祝融说。

Consider a common but composite launch scenario: developers or entrepreneurs spend most of their attention on code and product, then first consider distribution systematically only after deployment. They may devote many nights to the product, but effort alone cannot establish that users will discover or need it. This describes a structural risk, not a statistic drawn from the author's personal experience.

What happens next is, usually... nothing.

No queue of users. No media coverage. No avalanche of orders. Only the lonely crawler entries in the server logs and that nearly flat blue line on Google Analytics, representing single-digit visitors.

The cemetery of the digital world is piled high with "products" that are fully functional and beautifully designed yet utterly ignored. Their epitaphs could all share the same line: "I solved a problem no one cared about."

Failed projects fail for countless reasons. But successful projects all begin in a strikingly similar place: with the right product selection. In the digital world, "product selection" may sound a little mercenary, even like rummaging through goods at a wholesale market. But trust me, this is exactly the mindset we need. We are not artists pouring out inspiration; we are engineers solving problems. We are investors hunting for undervalued opportunities.

Product selection is the strategic fulcrum for everything you do afterward. A good selection makes traffic acquisition effortless and turns monetization into a natural downstream flow. A bad selection drags you into an endless quagmire; no matter how hard you optimize and promote, it feels like pushing a boulder up a mountain.

So how do you find that right fulcrum?

Forget your passion. Forget the ideas you think are cool. Product selection is a science — an interdisciplinary one spanning human nature, market dynamics, and the gaps in technology. In this chapter, I will distill the pitfalls I have stumbled into and the patterns I have learned over the years into an actionable selection framework. The heart of this framework is a single word: Painkiller.

People buy and use products for only two fundamental motives: to pursue pleasure or to escape pain.

The products for pursuing pleasure, I call "Vitamins." They are pleasant and useful; they add "frosting to the cake" of your life. A habit-tracking app, a meditation guide, an online course that teaches you a new hobby — these are "nice-to-have." But when users are short on time and tight on budget, these "vitamins" are the first to be dropped.

The products for escaping pain, I call "Painkillers." They directly and bluntly solve a pain point that is happening right now, one that is irritating and blocking progress. They are "must-have." When your head is splitting with a throbbing migraine, you do not care whether the pill bottle is elegant or whether the brand has a moving story. You just want to swallow it immediately and make the pain disappear.

Our goal is to find, build, and sell these "painkillers" of the digital world. Because only pain breeds the strongest demand, the highest willingness to pay, and the fastest user decisions.

In this chapter, we will pass through four progressively refined filters to help you sift precisely from the noise of the internet those high-value "painkiller" opportunities.

1.1 Intent over Keyword: Abandon Search Volume, Focus on "Problem-Solving Urgency"

In the old world of SEO, we were all disciples of the keyword. We worshipped tools like Ahrefs and Semrush the way diviners worship turtle shells. We studied "monthly search volume," "keyword difficulty," and "cost per click" with obsessive devotion.

Our logic was simple and direct: find a word with high search volume and low competition, build content or a tool around it, and wait for Google's rain of traffic. Over the past decade, this logic gave birth to countless successful websites.

But as I said in the introduction, the age of the great fracture has arrived. AI is taking over the work of integrating and presenting information, which makes any strategy that relies purely on keyword search volume increasingly fragile. A high-volume word like "how to lose weight" hides a wild tangle of user intents: some people want the underlying science, some want recipes, some are just idly browsing. AI can satisfy 80% of the shallow demand with a single perfect review article, thereby cutting off the traffic that would otherwise flow to our sites.

So we must evolve. We must move from asking "what are people searching for" (keywords) to asking "why are people searching" (intent).

I propose a new core metric to replace "monthly search volume": Problem-Solving Urgency (PSU).

This is a qualitative metric; you will not find it in any SEO tool. It requires you to think like a detective, analyzing and sensing the humanity behind search behavior. The higher an area's PSU, the more real the user's pain, the more rigid the demand, and the greater the commercial value.

Let us dissect this concept with a classic example: "PDF to Word" versus "how to write a resume."

By traditional keyword metrics:

• "How to write a resume": millions of worldwide monthly searches — a massive traffic pool. Countless blogs, courses, and template sites fight over this term.
• "PDF to Word": also millions of monthly searches, though perhaps slightly fewer than the former.

Under the old selection logic, both look like fine choices. But through the filter of "intent" and "urgency," their value diverges by heaven and earth.

Scenario 1: The user searching "how to write a resume"

This user might be:

• A graduating college student who is still months away from job hunting and wants to prepare a little early.
• A professional mulling a job change who browses their phone on the commute to see if there are any new tricks.
• Someone dissatisfied with their current situation but not yet resolved to act, treating "polishing my resume" as a way to soothe anxiety.

What do they have in common? No urgency.

