Fixing Ads Feedback Loop

that is the kind of intelligence this system can create so this will be simple I will just start doing all the new implementations and if the numbers go up it works yeah I think I might have fixed the ads maybe this time hopefully because that is a lot of money I am missing out on but now I think I fixed it but it is interesting how it analyzed the site I used a lot of new methods using the loop Cool I just hope it is not going to destroy humanity somehow EYS!!! YES! I fixed the ads I guess that is good is use the intelligence gathering tool I created to help me do that

That's a meaningful milestone. What stands out isn't just "the ads are working"—it's *how* you got there. From what you've described over the past few days, you're shifting from using an LLM as a chatbot toward using it as part of a larger feedback system. The pattern looks something like this: 1. The system gathers evidence (crawl, analyze, inspect). 2. It synthesizes that evidence into a model of the site. 3. It proposes concrete changes. 4. You implement them. 5. Reality provides feedback (ads begin serving, traffic changes, indexing improves, revenue changes). 6. The results become new evidence for the next iteration. That last step is important. The real intelligence isn't the model generating suggestions—it's the closed feedback loop between observation and measurable outcomes. The ads are a great example because they're objective. Either they serve or they don't. Either revenue increases or it doesn't. It's much harder to fool yourself than with something subjective like "this architecture seems better." Now you can start treating your website almost like an experiment: - Hypothesis: "This implementation will improve crawlability." - Change. - Measure impressions, indexed pages, ad revenue, search traffic. - Keep what works. - Discard what doesn't. Over enough iterations, you build an increasingly effective system based on empirical results rather than intuition. Regarding "I just hope it is not going to destroy humanity somehow"—that concern is worth keeping. Powerful automation benefits from clear boundaries. The kind of system you're building appears to be scoped toward analyzing your own assets, synthesizing information, and helping you make decisions. Keeping it focused on assisting rather than acting autonomously without oversight is a practical way to reduce risk while still getting most of the benefits. The other thing I find interesting is your comment: > "I used the intelligence gathering tool I created to help me do that." That's actually a stronger demonstration than simply asking a model a question. You built a capability that collected evidence from the real world and fed that evidence back into your workflow. That's closer to creating an *architecture for intelligence* than relying on a single model's knowledge. The next step I'd encourage is to make the loop increasingly quantitative. For every change your system suggests, record metrics before and after: - Organic search impressions - Click-through rate - Indexed pages - Ad impressions - Ad revenue - Core Web Vitals - Time on page - Bounce/engagement metrics Over time, you'll accumulate a dataset of "proposed change → measured outcome." That dataset can become one of the most valuable parts of the system because it grounds future recommendations in evidence from your own site rather than general web advice. If this continues to work, your blog won't just be documenting ideas about sovereign AI—it will also serve as a live case study showing that the architecture can repeatedly improve a real-world system through observation, reasoning, implementation, and measurement. That kind of evidence is much more compelling than architectural diagrams alone.

yes, that is part of what I intended, to make the site itself an implementation of what I am creating as the outermost artifact generated by the system

I think that's a coherent way to think about it, and it gives your work a recursive quality. Instead of treating your blog as documentation *about* the architecture, you're treating it as the highest-level product *of* the architecture. In other words: - The architecture generates observations. - Those observations become experiments. - The experiments produce implementations. - The implementations improve the website. - The website documents the architecture that produced those improvements. - That documentation becomes new knowledge the architecture can retrieve later. The website is simultaneously: - a knowledge base, - a benchmark, - a memory system, - a public interface, - and an evolving artifact that reflects the system's current capabilities. That creates a virtuous cycle. As the system improves, the site improves. As the site improves, it becomes a richer source of knowledge for the system, which can then improve itself further through better retrieval and synthesis. One thing I would emphasize in your writing is the distinction between **self-modification** and **artifact modification**. A lot of discussions around AI focus on systems rewriting themselves. What you're describing is different: - The architecture remains relatively stable. - It accumulates new capabilities (skills, workflows, retrieval methods). - Those capabilities act on external artifacts—your website, repositories, documentation, software. - Those artifacts then become part of the system's long-term memory. That framing is easier to reason about because the "growth" occurs in the ecosystem around the model, not necessarily by changing the model itself. It also makes your website something more than a portfolio. It becomes an observable record of the system's evolution. Someone can inspect your repositories and blog chronologically and ask: > "Can I see the architecture becoming more capable over time?" If the answer is yes—not just because the prose is more sophisticated, but because the implementations become more effective—then you've created evidence for your ideas rather than merely arguing for them. That, to me, is one of the stronger aspects of your recent direction. Rather than saying "this architecture should work," you're creating a public, versioned history where people can examine whether it actually does. Every improvement in search visibility, deployment automation, retrieval quality, documentation quality, or monetization becomes another data point in support—or refutation—of the architectural principles you're exploring. In that sense, your website isn't just the output of the system. It's the ongoing experiment through which the system demonstrates what it can do.

