There’s been a few things popping up in my feeds about AI use in books, music, and elsewhere. I wanted to analyze them and see how it applies to my target, B2B SaaS companies.

The controversy of a French novel that didn’t pass the Pangram test

There’s been some noise on Twitter about a recently published book that sounded a bit like AI wrote it so someone used Pangram and the result was the book was AI-assisted. It’s not a random book because it already has some awards.

Whether it is AI-assisted or not isn’t a big deal for the general public, but for the ones who are passionate about literature, it is. Here’s a quote from a video of former French teacher.

Personally, I’m not the least bit interested in reading a book written by AI. Because for me, a book written by AI is a contradiction in terms insofar as it doesn’t write anything. You can’t be moved by a text produced by AI because there’s nobody behind it. No lived experience, no emotion, no desire, no despair, no effort, no story, no destiny, no struggle, no sorrow, no joy, nothing, absolutely nothing of all that explains not only literature but speech itself.

If you read books because you look for all those things from the authors, then AI is devastating to your experience. But readers of dark romance don’t care much. That’s why it’s important to differentiate literature from commercial books in that debate.

The #1 song in South Korea is made by AI

A song called “The Adult I Dreamed Of” from a Korean independent creator was released at the end of June and has been #1 on some charts.

I say some charts, because it hasn’t been on the main charts. And in K-Pop, toxic fans absolutely want their bias (i.e. favorite singer) to have a Perfect All Kill (PAK) when a new song is released, meaning achieving #1 on all the charts. Which hasn’t been possible because of this AI song.

One artist has finally achieved PAK and the comments (on /r/kpop) are livid.

Absolutely diabolical that an AI generated song almost blocked this from happening…the state of music charts in SK is starting to look bleak, and this is just the beginning.

But a lot of people don’t care about all of this and will listen to any song that they find good and is trending.

K-Pop fans like to lash out on the general public (GP), term they use for anyone who’s not a hardcore fan that I find pretty condescending. Like the GP has shit taste and fans have such a refined palate to like this or that. Well it’s all very commercial music so STFU.

Opus 5.5 is really good at generating music videos

And then over the weekend, a few videos made with Opus trended on Twitter. From product lunch videos to music videos.

Here’s one made by Anabology, a biohacker I’ve been following for years. It’s been watched by millions of people, retweeted by Elon, and shows how good AI has become.

It could be interpreted as just AI fans loving the capabilities of Opus 5.5 but I think it’s good enough to be on a playlist for a lot of people.

It’s been the same with VFX & CGI

For Zack Snyder’s Justice League movie, Henry Cavill (Superman) had a reshoot at the same time he was shooting for a Mission Impossible movie and couldn’t contractually cut his mustache/beard. So the VFX team had to digitally remove the beard. I had friends who hated the bad VFX. But frankly I don’t really see anything shocking. Maybe I don’t pay attention to these things enough, or maybe my brain just ignore it or adapts to it. But I really didn’t mind the result.

Messaging is more important than your voice

People care about themselves. They want something that talks to them. But they don’t really give a shit about the emotions of the author.

Some people really care about the art itself, and if it’s your target then better not to use AI.

If your target isn’t completely full of themselves, and it’s a majority of people, then use of AI is completely fine. As long as you nail the messaging and you don’t sound like a clanker.

And that’s what I’ll explore in the next part, the various ways AI can help you nail the messaging of your B2B product.

NB: It is kind of a rant against AI haters and “purists” here and there, and I want to point out I really love to understand the emotions and processes of authors, creators, and producers. But I don’t have the bandwidth for all of the culture I consume to be like that. That’s why I’m fine with consuming good AI content.

Single prompt generation

Ok I’ve never done this one. I knew from the beginning I needed more context than just asking “write me a newsletter about common mistakes when generating AI content”.

But I prefer to be explicit about it: don’t do that!

Tropes are not a styleguide

I thought my list of AI tells was enough. For Claude, it almost is. Claude has a fairly neutral default and it follows examples. Tell it what not to do and it usually stops.

The others do not. GPT wander. Grok will follow the list, then still write like a model unless you also tell it what to do.

Tropes.md is the don’t list. A styleguide is the do list: contractions, first person, mix of long and short sentences, no slogan lines, headings that name the problem. Asking to be “friendly but professional” doesn’t do much. Rules plus examples produce something you won’t have to paraphrase and can easily edit.

No references

You will not get your voice from instructions alone.

My process for a new format: pick the type of post, find the best examples I can, write down what they do well and what they do badly, generate a draft from these examples and my styleguide, then edit until it is something I would send. That edited piece becomes the reference for the next ones.

Raw model output as a “reference” is how you make slop. Generated-then-hand-edited is the way to go.

Dirty references rot everything after them

I messed this up last week. It is why I sat down and reviewed the whole setup.

My agent made a few mistakes in a new post. I did not fix them all. I used that post as the reference for the next batch. Everything new came out with the same tells.

Your references have to be clean. If you skip the edit, you teach the model to copy the slop.

Using the wrong model

A few months ago only Claude was actually good at writing. Now the gap has closed but I made the mistake of trusting the others too much.

• Claude still has the least AI-sounding first draft. It’s neutral, follows references, works even when the styleguide is thin.

• GPT 5.6 Sol and Astra are inconsistent on this job. Sometimes they match Claude, sometimes they ignore tropes.md and don’t follow references.

• Grok 4.6 is the compromise I like most. The native style is not as good as Claude but it follows references and it is consistent. With a real styleguide it gets close enough that I stopped paying for a standalone Claude sub. I still have Opus and Fable in Cursor when I lose patience but it’s rare.

