DEV Community: Rákóczi Piroska

SEO's substitute is not GEO

Rákóczi Piroska — Fri, 25 Sep 2026 14:23:42 +0000

The right marketing strategy isn't simply about achieving AI visibility, but rather about creating the most effective advertising mix possible. Why?

Achieving AI visibility requires a tremendous amount of work, and even if your site is cited in the answer to a query, the likelihood of the user clicking through to your page is very low.

Although tools for measuring AI visibility are being developed—with solutions like QuoteMarker already available—numerous studies indicate that click-through rates for AI-cited content are lower than those historically seen with Google Ads.

Here is, for example, the SISTRIX research, in which the situation in Germany was researched. They came to this result:
"AI Overviews are displayed for around 20% of keywords in Germany
The CTR at position 1 drops from 27% to 11%, a loss of almost 60%
AI Overviews cost 265 million organic clicks per month in Germany
The average click loss across all keywords stands at 6.6%
The impact varies greatly by industry: from 1% to over 24% click loss
Biggest absolute loser in Germany: Wikipedia with 31.6 million clicks per month
Biggest proportional losers in Germany: Specialized health portals with over 30%"

How have search habits changed?

I have a system administrator friend who used to have a negative opinion of AI. Then, one day, he admitted that he had grown to love AI-generated summaries because they instantly tell him what a component does, saving him from having to sift through foreign-language search results to find the right one.

I also have a relative who is a medical researcher; initially, while he acknowledged the convenience of an AI search tool instantly retrieving the specific professional article that used to require a lengthy search, he actually enjoyed the process of searching itself. A year later, however, he was enthusiastically explaining how much faster and easier the AI search tool made his research work.

The bottom line is this: people have become accustomed to using AI because it allows them to gain deeper knowledge more quickly.

So why is the CTR lower?

Because, in most cases, the summaries provide users with sufficient answers.
It follows that they do not search any further—unless...

What will take the place of GEO?

GEO is a trendy practice these days, but it is becoming increasingly futile. There are two reasons for this:

1. AI search summaries are getting better and evolving nicely.
2. The links do not draw attention to themselves; they are positioned so discreetly that they escape notice.

How can you capture attention through AI search results?

Only through advertising. Since this field is still in its infancy, it is difficult to find reliable data comparing the click-through rates (CTR) of ads placed in ChatGPT or the new Google Ads against the click-through rates for content linked within search engine responses; however, one thing is certain: visual ads always attract more attention than text-based ones.

So, why is it worth switching to an advertising mix?

When advertising is the chosen strategy, it is worth investigating whether advertising on previous online platforms might be more effective. Every company must find the answer to this question for itself. Although industry-wide statistics exist, they may not yield the same results for your specific product as the broad averages found in such research.

If you have been active online for some time, you likely have statistics that you can analyze to determine the most effective advertising mix. However, if you are working with a limited budget, you have a great opportunity.

How to advertise if the budget is small?

How do you advertise on a small budget? An old method is set to make a comeback. You need to create something for social media that spreads virally—a very difficult task. Advertising agencies aren't the right fit for this; it requires an idea and a moment of inspiration. It follows, then, that the most realistic approach is to factor advertising costs into your pricing strategy right from the start. Whether this will drive up the prices of digital products and services is a topic for another a post.

What Do GEO and Linguistics Have in Common? Mapping Every Question a Blog Post Hasn't Answered Yet

Rákóczi Piroska — Thu, 10 Sep 2026 10:51:12 +0000

A note on what this is: this article describes an independent, self-run experiment, not a peer-reviewed study. The linguistic and marketing claims that come from published research are cited below. The claims about the question matrix, the categorization results, and the saturation curve are my own findings, based on a dataset and pipeline I built myself. I've tried to flag clearly, section by section, which is which, and what a reader would need to reproduce each part.

Why This Started: Predicting the Next Logical Question
This project didn't start as a linguistics side-quest. It started because I wanted to build a tool that predicts the next logical question on any given topic — the specific question a reader would ask next that a topic's existing content hasn't answered yet. Some topics are already thoroughly covered; the interesting ones, for content strategy, are the ones with an obvious next question still sitting open.
Before building anything, I wanted to check whether this was actually worth pursuing as a GEO tactic, so I asked Google's AI Mode directly.
Me: If I have a blog on a topic, and I guess what people will ask next, and I answer it with actual numbers — what are the odds Google AI quotes me when someone asks that question?
Google AI Mode: You've got good instincts here. Giving a precise, numerically-backed answer to a question nobody else has answered yet is, right now, the single most valuable SEO move you can make.
That answer is what turned this from an idle idea into a project worth building. I want to be clear about what this exchange is and isn't: it's anecdotal, not evidence in itself — a single conversation with an AI system isn't a citable data point. But it matches the direction of the peer-reviewed GEO literature (below), and it's the reason I went looking for a systematic way to find a topic's unanswered questions, rather than treating the idea as settled.