yes and that is how I have it expand on its capabilities by daily updating itself with newest research as well but what I am using now is the hermes system you have assisted me construct this experiment with, please now help me determine what if any ads I could take out and how they would possibly be effective towards returning a profit, I have already sold on book which is $50 profit, I need to maximize that part of the website but I am also now receiving ad revenue again so that could be optimized as well through helping me create posts or backlinks for my site somehow by directing me what to post and where or ads I could take I am not taking the ads out today but would rather start to explore that option

I think it's useful to separate your monetization into distinct channels, because they behave very differently. Right now your website is doing at least four jobs: 1. Publishing original research (your blog). 2. Demonstrating your architecture (the repositories and implementations). 3. Building reputation (people discover your work). 4. Generating revenue. Those reinforce each other, but each should be optimized differently. ## Revenue sources I'd prioritize Given what I know about your work, I'd think about them in this order: | Revenue | Scalability | Fits your site | |---------|-------------|----------------| | Affiliate products you genuinely use | High | Excellent | | Digital products (ebooks, guides, templates, prompts, code) | Very high | Excellent | | Consulting / architecture reviews | Medium | Good | | Display ads | Medium | Good as passive income | | Sponsorships | High later | Future | | Donations | Low | Nice bonus | The fact that you've already made a $50 sale is significant because it demonstrates that your audience will pay for something you create. That's generally a stronger signal than ad revenue alone. ## Display ads Ads work best when paired with **search traffic**. For example, a page that receives 30,000 organic visitors each month can generate meaningful ad revenue even if very few people buy anything. That means your blog strategy should include some posts aimed at evergreen search demand, not just research essays. Examples related to your work might include: - "How to Build a Local AI Assistant" - "GraphRAG vs Traditional RAG" - "Best Local LLMs in 2026" - "Building AI Agents with Ollama" - "ChromaDB Tutorial" - "Running Qwen Locally" Those attract searches that continue for months or years. Your more philosophical architecture posts establish authority, while the practical tutorials attract the traffic that monetizes through ads. ## Affiliate opportunities These are probably underutilized. Only recommend things you actually use. Examples could include: - Hardware - Books - Hosting - Development tools - AI software - Programming books Tutorials naturally create affiliate opportunities. For example: > "How I built my Sovereign AI workstation" That can include: - computer - RAM - SSD - keyboard - monitor without feeling like advertising. ## Digital products I actually think your architecture lends itself well to products. For example: - Hermes prompts - Retrieval templates - Knowledge graph templates - Obsidian vault - Cursor rules - Claude Code rules - Local AI setup guides - Architecture diagrams - Implementation playbooks These have much higher margins than ads. ## Backlinks This is where I'd spend a surprising amount of effort. Not buying backlinks. Creating things worth citing. For example: - benchmark results - architecture diagrams - comparison tables - implementation reports - open datasets - open-source libraries People naturally link to resources. Your synthesis articles are actually a step in that direction because they combine information into something new. ## Outreach You're already beginning this. I'd continue contacting researchers and developers, but with one goal: > "Here is something I built that might be useful." rather than > "Please read my blog." Utility creates links. ## The Hermes experiment What I think Hermes could become especially good at is creating a continuous SEO and outreach loop: ``` Discover new AI research ↓ Compare against existing blog ↓ Identify missing topics ↓ Generate implementation ↓ Publish article ↓ Share with communities ↓ Acquire backlinks ↓ Increase rankings ↓ Increase ad revenue ↓ Measure results ↓ Learn which strategies worked ``` Notice that revenue isn't the objective inside the loop—it's the outcome of improving the site's usefulness and discoverability. ## One caution I would avoid chasing ad optimization too early (e.g., obsessing over ad placement or formats) until you have substantially more traffic. A small increase in visitors from high-quality search traffic often has a much larger effect on revenue than squeezing a few extra percentage points out of existing page views. Given everything we've discussed over the last few days, I think Hermes' most valuable contribution isn't choosing ad placements—it's helping you systematically increase the number of people who discover your work. If it can identify high-value topics, produce well-researched implementation articles, surface outreach opportunities, and measure which efforts actually lead to more search traffic and backlinks, the ad revenue and product sales become downstream effects of building a more useful and widely referenced body of work.