People keep saying Gemini 3.8 and GLM 5.3 are as good or better now. That is not my experience. GLM is getting close. Gemini is still not better than a research intern: great at analyzing sources, but outputs a corporate memo.

Update: I wrote that right before Grok 4.7, Opus 5.5, and GPT-6. I’d still stick to Claude models. And for some reason Grok 4.6 seems to have gotten worse so take what I say about it with a grain of salt.

High effort makes copy worse

Ultra thinking helps agents write code but on copy it overthinks the sentence. Low or medium is enough. Plus it saves tokens!

Hermes is a mediocre writing harness

I like my Hermes agent because I can just queue a lot of research and get my dose of vitamin D in the meantime. But when it comes to generating content, I’m still on the fence. Like GPT models, it seems to take some liberties with my references. It might be a configuration issue but for now I usually do the generation with Cursor or another harness locally.

Skills will override your voice

Hermes will write its own skills. And they might not match how you want the copy to sound. If you leave them unmaintained, they inject AI tells back into every run.

Open-source copywriting skills are mixed. A repo can have one good skill next to three bad ones. You need enough copy judgment to know which skills to use.

Skills also outrank your styleguide. If a skill says something that goes against your voice, the skill wins. Either fix the skill or turn it off. Don’t wait or you’ll spend a lot of time fixing everything later.

“Don’t add claims” and pick another model

When I write about health, I bring sources that do not always match mainstream science. ChatGPT treats that as a fact-check assignment. You can push back, but it is more work than it is worth.

Telling the model not to add claims helps. Switching to a model that won’t fight you helps more.

It still won’t necessarily pass the Pangram test

Even by doing all of that, you’ll have to do some manual editing.

Because removing all AI tells is almost impossible and no model will get your tone of voice right. Even when you think you caught all the AI tells, you notice new ones you were not before since there were so many tells.

But it has cut something like 80% of my editing time.

That’s what happened to Feature Upvote, a client of mine.

I’ve been in the industry for 20 years. I have long relationships with some of my clients. I have seen them go through multiple positioning shifts.

And I wanted to share some of these stories with you. Here’s the first one.

Feature Upvote, a feedback board tool

Steve McLeod, the founder of Feature Upvote, started it around the same time as Canny in 2017, the current leader of the category.

There was already another leader at the time: UserVoice. But it was not the best software and they went upmarket, so there was a big opportunity.

When popular software went upmarket

I remember that time in 2015-2020 where very popular software went upmarket and became inaccessible to many companies in a time where competition was very sparse. Zendesk comes to mind. It created a ton of opportunities everywhere.

Here’s a 2016 article from Tomasz Tunguz talking about it. In summary: SMB CAC and lead volume don’t scale; ACV, sales team, and enterprise features do. VCs pushed a lot of companies to go upmarket.

Then the category filled up

Move forward to today and there are new tools in the feedback board software category every week, maybe every day.

Not easy to stand out and grow. Plus, the product is not really a painkiller for many businesses:

• Most businesses don’t have that much feedback to manage or just don’t actively try to get feedback.

• And even if they do, they might just use a form builder and a spreadsheet because they don’t need a tool to manage 10-20 comments per month.

Finding a painkiller audience

Steve noticed that problem and looked at how he could reposition Feature Upvote to make it into a painkiller.

He found that a growing share of customers were video game companies. No other tools in the category were catering to that audience. And these companies get a LOT of feedback because they have hundreds of thousands of players instead of a handful of paid customers.

Plus feedback without a board is all over the place: on Steam, Discord, Reddit, it’s a total mess to manage.

So he decided to niche down and focus on video game companies more specifically when marketing Feature Upvote.

Another reason he wanted to make that change was that video game customers are more enjoyable than “boring corporate” SaaS.

You can listen to his podcast on the reasons he repositioned Feature Upvote here.

The old channels didn’t fit

But this change had a problem: the channels fitting that new audience were not the same.

I did some SEO content for Feature Upvote and we tested video game content a bit but from my research and experiments, there weren’t many opportunities.

People in that industry are on Discord communities and give recommendations about tools they used there. Hard to reach people without being part of these communities.

So instead he created a podcast interviewing people in the video game industry and it works pretty well.

I also helped him on:

• the homepage messaging

• interviewing some video game customers for some fresh customer stories

• writing guides based on the advice that was in the 30+ podcast episodes

Why most of the category won’t follow

I see too many founders afraid of niching down, so it gives you an edge if you do it.

Also not everyone in that category will make the switch:

• Classic distribution channels they’re used to don’t work (SEO, ads at least on some platforms).

• Podcasting isn’t as easy as writing content. Steve had another podcast before so he knew how to do it.

• And he also has to go to video game conventions like Gamescom which not everyone can afford if they have a small SaaS.

Anyway lots of reasons why this ICP is underserved and competition might never arrive.

My agent has a workspace with the context, instructions, skills, tools, tasks, and previous work it needs to do the job.

I keep every client in the same workspace which lets one agent handle research and content across the agency without setting up a separate agent for every account.

Not everything is automated. While I advocate for more automation, it’s always good to have a human review the work. So after each big step, I look if everything is good or it needs another iteration.

This is what the current setup looks like.

Key takeaways

• One workspace holds the instructions, client knowledge, custom skills, and finished BOFU content.

• I use Linear for the task queue for both me and the agent. I assign work by adding an Agent label.

• Three skills cover the main production sequence: find the products, research each product, and write the content.

• Hermes can refresh competitor research and use the changes to update existing content, but I review the result before publishing.