Why This Matters for GEO?
The 2024 paper that coined the term "Generative Engine Optimization," by Aggarwal and colleagues at Princeton and IIT Delhi, showed that content style, structure, and — importantly — the presence of statistics and evidence can measurably change whether a generative engine cites a page in its answer (1). Later work on how generative engines select and absorb citations has continued to find that specific, well-evidenced answers tend to outperform generic ones. That published research is the independent backing for the instinct Google's AI Mode gave me above.
So the practical recipe I set out to build tooling for is:

Take the most recent articles on a topic.
Identify every possible Wh-question those articles touch on.
Check which of those questions the articles already answer.
Answer the ones that are still open, with specific numbers where possible. Step 2 is the hard one. LLMs can already do a version of this, so the natural question is: why not just ask one?

The Meta-Question Problem
To predict the next logical question for an arbitrary topic, I first had to solve a narrower problem underneath it — one I started calling the meta-question problem while I was working on it.
A concrete question like "How can I repair the wheel of a bus?" is really two things layered together: a topic (bus) and an abstract question shape (fix / how / indicative) that has nothing to do with buses at all. That same shape — fix / how / indicative — also generates "How can I repair a leaking faucet?" for plumbing, or "How can I repair a cracked phone screen?" for electronics. The shape is the meta-question; the topic is just a slot you fill in afterward.
That decoupling is the whole point. If the set of possible meta-questions is finite and can be enumerated in advance — independent of any specific topic — then generating candidate questions for a new topic stops being a creative or predictive task. It becomes mechanical: take the finite list of meta-questions, and for each one, slot in the topic. No guessing, and no risk of missing a category of question, provided the meta-question list itself is complete. The hard problem shifts entirely to building and validating that list — which is what the rest of this article is about.

Why Not Just Ask an LLM to Find the Missing Questions?
This is also the direct answer to a question people usually ask first: LLMs can already suggest "what someone might ask next" — so why not just use one instead of building a matrix?
● Once the meta-question problem is solved, question generation no longer needs a model that predicts likely questions at all — it needs one that enumerates possible ones. Those are different tasks. An LLM asked "what might someone ask next about buses?" is doing prediction: it's drawing on patterns of what people tend to ask, which leans toward what's emotionally salient — curiosity, worry, reassurance-seeking — rather than toward logical coverage of the topic's structure. A finite, pre-built matrix of meta-questions doesn't predict; it enumerates every fix/how/indicative-style shape and fills in "bus" mechanically. That's what makes it possible to check a topic for gaps systematically, rather than getting a plausible-sounding but incomplete list back from a model each time.
● It also meant I could build a lightweight, low-cost first version. Because slot-filling a known meta-question into a topic is mechanical, Python's NLP tooling was enough for an MVP — with one trade-off: the MVP only works on English text.
Both of these design choices are mine, not settled facts — a different implementation could route slot-filling through an LLM too, and the argument above doesn't depend on never using one. The point is narrower: enumerating which questions are possible for a topic doesn't require one, once the meta-question list is fixed.

Building the Meta-Question Matrix: The Linguistic Core
The starting idea, then: if I could enumerate every meta-question, filling in an actual topic afterward would be comparatively easy, as shown above. The question was how to enumerate the shapes.
Two things are true of every Wh-question in English: it contains a question word (what, why, where, who, when, how, which, whom, whose), and it contains a verb. Crossing those two dimensions gives a matrix — but a matrix built from individual verbs would be enormous. It only becomes usable if you build it from verb categories broad enough that any specific verb slots into one of them.
This is where I leaned on a rule of thumb familiar from primary-school grammar: verbs broadly do, be, or happen. I treated this as a first-pass, three-way top-level split and worked down into subcategories: do-type verbs such as make, execute, fix, move; be-type verbs such as define, qualify, compare; and happen. (This first-pass list turned out to be incomplete — see the note in the next section on the gap I found once I started categorizing real questions.) I want to flag directly what this is and isn't: it's a simplified, working taxonomy I built for this project, not a citation from the linguistics literature. The two closest published frameworks are Vendler's four aspectual verb classes — states, activities, accomplishments, and achievements, distinguished by how a verb behaves in time — and Levin's much larger classification of English verbs by their syntactic alternation patterns (2, 3). Neither maps cleanly onto a do/be/happen split; they're organized around aspect and syntax, not around the kind of coarse semantic grouping I needed for a question matrix. I'm citing them here because they're the established reference points for "verbs can be systematically categorized" — not because my three-way split is drawn from either of them.
Crossing the do/be/happen categories with the nine Wh-question words gives the matrix below (shown in simplified form — the do and be rows expand further into their subcategories):

The table above shows the matrix in its corrected, near-final form, including "have" as a do-type subcategory. As described in the next section, "have" wasn't in my original list — it was added only after real data exposed the gap.

