quality hans't declined. The 30–70% range is a proposed optimization target, not a guaranteed result. Actual savings would depend on your setup and would need to be demonstrated. **Now here's my question:** If I could demonstrate a measurable 30–70% reduction in token consumption on your actual workloads, with comparable results, what would that be worth to you? Would you prefer a one-time audit fee, a complete optimization package, or a performance-based model where I receive a percentage of the money you actually save? I'm not asking what you'd pay for another AI course. **I'm asking what you'd pay for a measurable improvement to a system you're already spending money on.** Would love to hear actual numbers.

Congratulations to @Curtis Hays ! Curtis is one our OG's and he takes the lead this week! He already has Lifetime VIP, so this time he gets a 30 minute 1-on-1 with Jake. ⏰ The 7-day clock resets. Next Monday someone new takes the top spot. 🎯 How it works: - 📝 Post things worth reading - 💬 Help people in the comments - 🛠️ Share what you're building, what's working, what's breaking - ❤️ Engage with what others are putting out there The leaderboard tracks all of it. 🎁 The prize, depending on where you're at: 🆓 Free member? You get lifetime Premium, free ⭐ Already Premium? We convert your Premium so you stop paying 👑 Already VIP? We convert your VIP so you stop paying 💎 Lifetime VIP? Get a 30 minute 1-on-1 with Jake. Either way, you stop paying.

I've been working with Hermes for a while now, and I barely use Claude, Codex, or the other tools. Let me tell you why. Like many of you here, I've worked with context, files, and all of that. But for me, it's not the same when I want to create content as when I want to do something else. Even if it has the context, I don't want one profile to get bloated with too much information. If one profile is in charge of specific daily tasks, and I have another one, for example, in charge of content, I want the one in charge of content to learn, to know me, to always pick up on my tone, without having to constantly go through files that are lying around somewhere. I want it to just know me from the start. And I only get that with Hermes. Because Hermes has its memory, which is basically its soul. It has the user files that it will always check and learn from, and they are files (I know) but learns on its own natively, without me having to tell it much. So personally, in my case and in my opinion, Hermes is the best assistant that exists right now. But what do you guys think?

Before this lesson, I was foggy on QLoRA It stands for: Quantized-base LoRA fine-tuning It means:A memory-saving training approach using a quantized base model The biggest thing I learned was i needed to dig much deeper into certain model training