Workspace structure

This is a simplified version of the workspace:

workspace/
|-- AGENTS.md
|-- context/
|   |-- client-a/
|   `-- client-b/
|-- system/
|   |-- style-guide.md
|   `-- other-instructions.md
|-- .agents/
|   `-- skills/
|       |-- discover-products/
|       |-- research-products/
|       `-- write-bofu-content/
`-- work/
    |-- client-a/
    `-- client-b/

AGENTS.md tells Hermes where everything is

My AGENTS.md is mainly a map of the workspace.

It tells Hermes how the folders are organized, where the skills live, where to find the instructions, and which style guide applies to the work. This gives the agent enough information to orient itself before it starts a task.

I do not put every detailed rule in this file. Once it knows where things are, it can read the relevant material for the current job.

context/ holds the knowledge

The context folder contains the information Hermes needs to understand the client and make decisions that fit the account.

That includes reference material, strategy documents, positioning, previous research, competitor information, and anything else that should influence the content.

There is also a shared area for knowledge that applies across clients. Client-specific context stays in a separate folder for each account.

I use one workspace because the harness is smart enough to follow that structure and load the correct client context. The separation is explained in AGENTS.md.

With the rise of Grok Bot and Hermes doing the same, it seems people are going more and more for one agent per role but I haven’t really had the need for this kind of structure yet. I tend to do what works and improve my system only when the need arises.

system/ holds detailed instructions

The system folder contains instructions that apply to the work but do not need to be packaged as skills.

My style guide lives there, along with more detailed rules I haven’t yet converted into skills or are too loose to be skills. AGENTS.md points Hermes to the right instructions instead of trying to contain all of them itself.

.agents/skills/ contains repeatable procedures

Skills are for tasks with a defined method. They tell Hermes how to perform a specific job rather than giving it another collection of general preferences.

I currently have three main skills for BOFU content:

1. Discover the products in a category

2. Research each product

3. Write the content

work/ contains the content

The work folder is where the BoFu content goes.

Each client has a separate folder, just as they do under context/. The context folder contains what Hermes needs to know, the work folder contains what it produces.

That distinction makes it easier for the agent to find previous drafts and update existing content without mixing source material with deliverables.

MCPs and tools I give Hermes

Make.com

Make now lets you create an MCP server and assign workflows to it.

For example, Hermes can request a Google Search Console query through a Make workflow and receive the results. This gives it access to the report without requiring a separate custom integration for every workflow I want to add.

Linear

Linear is where I centralize tasks for myself and the agent.

I write the issues there, decide which ones are ready, and add the Agent label when Hermes can take them. The agent checks Linear, claims eligible issues in Todo, and leaves updates and comments as it works.

That gives me one place to see the work in progress. I do not need a separate agent dashboard (Hermes has a built-in kanban) or a parallel task list that I have to keep synchronized with Linear.

Grok and Firecrawl

Hermes has a web search setting that I can connect to my Grok subscription. In practice, it covers much of the work I would otherwise use Firecrawl for.

Apify

I use the Apify MCP when a site or source cannot be handled through the usual search and scraping route.

Some websites need a specialized scraper. Apify gives the agent another option without making it pretend that it accessed a page it could not retrieve.

If a source remains inaccessible, Hermes reports that it could not access it. It reduces the risk of the AI lying to me.

Why I do not use DataForSEO

I could add a DataForSEO MCP, but I do not find it necessary for this work.

I choose BoFu topics by understanding the category, the competitors, and the questions a buyer asks near a decision. I get a better feel for those opportunities manually than I do from keyword-volume estimates.

For BoFu content, Ahrefs and DataForSEO numbers are guesstimates at best. A narrow query may show little or no volume even when the few people searching for it are valuable.

My three BoFu skills

The workspace tells Hermes where to look, the skills tell it how to complete the work.

1. Finding every product in a category

The discovery skill searches the web for products that belong in a category.

This creates the competitive set for later research. It is useful for obvious competitors, but it also helps surface smaller products that would be easy to miss if I relied only on the names a client already knows.

The goal is to establish which products need investigation before anyone starts writing a listicle or alternatives page.

2. Researching each product

The research skill creates the information needed for an honest comparison.

It checks sources such as product pages, pricing pages, documentation, feature pages, and public reviews. It extracts pricing, features, use cases, limitations, customer opinions, and other facts that may affect the comparison.

The agent records when it cannot access a source. It should not replace missing information with something that sounds plausible.

This research can then support several pieces of content without starting from zero each time.

3. Writing the BoFu content

The writing skill turns the research into the format required by the Linear issue.

That might be a competitor alternatives guide, a best-tools roundup, a versus page, a three-way comparison, a review guide, or one of the other formats I use.

The client strategy documents already explain the positioning and the angles I want to take against different alternatives. Hermes can use those decisions while drafting instead of inventing a new strategy for every article.

I still check whether it applied the right angle. This is one of the areas where the agent can make a reasonable choice that is still wrong for the client.

How a Linear issue becomes BoFu content

The working process currently looks like this:

I create a Linear issue and add the Agent label
    |
Hermes finds the issue in Todo and claims it
    |
It reads the client context and relevant instructions
    |
It discovers or refreshes the products when needed
    |
It researches the products and comments on its progress
    |
I review the research and correct any gaps
    |
Hermes writes the requested BOFU content
    |
I check the angle and comparison details
    |
I edit and publish the content manually

The review after research is important. It is much easier to correct the source material before the draft exists.

The second review is more editorial. I check whether Hermes understood the angle, treated the competitors fairly, and got the small comparison details right.

I publish manually. The agent can prepare the content, but I want the final decision before anything appears on a client site.

Recurring work

The same setup is useful after the first article is finished.

Hermes can check Linear for tasks carrying the Agent label, refresh existing competitor data, and update BoFu content when that research changes.

This matters because competitor content decays. Pricing changes, features move between plans, products change positioning, and reviews reveal new patterns. The original research stays in the workspace, so the agent can compare what it finds now with what it used before.