Verbs have a third relevant dimension: mood — indicative, conditional, imperative. Adding that as a third axis turns the matrix into a 3D grid of question types. Which raises the real question behind this whole exercise: does every real-world question people actually ask fall somewhere inside this grid? And does the grid produce false positives — cells that don't correspond to anything a real person would ask? Showing that the matrix captures real questions, without gaps or obvious dead cells, is what the rest of this article is about.

Testing the Matrix: Building a Real-Question Dataset
What I tested: whether real, naturally occurring English questions consistently fall inside the 3D matrix described above, or whether people ask things the matrix can't represent.
To test that, I needed a large set of real questions with real answers — not questions I invented to fit my own framework. I built a small Python and Streamlit tool that used the Exa.com search API to find and scrape FAQ-style question-and-answer content from American websites. That process produced a dataset of roughly 17,400 question-answer pairs.
What a reader can verify: the Exa API is a public, commercially available search product, and the method — searching for FAQ pages on a topic and extracting Q&A pairs — is straightforward to reproduce with the same or a comparable search API. I haven't published the raw dataset or the collection script alongside this article; if there's interest, I'm glad to share more detail on the collection pipeline in a follow-up. Readers should treat the 17,400-pair figure as a description of my own dataset, not as a benchmark anyone else can currently pull down and re-run against.
It's also worth being upfront about the dataset's limits: it draws only on English-language FAQ content from U.S. sites surfaced by one search API, so it's skewed toward however Exa's index and ranking favor certain topics and site types. A different search API, a different country's web, or non-FAQ question sources (forums, support tickets, search-query logs) could turn up question shapes this dataset doesn't contain. I don't think that undermines the core test — the matrix still needs to explain whatever real questions you throw at it — but it does mean "17,400 questions" describes one particular slice of the English web, not English questions in general.

Categorizing the Questions and Testing for Saturation
What I tested: whether the do/be/happen × Wh-word × mood matrix is comprehensive enough to hold a large, independently collected set of real questions — that is, whether new question categories kept appearing as I added more data, or whether the categories stabilized.
To categorize the 17,400 pairs, I wrote a second Python script that used the Claude API to assign each question to a cell in the matrix. Before automating the full run, I categorized the first 100 real questions by hand as a sanity check on the matrix itself — and that's where I found a gap. A cluster of questions built around "have" — things like "What documents do I have to bring to a car inspection?" — didn't fit anywhere. My original do-type list (make, execute, fix, move) simply didn't include a "have" bucket. I added "have" as its own do-type subcategory and re-ran the check before moving on to the full dataset.
I'm including this because it's a concrete, checkable example of the framework being corrected by real data, rather than the reverse. It's also a useful caveat for the saturation analysis below: it means the matrix that reached saturation on 800–900 questions is the corrected one, not my first draft — the first-100 gap is exactly the kind of thing a larger, unexamined sample could still be hiding elsewhere in the do or be branches.
I want to flag a further methodological limitation here directly: the categorization was done by a single LLM pass, and I have not yet run a human-coded validation sample to check inter-rater agreement against the model's labels. That's a standard check in qualitative coding work, and it's on my list for a follow-up — until it's done, the categorization results below should be read as a strong first pass rather than a validated coding scheme.
With the categorized data in hand, I ran a saturation analysis. The concept comes from qualitative research methodology: theoretical saturation is the point in a coding process where reviewing more data stops producing new categories (4). The most-cited empirical benchmark for what saturation looks like in practice is Guest, Bunce, and Johnson's study of 60 interviews, which found that the great majority of thematic codes appeared within the first dozen or so interviews, after which new codes became rare (5). I'm borrowing that logic here and applying it to question categories instead of interview themes: if, after a pilot sample of 800–900 questions, every cell in the matrix already contains at least one real question and no new categories are appearing, that's evidence the matrix is comprehensive enough to be trusted on the rest of the dataset.
That's what the pilot sample showed: once the sample passed roughly 800–900 questions, the rate of new-category discovery flattened, and no cells remained empty. The chart below is my own plot of that curve — new categories discovered on the y-axis, cumulative questions reviewed on the x-axis.

One caveat worth restating: saturation on a pilot sample from this dataset shows the matrix is comprehensive for this collection of American FAQ questions. It's a reasonable basis for expecting the rest of the 17,400 pairs to fit the same matrix — that's the standard inference saturation analysis licenses — but it isn't a proof that no English question could ever fall outside the grid, and it hasn't been tested yet against non-FAQ question sources or non-U.S. English.