Their pain is chronic and vague. They have plenty of time to read long-winded "ultimate guides," compare dozens of templates, and watch video tutorials. They are in the "learning" and "research" phase, with a very long decision chain. They read your article today, and they may not actually start writing a resume until a month from now.

Monetizing such traffic is difficult. You can sell templates, but the user thinks, "let me see what else is out there." You can sell a course, but the user thinks, "let me bookmark it and learn when I have time." Their willingness to pay is diluted by an endlessly stretched timeline.

Scenario 2: The user searching "PDF to Word"

Now picture this user. They are very likely in one of these maddening situations:

• HR just sent a PDF contract that must be filled in and signed, with a deadline of 5 p.m. today.
• They need to revise a report, but only have the final PDF — the source file is nowhere to be found.
• They downloaded an important paper and want to copy a passage, only to find the PDF is locked and cannot be copied.

What do these users have in common? Extreme urgency.

Their pain is sharp and concrete. They are not "learning" how to convert file formats; they are "executing" a task. They are stuck. Their workflow has been interrupted by a blasted file format. They need a solution that works immediately, right this second.

They have no patience to read a 3,000-word treatise on "the historical relationship between PDF and Word." They just want to see a big button that says "Upload your PDF file." They will click the first, second, or third link in Google without a second thought — whichever site loads faster and has a cleaner interface is the one they use.

For such traffic, monetization is direct. If your tool says, "Free conversion: 3 per day. You have used your limit. Upgrade to Pro for unlimited conversions," there is a very high probability they will pull out a credit card on the spot. Because a $5 monthly fee means nothing next to the risk of missing a contract deadline or catching a scolding from the boss.

That is the power of the painkiller. "How to write a resume" is a vitamin — it makes you better. "PDF to Word" is a painkiller — it takes you from a bad state back to normal. People will pay for "better," but they will pay almost anything for "normal."

So how do you quantify and assess a field's "Problem-Solving Urgency" (PSU)?

Here is a practical "urgency" checklist. When you find a promising tool direction, put it through these five questions:

1. Is the user "stuck"?

   • Does this problem interrupt a critical workflow or life process for the user?
   • High-PSU examples: file format conversion, code formatting, image background removal, unit conversion. Without these tools, the user's next step simply cannot proceed.
   • Low-PSU examples: habit formation, quote generators, knowledge-graph browsing. These are pastimes or self-improvement, not necessary flows.

2. Is there a clear "deadline"?

   • Does solving this problem often come under time pressure?
   • High-PSU examples: time-zone conversion ("I need to send the meeting invite before my client leaves the office"), countdown generators, project-duration calculators.
   • Low-PSU examples: recipe searches, travel guides. These can usually be planned well in advance or adjusted on the fly.

3. Is it directly tied to "money"?

   • Does solving this problem help the user save or make money directly?
   • High-PSU examples: mortgage calculators, currency converters, stock-investment return calculators, e-commerce cost-and-profit calculators. Every decision is tied to real cash.
   • Low-PSU examples: color-palette generators, font identifiers. Useful for designers, yes, but only weakly connected to direct financial outcomes.

4. How often does the pain occur?

   • Is this a problem encountered every day or every week, or one you hit once in a lifetime?
   • High-PSU examples: JSON validators (programmers use them daily), screenshot beautifiers (product managers use them weekly), temp-mail (used constantly when registering for sites). High frequency means high retention and high SaaS conversion potential.
   • Low-PSU examples: wedding-invitation generators, business-name generators. These are low-frequency, one-off needs.

5. How low is the solution's "substitutability"?

   • Can the user easily solve this problem by other manual means?
   • High-PSU examples: audio denoising, video compression. Doing these manually is nearly impossible, or requires expensive professional software.
   • Low-PSU examples: converting the case of text.

When users have to handle these tasks manually, they feel inefficient, error-prone, and even psychologically resistant. Your tool hands them a "one-click" magic button — value they can feel the instant they use it.

Run any potential idea through these five questions and you can score its "urgency." Our goal is to find "painkiller" ideas that score high across multiple dimensions.

Remember: stop chasing those "vitamin" keywords with millions of searches and murky intent. Embrace those "intent keywords" that may have only ten thousand searches but hide a level of urgency like "my house is on fire, I need a fire extinguisher." The former gives you vanity traffic numbers; the latter gives you real, sustained cash flow.

1.2 The Golden Formula: Public Data Source + Minimal Algorithm + Solving a Specific Pain Point = Hit Tool

When I first entered the industry, building a website was an endeavor with an extremely high barrier. You needed to buy expensive servers, master complex backend languages, and design and maintain your own database. Most importantly, you needed "exclusive content" or "exclusive data." In that age of scarce information, owning data meant owning power.

Times have changed.