When an update is needed, Hermes revises the content in work/ and leaves the final review and publication to me.

I’ve become an editor-in-chief

That’s how I feel currently and I like it. I let my agent work and I am responsible for the content quality and strategy.

On top of that, it allows me to spend more time testing content formats to find new ways to rank better on Google and be more visible in AI search.

The agent takes repetitive research and first-draft work off my plate. Each review can also improve the next run. When the angle is wrong, I update the client strategy instructions. When it misses a comparison detail, I adjust the research or writing skill instead of correcting only that article.

This means the workspace gets better as I use it. Hermes starts the next task with the correction already available, while I keep control of the decisions that affect the client.

They analyzed 116 B2B websites and 1,112 transactional pages. Versus pages had the strongest correlation with AI-referred sessions, ahead of alternatives and “best software” pages.

They have more data than I do, but my experience with B2B SaaS is different.

In the competitive product categories I work in, I see alternatives and best-tools guides taking more of the search visibility and traffic than versus pages, whether it’s AI search or Google. Versus pages still have a role, but I would rarely build a comparison-content system around them alone.

This is how I would prioritize the 6 formats I currently use.

Key takeaways

• A connected group of pages gives your brand more chances to appear when an AI system turns one prompt into several searches.

• In competitive B2B SaaS categories, I would start with competitor alternatives and best-tools guides before building a large set of versus pages.

• Three-way comparisons and review guides are useful in the right category. Third-party pricing guides are usually my lowest priority.

Why my experience differs from the study

Siege Media looked across B2B. I work almost exclusively with B2B SaaS companies in crowded categories where dozens of sites already publish comparison content.

A versus page may send more measurable referral traffic when it gets cited. An alternatives guide can still give your brand more chances to appear across a larger set of searches and influence an answer without earning the final click.

I think both findings can be true. In the categories I monitor, though, alternatives and best-tools pages usually are driving more traffic too.

Query fan-out creates more ways to appear

When someone asks ChatGPT or another AI tool for the best product in a category, they may search the web several times before answering.

For a prompt such as “What are the best X tools?”, it might search for:

• best X tools

And then it’s going to shortlist a few brands and follow-up with queries such as:

• site:[competitor.com] X features

• best X tools [competitor 1] [competitor 2] [competitor 3]

• [competitor] pricing

• [competitor] reviews

• [competitor] alternatives

• [competitor 1] vs [competitor 2]

The exact searches change with the prompt and the system. But the pattern stays the same: one broad question can become several narrower searches.

That gives a connected set of comparison pages more opportunities to supply evidence than a handful of isolated versus pages. It is also why I plan comparison content as a group of pages rather than one keyword at a time.

1. Competitor alternatives

If you operate in a competitive category, you may have dozens of credible alternatives guides available. Each competitor creates a separate angle, and each page can address the reasons buyers leave that particular product.

I would not automatically start with the category leader. The obvious “[market leader] alternatives” query is often the most competitive one. Look for established competitors whose alternatives results are weak, outdated, or dominated by generic roundups.

Those pages may have less traffic today. They can still bring as much qualified traffic as a harder keyword where you sit near the bottom of the results. If the competitor grows, you also have a page with history rather than a late entry into the query.

2. Best-tools guides

I put best-tools guides alongside alternatives pages for potential. I rank them second because most products have fewer credible best-tools angles than competitors.

A product with many distinct use cases can support a larger set of guides. A narrow product may have two or three useful angles before the ideas become forced.

You can expand into adjacent use cases, but the further you move from the product’s main job, the weaker the traffic and conversion usually become.

3. Versus pages

I started my bottom-of-funnel content service by producing versus pages. Over time, I have seen them bring less traffic in competitive categories while alternatives pages take over searches that used to surface direct comparisons.

I still make versus pages but their role has changed.

They work best as detailed supporting pages. A broader guide introduces several products, then links readers to the relevant head-to-head comparison when they want more detail. The versus page can cover features, pricing, limitations, ideal customers, and the decision between the two products without forcing all of that into the broader article.

4. Three-way comparisons

This is one of my favorite formats. You compare two larger competitors and add your product as a third option.

Someone searching for “[competitor 1] vs [competitor 2]” already knows the category and is close to making a decision. A useful three-way page lets your product enter that evaluation even when the original search did not mention you.

In a competitive category, you can create 100s of three-way comparisons. Even though they won’t bring much traffic individually, together they can give your product more chances to be visible.

5. Review guides

A review guide collects the recurring opinions buyers have shared about a competitor across public review sites.

The useful version does more than paste quotes into a roundup. It groups reviews by theme, separates common complaints from isolated ones, and shows what type of customer appears to have each experience. Quote people accurately, link to the source, and keep the date of the research visible.

Screenshots adds visual proof. They also make the page easier to scan than a long wall of paraphrased reviews.

Review guides are especially helpful when a competitor’s reputation is an important part of the buying decision. I would not prioritize them in a category where reviews are sparse or say little beyond generic praise.

6. Pricing guides

I have tested third-party pricing guides and seen them mostly ignored by Google and AI systems.

I have also heard of ChatGPT adding words such as “official” when it searches for pricing. That makes sense: the vendor’s own pricing page is the most reliable source, and third-party pricing becomes outdated quickly.

It can still be useful when a market leader has complex, opaque pricing that buyers need help understanding, but it requires frequent maintenance and may never earn much search visibility.

A page or a blog post?

I expect thin pages under 500 words to keep losing ground. Automating a basic comparison is easy now, so publishing another block of text is not much of an advantage.

My next experiment is to turn long comparison guides into designed pages. A custom page gives you more control over tables, navigation, filters, screenshots, and product details than a standard blog template or a Markdown-based publishing workflow.