What This Actually Shows?
Taken together, the test supports two claims, at two different confidence levels:
● Empirical claim: for the dataset tested, real Wh-questions consistently mapped onto cells in the do/be/happen × Wh-word × mood matrix, and the categorization stabilized well before the full dataset was reviewed. This is the part backed directly by the experiment above, with the caveats already noted (single-pass LLM coding, one search API, U.S. English FAQ content).
● Working hypothesis: that a comprehensive Wh-question matrix, applied to any piece of English text, can be used to enumerate every logically possible question that text hasn't yet answered — and that answering those gaps with specific numbers is a viable GEO tactic. This follows from combining the matrix result above with the published GEO research cited earlier; it's the connective argument of the article rather than something the experiment tested directly.
It leaves one problem unsolved. A real topic can generate dozens of theoretically valid unanswered questions — for example, for a used-car dealership, the where/move/indicative cell might expand into "Where can you take the car for a test drive?" With 50 or more valid candidates, which question should actually be asked, and answered, next?

What's Next: Picking the Question That Matters Most?
That's the problem I'm working on now — ranking the theoretically valid questions a matrix like this generates, so that the next piece of content answers the one question most likely to matter. That's a separate build, and a separate post. Let me know if it's something you'd want to read.

References

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), 5–16. https://doi.org/10.1145/3637528.3671900
Vendler, Z. (1957). Verbs and times. The Philosophical Review, 66(2), 143–160.
Levin, B. (1993). English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press.
Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Publishing Co.
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82.

Has "Measure Twice, Cut Once" Just Died in Game Design?

Rákóczi Piroska — Fri, 12 Jun 2026 17:51:50 +0000

Every carpenter knows the rule: measure twice, cut once. Caution before commitment, because mistakes are expensive. For decades, building games worked the same way—you planned carefully before writing a line of code, because writing it was slow.
That rule is quietly dying. And honestly? Good riddance.
Is there math behind all games?
Here's the thing nobody tells you: every game is secretly math. Underneath the pieces and the points sits a system of rules, and the whole craft is finding rules that resist being solved. You want a game where learning one clever strategy doesn't hand you an endless, boring streak of wins. Tic-tac-toe fails this test—two careful players draw forever. Even small chase games like rabbit-and-hounds are completely "solved." Chess survives only because its branching paths explode faster than any single recipe can keep up.
What is the real difference between chess and tic-tac-toe?
So the real question for a designer is: did I just build another tic-tac-toe, or something with the depth of chess?
Now the workflow flips. You don't measure endlessly. You scribble down a rule—say, a board where capturing a piece gives your opponent a new move—hand it to Claude, and it's playable in minutes. You poke at it. Within an hour you feel whether it's shallow or bottomless.
Then comes my favourite part: the conversation. You ask Claude, point blank, is this provably unlearnable? You talk through state space, symmetry, hidden information. You brainstorm what's missing—maybe a dash of randomness, a bigger board, fog-of-war secrecy.
What is the best way to find the math behind a game?
And here's what's easy to forget: playing the game was always part of building it. Designers have forever tested by hand, shuffling pieces around a kitchen table to feel where a rule went wrong. That instinct hasn't changed—it's just suddenly within reach. You can spin up a dozen variants and discover the strategies and tactics yourself, mid-game, the way a player would. And that beats grinding through equations on paper every time.
Measuring becomes iterating. Cutting is nearly free. Design stops being a lonely calculation and turns into a dialogue between intuition and proof. The whole process didn't just get faster—it got genuinely fun.

How to Build a Tool That Actually Generates Revenue

Rákóczi Piroska — Fri, 12 Jun 2026 17:44:20 +0000

Most founders fail for one simple reason: they build before validating. A polished product means nothing if nobody truly needs it. The real opportunity is not in building faster — it is in solving painful problems better than existing alternatives.

Sep No. 1

The first step is identifying a problem that affects a large number of people. Not a “nice-to-have” inconvenience, but a recurring frustration that users actively complain about. The stronger the pain point, the easier it becomes to sell the solution. Platforms like MonetScope are especially useful because they aggregate real discussions from Reddit, X, and Hacker News to uncover validated startup opportunities backed by actual user frustration. (MonetScope)

Step No. 2

The second step is measuring the severity of the pain. Many ideas sound good in theory but fail because the urgency is weak. If users are already paying for workarounds, complaining publicly, or switching between competitors, that is a strong market signal. Modern validation platforms increasingly focus on analyzing willingness-to-pay, urgency, and competitor weakness because these factors predict commercial viability far better than hype alone.

