Maximilian Wenzel

SEO, Online-Marketing, Selbständigkeit, Aktien, Solar & vieles mehr - oder weniger...

[Malte Landwehr @MalteLandwehr]

People often claim that prompt tracking cannot work because every prompt is unique.

We ran the numbers and can confidently say that this concern is not justified.

This conclusion is based on two experiments:

1️⃣ We analysed two sets of prompts that were written by Rand Fishkin’s followers.
2️⃣ We created our own prompt sets where we changed a set of base prompts by the minimal possible amount as often as possible without changing the intent

Here are the facts:
• While every human-written prompt was unique, 90% fell into a bucket of similarity where the likelihood of a brand being mentioned in the LLM answer does not really change.
• The style of the prompt matters a lot. Asking for the best or a list can greatly increase the number of mentioned brands. Giving the LLM a role (“you are an expert on SEO”) leads to fewer brand mentions.
• Both top and bottom of funnel prompts are robust against wording changes. Mid of funnel prompts however are much more sensitive. Small variations can quickly surface different brands in the answers.
• In ChatGPT and Perplexity, constraints reduce the number of brands shown. In Gemini and Google AI Overviews, constraints actually increased the number of brands. Potentially by triggering additional fanout queries.
• The length does not matter. As long as the intent stays the same, conversational fillers words do not significantly impact AI answers.

What does this mean for me?
• Do not obsess over exact prompts wordings. Focus on topic, intent, funnel stage, and context.
• Consider to be more granular in the mid of funnel prompts you are tracking. Here every prompt variation is most likely to surface additional brands, sources, and insights.

[Maximilian Wenzel @AllblogsDe]

@CyrusShepard Even if I pass over all my content via llms.txt?

[Chris Long @chris_nectiv]

Heads up SEOs: Google has officially rolled out ads in AI Overviews + AI Mode.

There are now three "Sponsored" results at the bottom of many answers.

[Maximilian Wenzel @AllblogsDe]

@gaetano_nyc No, because it's not part of the rfc robots.txt standard https://t.co/L17vbxbnAr

[Daniel Foley Carter @foley_seo]

Claude runs your SEO for $20 a month.

Yeah we know.

[Malte Landwehr @MalteLandwehr]

I am hiring for a Product Designer role at Peec AI.

Not a “please make this prettier” role. A real product role.

You will contribute to our design system, talk to users, and work side-by-side with product managers and engineers.

The role comes with a lot of ownership and I expect you to raise our quality bar.

You will join a world-class product & engineering team (ex Google, Tesla, Klarna, etc).

https://t.co/3H8wtr9ia4

[Maximilian Wenzel @AllblogsDe]

Ist ja super, dass man bei Claude jetzt 2x Tokens bekommt, wenn ich aber nicht mal meine normalen Tokens nutzen kann, was soll das dann bringen? @claudeai Vormittags kann ich Claude nicht nutzen & am Nachmittag sind meine Tokens schneller alle, als ich eine Frage stellen kann...

[Maximilian Wenzel @AllblogsDe]

@CodevolutionWeb Awesome! Thank you, this helps a lot. 📈

[Maximilian Wenzel @AllblogsDe]

@aleabitoreddit What are your thoughts on helium suppliers in this context?

[Maximilian Wenzel @AllblogsDe]

@notgrahamp Will learn this

[Maximilian Wenzel @AllblogsDe]

@klemenznidar Same here

[Lenny Rachitsky @lennysan]

My biggest takeaways from @sherwinwu:

1. AI is writing virtually all code at OpenAI. 95% of the engineers use Codex, and engineers who embrace these tools open 70% more pull requests than their peers, and that gap is widening over time.

2. The role of a software engineer is shifting from writing code to managing fleets of AI agents. Many engineers now run 10 to 20 parallel Codex threads, steering and reviewing rather than writing code themselves.

3. The average PR code review time has dropped from 10-15 minutes per PR to 2-3 minutes. Every pull request at OpenAI is now reviewed by Codex before human eyes see it, and Codex surfaces suggestions and catches issues up front. This allows engineers to focus on more creative and strategic work while dramatically increasing productivity.

4. The models will eat your scaffolding for breakfast. When building AI products, don’t optimize for today’s model capabilities. The field is evolving so rapidly that the scaffolding (vector stores, agent frameworks, etc.) that seems essential today may be obsolete tomorrow as models improve.

5. Build for where the models are going, not where they are today. The most successful AI startups build products that work at 80% capability now, knowing the next model release will push them over the line.

6. Top performers become disproportionately more productive with AI tools. AI tools amplify the productivity of high-agency individuals, so the gap between top performers and everyone else is widening. The ROI on unblocking and empowering your best people compounds faster than ever in an AI-augmented environment.

7. Most enterprise AI deployments have negative ROI because they’re top-down mandates without bottom-up adoption. Success requires both executive buy-in and grassroots enthusiasm. Sherwin recommends creating a “tiger team” of technically-minded enthusiasts (often not engineers) who can explore capabilities, apply AI to specific workflows, and create excitement throughout the organization.

8. The one-person billion-dollar startup is coming, but with unexpected second-order effects. As AI makes individuals more productive, we’ll see not just billion-dollar solo founders but an explosion of small businesses: hundreds of $100M startups and tens of thousands of $10M startups. This will transform the startup ecosystem and venture capital landscape.

9. Business process automation is an underrated AI opportunity. While Silicon Valley focuses on knowledge work, most of the economy runs on repeatable business processes with standard operating procedures. There’s massive potential to apply AI to these workflows, which are often overlooked by the tech community.

10. The next two to three years will be the most exciting in tech history. After a relatively quiet period from 2015 to 2020, we’re now in an unprecedented era of innovation. Sherwin encourages everyone to engage with AI tools and not take this moment for granted, as the pace of change will eventually slow.

11. AI models will soon handle multi-hour tasks coherently. Today’s models are optimized for tasks that take minutes, but within 12 to 18 months we’ll see models that can work on complex tasks for upward of six hours. This will enable entirely new categories of products and workflows.

12. Audio is the next frontier for multimodal AI. While coding and text get most of the attention, audio is hugely underrated in business settings. Improvements in speech-to-speech models over the next 6 to 12 months will unlock significant new capabilities for business communication and operations.

[Maximilian Wenzel @AllblogsDe]

@timbuiltit Awesome! 📈

[Google Search Central @googlesearchc]

Today, we're announcing an algorithmic change that enhances the quality of articles that appear on Discover. We're calling this the February 2026 Discover Core update.

Find out more in our blog post at https://t.co/AqLkmqcoPG

[Maximilian Wenzel @AllblogsDe]

@notgrahamp Thanks, that helps! I am working on engagement and more followers. 📈📈📈

[Maximilian Wenzel @AllblogsDe]

@notgrahamp Yeah, but I think it is easier with 100k than 10k followers to start viral content?!? 🤔

[Maximilian Wenzel @AllblogsDe]

@lilyraynyc Done :)

[Maximilian Wenzel @AllblogsDe]

@BowTiedBlogga Yes, on sunday

[Maximilian Wenzel @AllblogsDe]

@timbuiltit Which AI tool?