We Ran an AI Hackathon for Our Content material Group. Right here’s What We Constructed with Agent A

We constructed three instruments that chip away on the “what ought to we write” downside from completely different angles.

The most important is Mateusz’s Key phrase Analysis Hub, a four-tab workflow over Ahrefs knowledge:

• Discovery pulls seed-and-related key phrases with branded/NSFW filters.
• Content material Hole finds competitor key phrases we don’t rank for.
• Breakout finds weblog key phrases rating 31 to 100 that don’t have a devoted web page but.
• Grasp Listing dedupes every part and labels it by cluster and tier.

The intelligent bit is the tier system: every candidate will get a cosine distance out of your subject clusters, then lower by percentile into Tier 1 (core orbit) by way of Tier 4 (most likely noise). You cease arguing about whether or not one thing is “on-topic” as a result of the maths simply tells you.

Louise’s Trending Key phrases is the day by day model: takes her seed subjects, queries Ahrefs day by day, and surfaces what’s new, what’s rising 3m/6m/12m, and whether or not we already rank. The “spot it earlier than everybody else does” software.

My Entity Hole Finder comes at it from a special angle. It scrapes our total weblog for entities and phrases we point out typically, checks if we’ve a devoted web page for every, and exhibits the place we rank.

I constructed it as a result of I stored noticing we’d reference an idea fifty occasions throughout the weblog with out ever writing the publish that ought to rank for it. Plumbed into the pipeline, it ought to generate these posts routinely.

Mateusz and Louise each constructed Reddit listeners. Independently. On the identical day. That most likely tells you every part about how a lot we needed one.

Each variations scan r/Web optimization, r/bigseo, and r/SEO_LLM for AI-search discussions (GEO, AEO, AI Overviews, Perplexity, ChatGPT search), flag the “sizzling” posts the algorithm is surfacing, and roll the week up right into a Monday report: themes, ache factors, rising tendencies, weblog concepts. Mateusz calls it “RSS on steroids”, which is the perfect description.

We additionally constructed two adjoining radars.

My Search Advertising and marketing Information Aggregator grabs the final seven days of search-and-marketing information (constructed for our e-newsletter, now utilized by anybody scanning what occurred this week).

And Mateusz’s Web optimization Experiment Tracker enables you to arrange an experiment with a URL and speculation (“including FAQ schema will improve AI Overview citations”), snapshot baseline site visitors and rankings from Ahrefs, take periodic snapshots, and on the finish hit Assess for an LLM verdict: Labored, Didn’t Work, Inconclusive, or Too Early.

Cease counting on “I believe this labored” and have the receipts.

While Louise built her own Editorial pipeline: brief → outline → draft → edit → polish → verify → publish, with scrapbook context fed into every stage.

Each stage’s output is editable before moving on, and after it finishes there’s a Refine mode, a chat loop where Louise can ask for changes (“tighten the intro”, “swap this example”) and adopt or revert each one individually.

My Data Refresh automates the surprisingly painful quarterly chore of updating our data-driven posts (top Google searches, top Google questions, and so on). It pulls fresh data, filters it, and hands me TablePress-ready output.

My Press Release Generator turns a blog URL or product-feature note into a press release; goal is to plug it into our data-studies category so every new study auto-generates one.

Louise’s WP Processor takes a finished draft and returns WordPress-ready HTML with internal links and formatting handled.

None of these are sexy. All of them claw back hours.

The thing that quietly impressed me most isn’t a tool.

It’s the pattern Mateusz wired through Scrapbook, Notes, and Source of Truth: every repo has an index.json that auto-updates whenever a file is created, edited, or deleted.

From that index, a lightweight reference file gets regenerated, a plain-text summary the agent reads at the start of any conversation. The agent knows what exists without fetching anything, and only pulls full content when it actually needs it.

As Mateusz stated:

“This software expands what feels doable, and it’s addictive. You retain occupied with what else you might construct, even past Web optimization.”