Step No. 3

Next, study the competition carefully. Find out how long competitors have existed, what pricing models they use, and whether customers are satisfied. If companies have survived for years, the market is proven. Your job is not to reinvent the category — it is to deliver more value, faster, cheaper, or with a better user experience.

The near future or step No. 4

Finally, calculate your potential profitability. Ask a simple question: how much more value can you provide at a lower cost? This is where most successful SaaS businesses separate themselves from failed experiments. A clear value-to-price advantage creates momentum, especially in crowded markets. I am currently developing a calculator specifically designed to measure this gap and help founders estimate whether their idea has real commercial potential before investing months into development.

The old school of startup says

Traditional startup schools often teach a brutally simple philosophy: launch the product as fast as possible, put it in front of the market, and if people pay for it, continue building — if they do not, move on. While this approach sounds efficient, the strongest critics of it are usually founders who already experienced rejection from the market firsthand. After spending months building products nobody wanted, many eventually realize that proper idea validation could have saved them enormous amounts of time, money, and energy. The market will always validate the truth eventually, but discovering that truth before development begins is often the difference between building a business and wasting a year chasing the wrong idea.

How to Build a Tool That Actually Generates Revenue

Rákóczi Piroska — Sat, 16 May 2026 11:07:23 +0000

Sep No. 1

The first step is identifying a problem that affects a large number of people. Not a “nice-to-have” inconvenience, but a recurring frustration that users actively complain about. The stronger the pain point, the easier it becomes to sell the solution. Platforms like MonetScope are especially useful because they aggregate real discussions from Reddit, X, and Hacker News to uncover validated startup opportunities backed by actual user frustration. (MonetScope)

Step No. 2

The second step is measuring the severity of the pain. Many ideas sound good in theory but fail because the urgency is weak. If users are already paying for workarounds, complaining publicly, or switching between competitors, that is a strong market signal. Modern validation platforms increasingly focus on analyzing willingness-to-pay, urgency, and competitor weakness because these factors predict commercial viability far better than hype alone.

Step No. 3

Next, study the competition carefully. Find out how long competitors have existed, what pricing models they use, and whether customers are satisfied. If companies have survived for years, the market is proven. Your job is not to reinvent the category — it is to deliver more value, faster, cheaper, or with a better user experience.

The near future or step No. 4

Finally, calculate your potential profitability. Ask a simple question: how much more value can you provide at a lower cost? This is where most successful SaaS businesses separate themselves from failed experiments. A clear value-to-price advantage creates momentum, especially in crowded markets.I am currently developing a calculator specifically designed to measure this gap and help founders estimate whether their idea has real commercial potential before investing months into development.

The old school of startup says

The Real Problem With vibe-Coding or Why Faster is slower?

Rákóczi Piroska — Fri, 10 Apr 2026 12:19:34 +0000

If you expect security and efficiency with an AI coder like Replit, you need to follow strict workflow rules. I'll just briefly talk about them. This way of working doesn't take away from the experience that Replit can provide, that you can code quickly something that your intern can code slowly, but if you also write prompts to prevent and/or fix Replit's errors - well, that makes the faster one slower. Why?

You will need at least 6 prompts

These prompts are the followings. These were useful in a certain case. Making them useful you should apply to your project.

A security guard prompt
“Help me implement [TASK] in Python, following strict security rules:

Use only well-known, actively maintained libraries.
Avoid newly published or obscure packages.
Pin all dependency versions explicitly (package==x.y.z).
Never include API keys or secrets in code. Use environment variables.
Before finalizing, simulate a pip-audit and flag known vulnerabilities.
Validate all user inputs against injection or malicious data.”

A transparency prompt
"Modify popup.css [your request]. Strict condition: List each file you plan to modify before you start coding! If you need to touch popup.js or manifest.json in addition to CSS, explain exactly why. Do not make 'silent' changes to files that you did not mention in your answer!"

A diff summary prompt
"I see that the content of the popup.js file has also changed, even though it was only the CSS. Generate me a complete list of all the files you modified in the last round! Write a sentence next to each file explaining exactly what you changed and why. In the future, start all your answers with a 'Change Log' like this before you update any code!"

A lockdown prompt
"We are currently working exclusively with the popup.css file. I am disabling access to edit the popup.js and manifest.json files for this round. If my request cannot be fulfilled without modifying the JS, then do not write code, but report the technical obstacle! Work only in the file I have designated and do not perform background work on other modules!"

A structural inspector prompt
"My goal is to update the look of the Chrome extension in popup.css. Don't generate new HTML elements from Javascript, and don't change event listeners. If you feel like you need to change popup.js for a design element, stop and ask me before you rewrite anything! Just work with the existing CSS selectors for now."