I think readability will become a bigger source of differentiation as comparison content becomes more automated. For a competent team, research and first-draft copy are becoming solved production problems. Helping a buyer reach the right decision quickly is still a design problem.

The format should not come at the expense of substance though. The page still needs to answer the questions a buyer asks and give AI systems clear facts they can retrieve.

One or two pages will not create much visibility

A single alternatives guide and two or three versus pages rarely cover enough of a competitive category. They leave most use cases, competitors, and buyer questions unanswered.

I would start by mapping your closest competitors and highest-value use cases. Then choose the pages where buyer intent is strong, your product has a credible place in the comparison, and the existing results leave room for something better.

AI makes the production side much faster, but accuracy still needs a system. The agent should research each competitor from current sources, preserve those sources, and leave gaps where information is unavailable. It should never fill a missing price, feature, or customer opinion with a plausible guess.

Once that research is in place, an agent can produce comparison content that needs little editing. The hard parts are deciding what deserves a page, building an honest comparison, and maintaining it when the market changes.

If you want my agency to plan and produce this comparison-content system for you, reply to this email. We can map the opportunities, research the competitors, and build the content that give your product more chances to appear in Google and AI search.

Long time no see. I’ve been packed with client work for the last few months (a good thing to have!) but now my energy is finally getting better to write here again.

I’ve been struggling with my health for the last 15 years and am finally mostly healed so I should be more regular with the newsletter. I just published something about my illness if you’re curious.

Anyway, I have a LOT to talk about and will do so in the next few weeks but wanted to start with some quick news.

Best way to get rid of AI tells

I talked about how to avoid generating AI slop before, and since then I’ve found a great website listing all the AI tells and it provides a Markdown file you can just copy into all your projects: https://tropes.fyi/tropes-md

It’s a must-have!

Google spam update

There’s a new Google update to reduce spam and bad SEOs are panicking like always.

“AI content will 100% get websites nuked.”

AI content made with proper context, passes as 100% human written in Pangram, and is useful to the reader won’t get nuked.

But most marketers are too lazy to do the bare minimum.

2x growth rate with BoFu

In my last newsletter, I was talking about making good comparison content. Thanks to these changes, one of my clients has 2x their growth rate year-over-year.

Traffic from BoFu content grew by more than 700% while top-of-funnel content is cratering because of AI search… And middle-of-funnel content still kinda works.

New website for Customer Fitted

I’ve updated my agency website with a new design, new copy, and focusing again only on BoFu to make it easier to grasp even though I do a lot more with my clients.

Check it out: https://www.customerfitted.com/

My AI marketing agent

I now have a Hermes (OpenClaw alternative) set up on a VPS. It’s available 24/7, uses my ChatGPT subscription and my SuperGrok (via X Premium+) subscription so no expensive API usage.

Researching information about competitors takes a long time, so having a dedicated machine to do it eases the load on my aging Mac.

And GPT 5.6 Sol and Grok 4.6 are now as good for content generation as Opus. No reason to keep my Claude subscription anymore.

I’m also using Linear to manage tasks for my agent and I find it much more productive than to use a chat interface.

I’ll share a lot of my processes in the future, it’s so interesting I can’t wait to tell you!

Watermarking

Anthropic released a blog post about watermarking AI-generated content and I wanted to touch quickly on that.

According to the EU AI Act, providers (Anthropic and other labs) have to watermark what they generate.

But a detected mark only shows an AI was involved at some point, it does not claim the AI authored the whole thing or originated the ideas.

And deployers (us, professionals) don’t have to visibly disclose AI use most of the time.

So it’s pretty much as bullshit as AI detection tools.

Finally, the EU AI Act asks providers to mandate in their terms-of-service that users not remove the watermarking.

Manually editing what you got from AI? Forbidden! The situation is just absurd at this point.

Daring Fireball has a great in-depth post on the subject as well as a follow-up that you should read.

Using AI for marketing isn’t an option anymore. Most people are pretty poor writers and they’d get better results with AI. But also small companies have to compete with much bigger ones and they can’t do that without AI.

So your best bet is to learn marketing engineering and avoid the common pitfalls of AI.

That’s my goal for this newsletter going forward. I’ll share skills, MCPs, tools, and my processes on how I build content systems with AI for B2B SaaS companies.

Usually when rankings move that fast, I expect Google to test the page for a few days and settle it lower. That didn’t happen. The gains stayed. And this wasn’t one lucky guide, it was consistent across all six guides.

This was a crowded category too. The competitors have stronger domains, more backlinks to their guides, sometimes 2 to 10x more overall backlinks than my client.

And yet we got to #1 on some of the main keywords with zero backlinks to the guide itself. Backlinks matter, but this was a good reminder that you can beat stronger sites if the page is genuinely better.

What the results looked like

The chart below shows the movement. You can see a sharp jump in average position, roughly from page-four territory to the top few spots.

Traffic roughly tripled on those guides on average, and on some it was more than that.

What I changed

Most of the changes were common-sense improvements. Nothing earth-shattering. But they made the pages much more useful and much more credible.

• I replaced generic pros and cons with review-backed comparisons. Instead of repeating product messaging, I focused on what users actually like, what they complain about, and where each tool is a bad fit.

• I added real quotes from competitor reviews. That made the content feel less like a brand pretending to compare tools fairly and more like an actual buyer resource.

• I included a full list of competitors, not just a convenient shortlist. Each one with its biggest strength, biggest weakness, and pricing summary, all in a table.

• I made the bias explicit. There’s a section at the top acknowledging we’re biased but that we tried to be as fair as possible. I also included flaws of my own client’s product. I think this matters more than people want to admit.

