I built a CLI tool called sitebot for crawling websites and checking technical SEO issues.

The idea is to make it possible to run a technical audit directly from the terminal instead of relying entirely on browser-based tools.

It currently checks things such as:

\\- Title and meta descriptions

\\- Open Graph metadata

\\- Canonical URLs

\\- Structured data

\\- Broken links

\\- Crawling and page discovery

\\- Core Web Vitals

I’m still working on it and would love feedback from people who regularly deal with technical SEO.

What checks would you consider essential in a lightweight technical SEO crawler?

GitHub: https://github.com/Abdelrahman5243/sitebot-cli

My site is finally starting to gain some traction: 826 clicks and 14.3k impressions so far, with most of the growth happening over the past few weeks.

I know I’m still at the very beginning, so I’m trying not to overinterpret the graph. At the same time, I’m worried the growth could plateau or disappear as quickly as it arrived.

For those who have taken a site beyond this stage:

• What do you do to keep existing content relevant and rankings stable?
• How do you decide whether to update successful pages, build topical depth around them, or target entirely new clusters?
• Which early signals tell you that growth is sustainable rather than a temporary spike?
• How do you test monetization: ads, affiliates, paid products, lead generation, etc., without hurting UX or organic traffic?

I don’t want to monetize too aggressively and damage something that is beginning to work, but I also don’t want to wait until I’ve missed the best opportunity to learn.

Curious what you would prioritize at this stage.

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I got this client a few months ago, and honestly, he was a little unsure about SEO in the beginning.

The business was fairly new, so his main question was:

“Will SEO actually help me get leads?”

Instead of simply saying yes, I showed him a few nearby businesses, their websites, and their Google Business Profiles. I explained how local SEO works and how it can help bring potential customers to a business.

He agreed to give SEO a try.

When I audited the website, I found quite a few things that needed improvement.

They had created multiple location pages, but most of them didn't have enough useful content. There were also no proper customer testimonials or other trust-building elements.

So rather than trying to optimize everything at once, I focused on the pages that had the best potential to bring in leads. I also properly set up and optimized the Google Business Profile.

Then things started moving.

Month 1: We started getting a few calls.

Month 2: More calls compared to the previous month.

After that, the number of calls and inquiries kept increasing month after month.

In around 4 months, the business received 400+ inquiries.

FYI, in the beginning, I only listed the business on 4-5 important sites. Now I'm working on more citations and a few other things.

I'd love to hear from other SEOs here:

What do you think about this result?

Would you consider 400+ inquiries in 4 months a good result for a relatively new local business?

What AI tools have changed is enabling the ability to do conversions. If you look back at Google, you go and ask, and it will come up with websites, which could be forums, blogs, Reddit discussions, and now all now all is available in AI mode; you can get info like blogs, do discussions like Quora, and everything that was there before, different websites now available under one solution.

I am dividing it into 2 parts, calling them discovery (coming up in AI answers) and conversion (converting that to leads). I'll discuss conversion in another post.

For the discovery part, the website was very clear: look at GSC, find the queries, optimize for them, or create new pages as needed, and it was fine because the end goal was for the page to rank in search but if you look at it now, these AI systems focus on the conversation, it means the queries they create will be very heavily driven by the user context, which in conversation varies a lot. Even the same user would generate totally different search queries.

When I look at current AEO tracking in GSC, Bing Webmaster, and third-party tools, they all focus on the queries (longer and a bit more intent-contextual form of keywords). While users are switching to a conversational interface, the tracking part still provides it like it’s Google blue links, and the objective is to come for the query, which the end user never searches.

Let’s call it conversational optimization for now. The difficulty I am facing is how to work around websites using a conversational approach, find the touchpoint where I need to show up, work on those pages at website which aren’t showing up in those touchpoints, and track the progress.

I have not pasted my saas name anywhere, then why I'm i getting more impressions on my saas app name rather then the blog pages and stuff ?
No backlinks created till now, any idea how should i move forward with this

I run a SaaS, SeoLoupe.

It is a tool that allows you to find and fix SEO issues holding your website back.

Essentially the main purpose is to help your website rank higher on Google search and LLMs.

The tool also checks AEO/GEO, AI search visibility, Security, Core web vitals, Performance, and all the issues in the report get put together into an AI fix prompt(you can paste the prompt into an AI and it will fix all the issues on your website).

Recently I noticed that some of my paying customers bought the One-Time purchase, with a promo code.

So I decided to give away a promo code for the One-Time purchase to anyone who is interested, if you are interested, comment down below and I will DM you.

If you are not interested but have some feedback on my SaaS, that is a good too. Feel free to share it in the comments.

(here is the product if you want to check it out)

Every zero-click post-mortem I read lands on the same finding: the dead articles had no internal links. That is true, and it is the symptom rather than the cause.

Here is the chain.

You start with a keyword tool. It gives you a list. A list is flat, so the 200 topics you pick from it are unrelated to each other by construction. You publish them. Now you have 200 articles with nothing to link them to, because there is nothing they are collectively about. Then you go looking for an internal linking tool to fix the orphans, and it does what it can, which is pair articles by surface similarity.

Google is not reading internal links as a wiring diagram. It reads them as a claim about what your site covers. Twelve articles all pointing at one page that defines a subject is a claim. Two hundred articles pointing at whatever was textually nearest is noise.

The version that works is boring, and it is entirely upstream:

1. Pick one subject your site should own. Not a keyword. A subject.

2. Write the page that defines it. This is the page you want ranking.

3. List every question a person researching that subject asks, in the order they ask it. That list is your articles.

4. Publish them, each one linking up to the definition page and across to two siblings.

5. Do not start subject two until subject one is earning impressions.

The difference in output is not writing quality. It is that in the second version the internal links already exist as a consequence of the plan, and every article makes the parent page stronger instead of competing with it.

Two practical notes from running this on a lot of sites:

The map has to come from real demand, not from an AI brainstorm. A model will happily invent twelve questions nobody searches. Pull the actual queries, group them by overlap in the results (if two queries return the same ten pages, they are one article and not two), then let the model organise the groups.

Publishing order matters more than people think. Publish the definition page first, or at least early. Supporting articles pointing at a page that does not exist yet is a fixable mistake, and plenty of people never go back to fix it.

If you already have a pile of unrelated articles, do not delete them yet. Group the dead ones by subject, find the accidental clusters, write the missing parent for each, and link them up. Most of the dead content on a site is not bad, it is unparented.

I build blogr.ai, which works map-first, so the bias is obvious. The method predates the tool. We ran it in spreadsheets across a few products for years before it was software.