A code quality enforcement prompt
"Act like a Senior Full-stack Developer. Take a critical look at the current state of [filename]. It seems like we've produced 'spaghetti code' while fixing the bug. Please don't just fix the bug, but refactor the code: make it modular, clean, and follow [language/framework, e.g. React] best practices. Pay special attention to type safety and error handling to avoid similar regression bugs."

How could this be made faster?

At first it will be even slower, because you will have to save all the prompts you have written. It would be best if you had a Chrome extension that would save the prompts with a single click after you type them. The extension would organize the prompts into an FAQ-like system. Then you would just have to access the Chrome extension, find the prompt and CTR C, CRT V. Are you interested in such an extension? Write it in the comments, please.

How to Find SME Customers of Your AI Product?

Rákóczi Piroska — Sun, 29 Mar 2026 12:29:49 +0000

You program what you feel like doing. That's OK. But believe me, it won't be 3 months before you feel like you've put in so much effort that you should be getting paid for it. But who's going to buy that? And this is where most tech people hit a dead end. And yet...

There is a way out of the dead end
First of all, you should know that most AI tools do not save enough working time and that is why SMEs do not buy them. It is worth developing AI stuff that does not speed up work, but gives humanity new tools (no exaggeration!) that they can only get from this new technology. For example, an AI tool for the blind that reads colors from an image. This could be an aid for a packaging worker. In other words, those people can develop useful AI tools who can dream up AI tools like writers dreamed up television back in the day. (Just a parenthetical comment. Maybe it is worth reading science fiction novels with this kind of eye.) However, this is a very rare case. This is not the best way out of the impasse. The best way out is to rephrase your question.

Who is the SME leader who would buy an AI tool?
The ideal customer for AI tools is the SME executive coming from a multinational company. Why? Someone coming from a multinational faces two things in a medium-sized company that hurt them:

Data vacuum: They are used to getting market analysis, competition monitoring and legal due diligence at the click of a button at the multinational. They are groping in the dark at the SME.
Structural chaos: They see 10 people doing work that at the multinational was solved by well-configured software and 2 operators. For them, AI is not a "miracle weapon", but an infrastructure supplement.

But what tools would he or she actually buy?
Which is a painkiller for them. So, they respond to the following problems by spending money:

Decision paralysis: They have no data on market trends -> They buy the AI-based predictive analytics.
Scalability barrier: They would like to hire 20 more people, but they can't find them -> They buy the AI, which will double the efficiency of the existing 20 people.
Audit fear: They are afraid that the colleges will cheat during an audit -> They buy the automated compliance/legal monitor.

How big is the market?
This sounds good so far, but how big is this market?
The statistics: About 18-22% of mid-market (100-500 employees) leaders will come from large-enterprise (Fortune 500 or Big Tech) backgrounds in 2025-26.
Why is it happening? Due to layoffs at large tech companies and burnout of "gray eminences", many senior leaders (VP, Director level) decide that they want to be a "big fish" at a smaller company, where they have real influence on processes.
The window: The first 90-180 days of a new leader is when they want to "set things right". This is your sales window.

A service can help you
You may think now that you can find these SME leaders. I can tell you: you needn’t. There is a person who is good at sales and programming who could bring you 2-3 such leads per day. The hourly rate for a senior developer (you) in 2026 should be $80. If you write the codes of a tool finding these leads yourself and maintain the necessary bots, and you clean the data, that's at least 10-20 hours per month. That's $800 - $1600 for you.
If you give it to service provider for $150/month, you are in pure profit. Would you give it a try…

Content Marketing in 5 Steps for Tech Founders

Rákóczi Piroska — Mon, 16 Mar 2026 13:30:31 +0000

Many tech founders struggle with writing. That’s completely normal. However, if you want to succeed in your market, your product website should contain at least 4–5 well-written blog posts.
If you also want your website to perform well in SEO, you’ll need multiple blog articles that link to each other in a structured way. Internal linking between relevant posts is a classic and effective SEO method.
Yes, link-building platforms and backlinks are important, but an old and proven strategy is to build your own network of real blog content.
So, if you’re not a strong writer, does that mean you have to suddenly become one and write endlessly? Not at all. The truth is that you can write — you may simply have been approaching it the wrong way.

1. Stimulate Your Brain Before Writing

Different people unlock their creativity in different ways.
The German poet Friedrich Schiller reportedly smelled rotten apples before writing. Some people get their best ideas while walking. Others think better while listening to music. There was even a Hungarian writer whose wife would lock him in a room close to his deadline and refuse to let him out until he finished his manuscript.
Today, many people turn to artificial intelligence for help. That can be useful — but it should usually be the last step, not the first.
The first step is creating the right environment and mental state for writing.