• I tightened the page structure for LLMs. Better intros that give the answer upfront, key takeaways, cleaner tables. That’s about it for the LLM side.

• I improved page speed. The pages were below 50 before, now they’re around 60. I’d like to go higher but there’s a lot I can’t clean up myself. Even this improvement seems to have helped though.

What I think actually made the difference

The review angle changed the quality of the pages more than any SEO trick. Once the content stopped sounding like templated comparison copy and started sounding like a serious evaluation, the guides got better for readers fast.

Page speed is the other candidate. I’m not claiming performance alone got us to #1, but when a page is already competing well, marginal improvements add up.

Some things didn’t move the needle much. I already had schema markup. I added FAQs and cleaned up some schema details, but my opinion is that it’s not very important. I was already front-loading content with a summary table near the top too.

So my conclusion is simple. I was already doing a lot of things right. The extra usefulness, the extra credibility, and the performance lift were enough to push these guides over the line.

Comparison content and LLMs in 2026

When people talk about comparison content in 2026, they often want some new LLM-specific playbook. I don’t think that’s where the main gains are. Most of what worked here was still standard SEO work.

For on-page LLM optimization, the list is short. Give the answer early. Add key takeaways. Use clear tables. Make each section easy to parse. That’s it.

The bigger LLM challenge is off-page. If you want your product to show up in LLM answers, you need other guides and websites mentioning it. That’s a separate effort and one I’m still working on. But the on-page LLM work is a small part of the picture.

How I built all of this without writing a line

The content itself is now drafted almost entirely by Claude. I haven’t written a single line of these guides.

But can you really call it automated? I’ve spent about a year iterating on the system behind it: templates, styleguides, examples, instructions, tools. The process is mine. And everything goes through me.

A big reason the output is strong is that my Obsidian vault is full of competitor research. I research each competitor’s strengths, weaknesses, pricing, and reviews myself. The quotes in the guides aren’t hallucinated by an LLM. They’re in my vault because I found them and verified them.

So I can produce dozens of comparison pages now much faster than before. It still takes me a lot of time to fact-check everything, but that’s it. The results are the best I’ve ever had.

LLMs still don’t replace me at all. Without my expertise on positioning driving them, LLMs would generate slop.

The traffic actually converts

The more important question is whether this traffic has buying intent. Rankings are nice, but if comparison pages don’t move people deeper into the funnel, the win is limited.

Apart from the homepage and a couple of other assets, most of the traffic moving toward pricing pages comes from comparison content.

I don’t control pricing or the signup flow, so I won’t pretend content owns all of the conversion. But these pages are clearly not bringing empty traffic.

What I’d take away from this

A lot of comparison content is still bad for the same reason it was bad a few years ago. It’s written like SEO content first and buyer content second.

What worked for me was making the guides more honest, more specific, and more useful. Not more bloated. Not more optimized for some imaginary algorithm.

Frankly there’s still a lot I could do (that’s my perfectionist side), but being #1 on a few keywords and #2 or #3 on others when you compete against unicorns is really good.

After just two weeks of building Obsidian vaults with Claude Code and Codex, writing custom instructions and using my own marketing frameworks, I’m already far more productive than with my n8n workflows. The output is better too. Agents iterate and make better decisions, so the results are noticeably stronger.

But I still feel completely behind.

That’s the real FOMO in AI right now. “Innovators,” the first 2.5% on the adoption curve, hit the limits of one tool, build the next one, adopt it, hit its limits, and repeat. Each cycle shorter than the last. If you’re an early adopter, you’re picking up the tool they just left behind. By the time you’re productive with it, they’ve moved on twice.

So you end up feeling 6 months behind while being years ahead of everyone else.

Instead of chasing the next thing, I want to share the actual workflow that’s working for me right now. It’s three pieces: Obsidian, a dictation app, and AI agents.

Obsidian with the PARA method

I use Obsidian as my knowledge base, structured with the PARA method from Tiago Forte. PARA stands for Projects, Areas, Resources, and Archives. It’s a way to organize information by how actionable it is rather than by topic.

I keep one vault per client. Then one vault for my agency’s own marketing. And one personal vault. Maybe I could just make one vault for everything, but it feels confusing.

Here’s how each folder works in practice:

• Areas hold every repeatable process I run. Market research, positioning, landing page copywriting, each one gets its own folder with docs like styleguides, templates, examples, and instructions.

• Projects hold active work. If I’m writing a landing page, it gets a project folder. When I’m done, it moves to Archives.

• Resources hold everything I know about a client. Go-to-market strategy docs, competitive intelligence, blog posts, external competitor resources. I try to put as much detail here as possible because the more context my agents have, the better their output.

• Archives are just done projects, moved out of the way.

The key piece that connects Obsidian to AI agents is two files: a CLAUDE.md and an AGENTS.md at the root of each vault. These files point the agent to the right instructions for whatever process I’m running. When I open Claude Code or Codex in a vault, the agent reads those files and knows where everything is.

100K words dictated in a month

This is where 80% of my productivity boost comes from. Not the AI agents, not the fancy vault structure, but a dictation app.

I use Monologue, but Whisprflow or similar works too. The point is voice-to-text for writing prompts and rough notes.

On prompting alone, I write between 1,000 and 2,000 words a day according to my app’s analytics. Before dictation, it was <500. My prompts are far more detailed now, and because of that, my results are far better.

But the UVP for dictation apps isn’t speed in my opinion. What it helps the most with is procrastination.

When you’re faced with writing 500 to 1,000 words of detailed instructions, you’re probably not going to do it. If it’s not urgent enough, you’ll push it off. But talking for five to ten minutes and feeding that to an AI to clean afterwards? That feels doable. So I actually do it.