This was how Mateusz ended up with instruments like Scrapbook, his very personal inspirations clipping software. Paste any URL or uncooked textual content, and Agent A will learn it and generate a structured be aware with a abstract, key bullet factors, particular claims, knowledge factors, and three article concepts impressed by the content material.

It’s in a roundabout way Web optimization-related nevertheless it’s a base for him to draft his subsequent thought management piece.

That’s what “use AI extra” can’t seize. Utilizing ChatGPT seems like asking a wise good friend for a favour. Constructing a software seems like hiring one. When you’ve employed one and watched it work, you begin trying round your week for the following factor at hand off.

Not one of the standout initiatives requested anybody to invent a brand new workflow from scratch.

• We have been already saving LinkedIn posts; SavedIn made the saves usable.
• We have been already gathering URLs; Scrapbook gave them construction.
• We have been already lurking on Reddit; the listener turned the lurking right into a weekly report.
• We have been already refreshing knowledge posts each quarter; Information Refresh simply made the refresh take an hour as a substitute of a day.

Don’t construct a software that requires a brand new behavior. Construct the one which makes an current behavior sooner.

The large unlock wasn’t “AI can write.” Everybody is aware of that.

It was that the agent might pull up the proper details, like previous drafts, saved analysis, our inside type information, what we already rank for, with out us pasting them in each time.

Instruments like Supply of Fact, Scrapbook, SavedIn, Notes, the GitHub-backed indexes, Louise’s writing-sample library, the editorial-style ability, none of those generate content material. They seize, organise, and retrieve context.

The drafts that come out of pipelines hooked into them are markedly higher than drafts from pipelines that aren’t. When you’re selecting one factor to repeat from this hackathon, copy the reminiscence layer first. The writing instruments enhance themselves as soon as the reminiscence exists.

Louise had already prototyped items of her workflow on Lovable, and was bracing for a painful rebuild. She acquired the other:

“It’s very simple to maneuver a venture from one other platform like Lovable and rebuild it in Agent A. Simply export the code and Agent A immediately rebuilds it.”

So should you’ve already began constructing some place else, you don’t lose the work. You simply plug it in subsequent to Ahrefs knowledge.

That is the one which quietly kills most “innovation weeks.” Don’t ask individuals to construct “alongside” their regular work. They’ll default to the traditional work.

Ryan cleared our week the Friday earlier than: no posts, no edits, no conferences outdoors the hackathon, OOO replies on Slack. When you genuinely can’t spare 5 days, do three. Don’t do one.

I’ll be sincere: We didn’t do that for our hackathon. However I did this for myself personally and located it useful.

As a result of the listing of what you might construct is infinite. Between that and “use AI extra”, you might be caught in a panic and find yourself doing nothing. So, having an inventory of frustrations made tackling the hackathon simpler.

So, you’d wish to listing down the issues in your job that you just preserve doing manually that you just want you didn’t must. That’s how I got here up with my Information Refresh software. It was as a result of one thing that appeared so easy on paper took me surprisingly lengthy to do.

Two guidelines:

• Be particular. Not “analysis”, however “I spend two hours each Monday going by way of my LinkedIn saves and pasting the great ones right into a doc.”
• Be sincere. Boring chores rely. Essentially the most-used instruments we constructed got here from chores, not from anybody’s intelligent AI thought.

These lists are the briefs. The extra particular the frustration, the higher the software.

Why does this interview step matter? Right here’s what Louise stated:

“It’s simple to get caught in immediate loops bettering the UI of your app, and making fixed incremental enhancements, quite than ensuring the app achieves its overarching purpose. This results in quite a lot of token waste. As a substitute it helps to plan what you need beforehand and spend time speaking/being interviewed by the Agent earlier than you begin constructing.”

Once more, full honesty: I didn’t do that myself. Nevertheless it’s such a fantastic thought. The following time we run a hackathon, and even simply me constructing one thing for myself, I’m going to do this.

You need to too.

Everybody exhibits what they constructed, why, and the way it works.

The demos are the place the cross-pollination occurs, the place somebody realises their software could be 10x higher with the information one other teammate’s software produces, and the place the following week’s work plans itself.