2. Use Interviews as Inspiration

This method is simple and surprisingly effective.
Find interviews with developers or founders who created products similar to yours. Alternatively, read interviews where experts discuss your competitors’ tools.
Select the three most interesting questions and write your own answers to them. Then turn those questions into subheadings.
If the text still feels unfinished, you can give it to an LLM and ask it to generate an introduction and conclusion. With that, your first blog post is ready.

3. Write a “Making-Of” Post

Take a sheet of paper and write down the different phases of building your product. Don’t worry about the order at first — just list the stages as they come to mind.
Once you have them all, categorize them by difficulty. Then arrange them in chronological order.
For each phase that required a key idea or creative solution, explain how you came up with it. When you’re finished, ask an AI tool to turn your notes into a blog post titled something like:
“How We Built [Your Product Name]”
This type of behind-the-scenes content is very engaging for readers. And it would be a strong plus, if you wrote the fortunate events, simply because having good luck is interesting for the readers.

4. Use a Structured (Even Schematic) Approach

If writing still feels difficult, try a more structured method.
Look at reviews of similar products. Many reviews follow a predictable structure: they describe features, advantages, and typical use cases.
Write short sentences based on these patterns, but replace the product name and features with those of your own product.
Your text might feel a bit mechanical — and that’s okay. Give the draft to an AI model and ask it to rewrite the text with richer vocabulary and more natural phrasing.
The result will be a polished article built from your structured notes.

5. Write News Using the 5W + 1H Method

Think about events during your product development that could be interesting.
For example:
• Someone on your team discovered the cheapest reliable hosting provider.
• You finalized the color palette for your entire website after a long discussion.
• A technical breakthrough solved a persistent bug.
Each sentence (or pair of sentences) should answer one of the classic journalistic questions:
Who, What, When, Where, Why, and How.
Even if you only answer three of the W’s plus “How,” that’s perfectly fine.
Once you’ve written these short notes, explain why each event mattered. Then select the most unique or interesting moment and ask an AI tool to generate questions about it. After answering those questions and organizing the responses, you will have a complete blog post.

Summary

Tech founders don’t need to be professional writers to create effective blog content. By using structured methods — such as interviews, development stories, schematic reviews, and the 5W+1H framework — any founder can turn their knowledge into valuable articles. With the help of AI for editing and polishing, even simple notes can become engaging blog posts that improve SEO and strengthen a product’s online presence.

Chrome Extension in Almost Zero Minute

Rákóczi Piroska — Mon, 20 Oct 2025 11:36:17 +0000

If you read this story, you might be shrugging your shoulders. "Okay, I managed to make a Chrome extension in 5 minutes. What's that?” But if I told you how many hours of work it took to come up with that five-minute solution, you'd ask, why didn't I start with Agent-Zero?

The False Lure of Free

What did I actually make? A Chrome Extension that logs and makes searchable the online marketing activities of a guerrilla online marketing specialist on the web.

I thought it was small enough to fit into free vibe coders. And I wasn't wrong. Three services wrote codes, but none of them worked. Two services said that the manifest.json program part needed to be rewritten. No matter how I rewrote the manifest.json codes again and again and again, it simply wouldn't start.

Then let's pay, but it matters how

I have an agent that runs on my machine in a Docker container. Anyone can download and use it for free, you just have to pay for one of the LLM API keys - or you musn't pay even for that. So why don't I try this?

And it worked. That is, it didn't work. I couldn't create a working extension with Agent-Zero either. But he told me why. He said that the manifest.json is not good because the background file is not good. That's where the problem lies. But the problem was with me.

You have to be able to notice

The mistake was that I left out the letter "k" from the name of the background file. If Agent-Zero doesn't tell me to check the background file, I might as well scrap the whole plan.

What's the lesson from this?

No, it's not just about being attentive. It's also about using an agent that makes LLM capable of real dialogue. The solution wasn't about LLMs. It wasn't about paying a few cents to use the API. I could even use free LLMs form Venice.ai The key to the solution is in the structure and memory of Agent-Zero. This opened the door for me to a solution. You may try Agent-Zero too.

How to solve dictionaries versions problem?

Rákóczi Piroska — Fri, 10 Oct 2025 08:45:32 +0000

Managing library dependencies is a notorious challenge in software development. A task like reconciling compatible versions of NumPy, Pandas, and Selenium can consume hours, if not days. While experienced developers might navigate this with confidence, it remains a significant bottleneck.

AgentZero offers a powerful solution. It is a local AI agent designed to automate complex, information-based tasks, including dependency resolution.