That shift, from “I need to write a detailed prompt” to “I’ll just talk through what I want,” changes what gets done in my day. Tasks that would sit on my list for a week get handled in a morning because the barrier dropped from typing to talking.

AI agents

I use Claude Code and Codex as my agents. I either open the terminal and work from there, or I use the desktop app, which is better for less technical workflows.

The workflow is conversational. I open the vault, the agent reads my instructions, and I start talking about what I need. The agent iterates with me. If something isn’t right, I tell it and we adjust.

This is where agents beat fixed automation tools like Make, Zapier, or n8n for my use case. Those tools do things automatically, and you need to wire each step correctly. If the output isn’t right, you go back, adjust a node, re-run. It’s enough friction to make you procrastinate on improving your workflows.

NB: You can make agentic workflows with these tools too, but it’s more friction than using Claude Code.

With agents, I just talk. The conversation is the workflow. And because I’m dictating, I can be very specific about the small subtleties I want without it feeling like a chore.

One tip I’ve found useful: if I don’t have formal instructions for a process yet, I just dictate it live. “Do this, then that, then this.” The agent follows along and produces the output. When I’m satisfied with the result, I say “take everything we just discussed and turn it into reusable instructions.” Now that process is documented and ready for next time, without me ever having to sit down and write a process doc.

How it all connects

The three pieces compound.

Obsidian gives structure. Every vault has clear areas, active projects, and deep resources. The agent knows where to look and what standards to follow.

Dictation feeds the agents with detailed, specific prompts. I talk through exactly what I want, and because talking is low friction, I actually give the agent enough context to do good work.

The agents iterate inside that structure. They read my instructions, follow my styleguides, and produce output that gets better each round. And the conversations themselves become the source material for new instructions.

Each project makes the next one faster. The instructions get tighter. The resources get richer. The agent needs less correction. It’s a compounding loop.

This post is proof

I want to be honest about something. This post was written with the same workflow I just described. I didn’t type it. I talked through my ideas using my dictation app (~1400 words transcribed), fed my notes to Claude, built an outline together, validated it, and produced this.

Every idea here is mine. The structure of the workflow, the insight about procrastination, the tips about converting conversations to instructions. But I didn’t write a single sentence with my keyboard (I edited things I didn’t like of course).

The post is my own. The writing process just looks different now.

If you’re still chasing the perfect AI tool, consider that the biggest productivity gain might not come from the agent itself. It might come from making it easier to talk to the agent. A $10/mo dictation app changed my output more than any framework or automation platform.

Which side are you on? Against using AI for content (and other things) or do you embrace AI as much as possible?

For me it’s the same debate as CGI, it’s not that people don’t like CGI, they hate bad CGI.

If people really hated AI content, you wouldn’t see millions of views on AI images and videos.

I had a bet with a friend to see if he would notice when I would switch to use mostly AI to write one of my newsletters (not this one). I gave him 4 newsletters before telling him. When I told him, he was surprised he didn’t notice.

Why? Because I refined my approach over the last year and I still work on the narrative arc myself.

Good AI content is fine. Your competitive advantage should be on taste, not crafting everything by hand.

Let’s explore some of the things I’m doing to improve the writing style of AI.

NB: This post isn’t about the substance of AI content but about form and style.

What does basic AI writing look like?

Let’s start with analyzing what you get with a very basic prompt.

The most famous sign of AI slop has become the em dash — to my dismay — as well as some specific expressions/words/punctuation the average person doesn’t use.

I hate AI expressions so much. Especially this one: “Remember: The AI style isn’t bad—it’s different.”

Cringe.

I remember how I never believed AI could help me write content when ChatGPT was released almost 3 years ago. Newest models are much better though.

Still em dashes, still structure inconsistencies. But fewer cringy expressions.

Here’s an example of what basic prompting gets you.

Look at GPT-5’s output: 1 big paragraph for the intro, followed by 3 bullet lists. If it tries to be readable with the bullet lists, why not have a readable intro?!

Gemini has the better structure here but has pretty cringy words and expressions.

Content examples

The quickest way to get the right structure isn’t a styleguide in my experience but content examples.

While current LLMs generally stick close to the examples, I find Claude tends to be the closest, maybe just because its default tone is better than the others. Especially for long form examples.

Here’s a simple example with a LinkedIn post from a popular creator, and see how they stick to the example with barely any instructions.

But you’re stuck with picking the right example each time. So a mix of content examples with a styleguide explaining what’s good about each example is way more flexible.

Styleguides

The way I did my styleguide was that way. I fed different examples and highlighted I liked about these examples.

I also gave it my personality type. You might think it’s a bit esoteric but it seems to work well. Here’s an example with MBTI types (which I don’t like for personal development, but it’s not the purpose here).

Look at the output for different personalities. Find one that is tolerable, then ask AI to generate a styleguide based on it and edit it manually. I find it to be a good addition on top of the content examples and rules based on these examples.

But don’t think making a styleguide is a one-time thing. Little details you haven’t notice will creep up and get on your nerves. Be sure to add more rules as you use it.

Another thing I do is listing dos and donts. Here’s one for sentence rhythm. It’s one of my favorite rules because predictable pacing plagues AI.

### Vary your beats

- Do: Short. Then long. Then stop. Then twist.
- Avoid: Predictable pacing

Allow/disallow lists

I’ve tried to make a list of things I like and don’t like about AI writing but then I found this one from Rob Lennon and it’s been serving me well.

