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!