Consider a recent case: one of our developers faced a project requiring the reconciliation of eight different libraries, a task he estimated would take a full day. By delegating the problem to AgentZero, he received a perfect, conflict-free configuration in minutes.

AgentZero is more than just a programming assistant. It's a versatile tool capable of handling a wide range of tasks, from generating images and writing text to automating email workflows.

Key Benefits of AgentZero:

Local and Free: AgentZero runs on your machine and is completely free to use.

LLM Agnostic: It works with a wide variety of Large Language Models.
Privacy-First: For confidential projects, you can integrate AgentZero with Venice.ai, a private and uncensored LLM provider, ensuring your data never leaves your environment.

Enhanced Capabilities: AgentZero goes beyond standard LLMs by providing superior context management, memory, and intelligent task design, leading to more accurate and reliable results.
Whether you're solving dependency conflicts or building complex automations, AgentZero helps you get it right the first time. To get started, simply install Docker and add AgentZero to your toolkit.

Do you often have a version incompatibility issue in Python?

Rákóczi Piroska — Mon, 06 Oct 2025 12:37:59 +0000

When was the last time you struggled to find compatible versions of different Python libraries? If you're a black belt Python programmer, you probably know which version of NumPy works with which version of Pandas. But do you also know which version of Selenium is compatible with other libraries you use in your program?

What is a simple solution to the version number reconciliation problem?

You may have heard of AgentZero. It is an AI agent that runs on your own machine and is excellent at solving the version number reconciliation problem.
One of our programmers was working on a new project and had to reconcile 8 libraries. He said it would take a day. Then he had an idea. What if he gave AgentZero the task?
And he gave it to him. And the result was there in no time. And was it good? It was perfect.

You can solve it too...

To overcome this difficulty so easily, all you have to do is download Docker and add AgentZero.

But there so much more

I'll tell you something. AgentZero is not an AI agent designed for programming. It can solve a lot of tasks, from image generation to text writing to email automation, almost anything that can be done with information. And the best part...

How much does it cost you?

AgentZero is **free **and works with a wide variety of LLMs.

If your project is confidential...

If you have a task that requires maximum privacy, AgentZero is the best solution. Because you can use Venice.ai with it, which is a private and uncensored LLM collection.

Is it worth all those installations for a good version number?

I myself have recently created several chrome extensions with vibe coders. The only extension that worked perfectly the first time was written in AgentZero. The LLMs were the same, but with the additional knowledge of AgentZero (excellent context management, great memory, smart design, etc.) I got a version that didn't require any fixes. What do you have to lose by trying it out?

What is the perfect solution for AI agent security?

Rákóczi Piroska — Fri, 19 Sep 2025 10:46:47 +0000

Agent0 and Venice.ai, two natural complements in the world of artificial intelligence, have connected. It means that there is a quintessential solution for harnessing the full potential of AI technology while maintaining unwavering commitment to privacy. Sounds complicated? Let’s break it down.

What is Venice.ai?

Venice.ai is the best LLM collection in the crypto world. It is an AI chatbot with 2 huge advantages over its competitors. On the one hand, its architecture allows the user's AI prompts to be 100% private. All data stays on your device, not on their servers. On the other hand, Venice offers the most uncensored models for a truly unrestricted AI experience.

What is Agent0?

Agent0 is the “master mind” of free AI agents. It is your own self-hosted AI powerhouse, ready to execute and automate demand. Agent0 with 3000+ people on discord and 1600+ followers on skool controls its own virtual computer and can accomplish any task. It can install software, execute code, connect anywhere, use browser and much more.

Why does self-hosting matter?

I would like to illustrate why this is so important with a practical example. Let's say a company wants to automate the writing of weekly reports that contain analytics. The company's manager has a hunch that some of their employees are using external LLMs to prepare productivity reports. The CEO wants to eliminate this practice because he wants to keep their employees' income data safe. This is when Agent0 and Venice.ai come in very handy. How?
Agent0 can do everything for making the report inclusively counting productivity without leaving the laptop of the college – and it can do with LLMs that don’t send and share the prompts with anyone.
In other words. Privacy is an issue for Agent Zero because it has memory, learns as you use it, so it may hold sensitive information about the user or his company. This issue is solved, and the collaboration with Venice.ai even closes the last gap. If AO is used with Venice.ai not even a single prompt can get out of security control.

Our community is at work

Agent0 already has a community of curious developers. We are at the beginning of our collaboration with Venice.ai. Community members are now testing the 14+LLMs that users can access in Venice.ai.

Until then

If you are a developer and have a cherished dream of a project that you don't want to make public yet, then this article is definitely for you. Agent0 is fully scalable, and Venice.ai guarantees that not a single thought will leave your computer and go online. They say it's worth a try. You can join the community here: https://www.skool.com/agent-zero