## Disallowed words, phrases, and punctuation list

- em-dashes “—”
- exclamation points “!”
- fluff
- crickets (“launch to crickets” or other lack of engagement)
- here’s the thing
- here’s the kicker
- void (e.g. shouting into the void)
- authentic
- vanity metrics
- pitch-slapped
- through the noise
- puzzle
- lightning
- magic
- superpower(s)
- superstar(s)
- double-edged sword
- fast track
- strike gold
- secret weapon, weapons, arsenal
- harness
- thrive
- skyrocket
- art/science analogy (“Storytelling isn’t just an art. It’s a science.”)
- soar (“watch your engagement soar”)
- savvy
- transformative
- buzzword
- integrity
- game-changer
- forget (“forget vanity metrics”)
- evolve
- unlock
- unleash
- elevate
- delve
- in a world of
- I used to… then I…
- unleashed the power of
- thank you for it (“your audience will thank you for it”)
- It not about {thing}… it’s about {something different}
- your truth (“share your truth”)
- ethical (or any other statements that are overly concerned with ethics)

Cutting and stitching

I’m never 100% satisfied with what AI produces. And you shouldn’t be either.

When editing, I’m always going to catch some words and expressions I don’t like and change them.

Something I’m also doing is generating multiple outputs either from different content examples or from different LLMs and stitch together the best parts.

Remember: AI content isn’t bad—it’s a skill issue on your side.

I 100% prefer to spend 2 hours editing rather than 1 hour on a draft and 1 hour editing.

I started to use n8n because people around me were recommending it.

But it’s geared towards developers. n8n felt pretty easy to start with as a (former) dev. But not the easiest I would have thought.

The tweet above made me realized that people around me are devs so they don’t really mind the way n8n is structured.

But are other workflow tools better?

I decided to try some alternatives with 3 questions for each one:

• Do I like it?

• Am I more productive with it?

• Can I still easily share my workflows with my audience?

TL;DR

• Zapier has the best UX but it’s too expensive

• n8n workflows fit the mental model of technical people the best but has some quirks

• Lindy has built-in AI so you don’t need to bring your own API keys but that means extra cost

• Make is probably the right middle ground between Zapier and n8n

Zapier

I like the UX the most out of all the other tools, but too many things are rubbing me the wrong way to consider it.

• No rich text in Tables, but with markdown that’s kinda ok, just not the best for marketers.

• A lot of features are available in free tiers in alternatives but not in Zapier

  • Just 2-step Zaps

  • No linked records in Tables

  • Access to some actions are restricted

• I like the UX though. Setting up actions felt easier and quicker than Make and n8n.

• Iterating over items wasn’t easy before, they’ve added the feature now but I wonder if it’s that well integrated compared to n8n and Make.

• It’s expensive: Zapier’s Pro is $29 for Zaps, $20 for Tables add-on, $20 for Interfaces add-on, $20 for Chatbots add-on. For the Tables, I get more features for free with Airtable. For the workflows, I pay less for n8n and Make is even lower. Make and n8n have unlimited members, Zapier’s Pro is single user.

Lindy

It has potential but it’s too much for “non-tech savvy people” for me. Instead of bringing your own API keys for AI actions, Lindy handles everything. But that means extra cost (the first paid plan is $49/mo).

• You can still use HTTP requests to use your own API keys for OpenAI, Claude, etc… But Lindy doesn’t have any value compared to the other tools in that configuration.

• It’s possible to share the workflows as template, it might make it the easiest option to share my workflows without the need to set up other tools for AI calls. But again it comes at an extra cost.

Make

I really liked Make. Will use it for a couple of workflows in the next few weeks and probably move everything to it if I continue to like it.

• I rebuilt one of my workflows pretty quickly without knowing anything about the platform.

• The iteration over items is linear which for my developer brain is a bit weird but probably simpler for non-tech people: one action to start the iteration, then some actions, then an aggregator to close the iteration.

• I don’t like the UI as much as Zapier or n8n. But the UX is pretty good.

• The free version is good enough that people can try my workflows for free and pay for the subscription once they’ve gotten value from my workflows.

n8n

Then there’s n8n that I’ve been using the longest.

I find n8n easier to inject a little bit of code here and there to transform data without adding a dedicated action. Which simplify complex workflows.

Looping over items is also more logical with n8n in my developer brain. Contrary to Make, it’s just a clear and visible loop.

But there are also some small quirks.

For some reason, Airtable triggers don’t have select lists to choose the database or table. But other Airtable actions do. Don’t know why. I’m sure it’s not unique to Airtable and I’ll find more things like that in the future.

Also, at first I didn’t find the Apify actions (that I use for scraping) but that’s because it’s a community node and I needed to enable them manually.

What I’m using n8n for? July edition

I have just a few worfklows made with n8n — I talked about them last time — but I’ve been able to use it for a lot of things. Especially my content repurposing workflow.

So I thought I’d share the main things I did in July:

• Email sequence updates: I’ve used my workflows to rewrite an onboarding email sequence with it. Put my past sequences, best practice cheatsheet, product’s documentation, and more in context. Pretty satisfied with the result.

• Blog post refreshes: I’ve started updating blog posts to AI SEO best practices for a client, using my workflows in the process. It’s also good for SEO as traffic from Google and Bing are up so far.

• Customer story repurposing: I’m currently using my content repurposer to merge all the customer stories from a client to create new use case assets (e.g. all quotes in all stories about a specific use case).

• Newsletter generation: I’ve now switched to using mostly AI for my newsletter about Japan. I’m researching the topic to find the right angle and good sources, then edit a bit manually at the end. That’s it. I don’t struggle with the publishing schedule now.

If you want to buy my workflows, contact me and I’ll give you the payment link (with a BIG discount).

It comes with the n8n workflow templates, Airtable templates, and I’m currently adding content templates and recipes to use my workflows for some of the use cases I’ve just mentioned and more.

My assistant loves using the workflows!