Today we live in an age of data surplus. Data is no longer a treasure locked in a palace vault; it is like air and water, everywhere. Governments, academic institutions, and commercial companies release vast troves of data into the public domain every day. This presents an unprecedented historical opportunity for us independent developers and "digital craftsmen."

You no longer need to be a data producer; you can become a data refiner. You do not need to invent new particles; you only need to build a furnace that turns iron ore into steel.

This is the Golden Formula I have distilled. It is the underlying logic behind all my successful tool-site projects. It breaks the seemingly complex "product development" process down into three extremely simple, executable modules. Master this formula, and you have mastered a machine capable of mass-producing "digital assets."

Let us unpack each part of the formula.

Part 1: Public Data Sources — The World Is Your Database

This is the "raw material" for all your tools. The good news is that most of these materials are free and of excellent quality. You just need to know where to find them and how to use them.

"Data source" here is a broad concept, and it can be divided into the following categories:

1. Government and public-institution data

This is the most underestimated goldmine. Governments around the world, for transparency and research purposes, have opened up enormous databases. This data is usually authoritative, clean, and updated on a stable schedule.

• Examples:
  • The U.S. Census Bureau provides data on population, income, and racial composition down to the block level. You could build a "best cities to relocate to" recommendation tool.
  • The Bureau of Labor Statistics provides average prices for a wide range of goods. You could build a "my personal inflation calculator."
  • Central banks publish historical and real-time interest-rate and exchange-rate data. This is the foundation of every financial calculator tool.
  • The World Health Organization (WHO) provides global health and disease data. You could build a "travel health risk assessment" tool.
  • Mindset shift: do not keep asking "what data can I create?" Instead ask, "based on [Some Organization]'s public data, what convenient query-and-comparison service can I offer users?"

2. Public APIs

Many companies expose their core data or functionality through APIs, letting you build new applications on top of them.

• Examples:
  • Weather APIs (like OpenWeatherMap): you could build a "best cities for outdoor barbecue in the next 7 days" recommendation tool — not just a bare weather forecast.
  • Map APIs (like Google Maps, Mapbox): you could build a tool that "calculates calories burned driving, cycling, or walking between two points."
  • Financial data APIs (like Alpha Vantage, IEX Cloud): you could build a visualization tool for "if I had bought $1,000 of Apple stock five years ago, what would it be worth today?"
  • AI APIs (like OpenAI, Anthropic): you can wrap a general AI model into an "AI Wrapper" that solves one specific problem. We will discuss this in section 1.3.
  • Mindset shift: you are not competing with these giants; you are dancing on their shoulders. They provide the engine; you just need to craft a beautiful, focused steering wheel.

3. "Generative" data sources

This is a very special and powerful category. Its data does not come from outside; it is generated by logic and rules themselves.

• Examples:
  • Color codes: there are only 16,777,216 HEX color codes in the world. This is a finite, deterministic set that a program can iterate through and generate. You could produce an independent page for every single code (e.g., "HEX to RGB," "color-palette recommendations"), laying the foundation for a pSEO site with tens of millions of pages.
  • Time and dates: every single day from year 1 to year 9999 is a deterministic data point. You could build a tool like "what happened on this day in history" or "calculate the days/hours/seconds between two dates."
  • Mathematical constants and sequences: pi, the prime numbers, the Fibonacci sequence. These are infinite but regular. You could build a tool that "queries the Nth digit after pi's decimal point."
  • Characters and encodings: the ASCII table, the Unicode character set. You could build a "Unicode character lookup" or "copy special symbols with one click" tool.
  • Mindset shift: many times, the "database" you need is just one for loop. Do not underestimate the power of data produced by pure logic; they are perfect fuel for building massive, evergreen pages.

4. Legally scraped public web data

This is a gray area, but done properly, it remains a powerful data source. Scrape only data that is public, non-personal, and permitted by a site's robots.txt protocol.

• Examples:
  • Scrape public product prices from e-commerce sites (excluding any user data) to build a "historical price tracker."
  • Scrape public, trending hashtags from social media to build a "TikTok trending hashtag recommendation" tool.
  • Important warning: always honor a site's terms of service and robots.txt rules. Do not scrape at high frequency or put stress on the target's servers. Never scrape anything involving personal privacy. This method carries higher risk; for beginners, I recommend the first three categories.

Finding your data source is the first and most important step in the Golden Formula, because the data itself defines your battlefield. A good data source is like fertile land: with the simplest tools, you can cultivate it and reap a rich harvest.

Part 2: Minimal Algorithm — Your "Secret Recipe" Is a Thin Layer of Logic Frosting

When we hear the word "algorithm," our minds may conjure complex mathematical formulas, neural networks, or mysterious black boxes like Google's PageRank.

Forget all that.

In our Golden Formula, "algorithm" is redefined. It does not mean arcane science; it means an extremely simple layer of logic for processing data. Its core value is not "complexity" or "intelligence," but "usefulness" and "time-saving."

Your algorithm is the bridge spanning "raw data" and "user pain point." This bridge does not need to be built of gold; it only needs to be sturdy enough to let people and vehicles cross.

Let us look at the main types of these "minimal algorithms":

Calculation

This is the most common type. You feed the user's input and your data source into a public, mature formula, then return the result.

• Examples:
  • Mortgage calculator: the algorithm is the public equal-installment/equal-principal formula. The data source is the user's loan amount and term, plus a real-time LPR rate if you have integrated one.
  • BMI (Body Mass Index) calculator: the algorithm is weight(kg) / height(m)^2. The data source is simply the user's input.
  • Recipe-ingredient scaler: the algorithm is simple multiplication and division. The user enters the original portion and the number of servings they want, and the tool automatically computes the new amount of every ingredient.
  • Secret: you do not need to invent new mathematics. Just find the situations where people need a calculation in daily life but are too lazy to reach for a calculator or find it a hassle. Your value lies in convenience.

Conversion

The essence of this type of algorithm is "format translation." You accept input in one format and transform it into output in another.

• Examples:
  • File converters (PDF to Word, PNG to JPG, MP4 to MP3): the algorithm simply calls a mature open-source library (like pdf-lib, sharp, ffmpeg). You do not need to reinvent the wheel.
  • Unit converters (inches to centimeters, pounds to kilograms, Celsius to Fahrenheit): the algorithm is a fixed conversion factor.
  • Code formatters/converters (JSON to CSV, YAML to JSON, SQL Formatter): again, just calling mature open-source libraries.
  • Secret: find the incompatible, walled-off "digital islands" and build a bridge across them. Every successful format conversion is an act of value creation. Programmers, designers, and office workers are the high-frequency users of these tools.

Generation

This type of algorithm creates, on the basis of a set of rules, something the user needs.

• Examples:
  • Random password generator: the algorithm is "randomly draw N characters from a character set containing uppercase letters, lowercase letters, numbers, and symbols."
  • Lorem Ipsum placeholder-text generator: the algorithm randomly plucks sentences and paragraphs from a Latin source text.
  • Resume/privacy-policy/terms-of-service generator: the algorithm is a "template assembly" system. You pre-build a large library of text modules and, based on the user's simple choices ("Does your company use cookies?"), splice the relevant modules into one complete document.
  • Secret: find the things users need to "create from nothing" but find repetitive, tedious, or uninspiring. Your tool is their "creative crutch."

Filtering and aggregation

This type of algorithm dwells inside a vast dataset and, according to the user's conditions, quickly finds the information they want.

• Examples:
  • A "what movie should I watch?" tool: the data source is IMDb's movie database; the algorithm lets the user tick a few tags ("90s," "sci-fi," "rating above 8.0") and returns a list of matching films.
  • A domain-search tool: the data source is the APIs of major registrars; the algorithm combines the user's keywords with various prefixes and suffixes, then batch-queries whether those domains are registered.
  • A "what dishes can I make with these ingredients?" tool: the data source is a recipe database; the algorithm lets the user enter the few ingredients they have on hand and matches every recipe containing them.
  • Secret: when the ocean of data is too vast to navigate, your tool is an efficient "fishing net." You rescue users from the anxiety of information overload.

Notice the common thread? Not one of these algorithms requires a machine-learning PhD. Most can be implemented in a few dozen lines of code, at most a few hundred. Their brilliance lies not in technical depth, but in precise insight into user needs.

Always remember: code is cheap, traffic is expensive. Spend 20% of your time on the "minimal algorithm" to make it stable and accurate. Then put the remaining 80% into the traffic acquisition we will cover in later chapters. Do not fall into the "perfect algorithm" trap — that is a rabbit hole with no bottom.

Part 3: Minimal UI — Get Out of the User's Sight

You found a golden data source, and you wrote a clean, efficient algorithm. Now you need a "container" to present them to the user. That container is your user interface (UI).

Here I want to offer a possibly counterintuitive view: the best UI is no UI.

Our goal is to make a "painkiller," not a "work of art." A person with a splitting headache just wants to open the bottle and swallow the pill as fast as possible; they do not care what patterns are carved on the label. Likewise, a user tortured by a PDF format just wants to see an upload button; they do not care what fashionable gradient colors your site uses.

Minimal UI is a philosophy. Its core is: respect the user's time, let them complete their task as fast as possible, then get out of their sight.

A successful tool-site UI must obey the following iron rules:

The first screen is the deciding battle

• User attention is the scarcest resource on the internet. The moment your page finishes loading, the user must be able to see and understand how to use your tool without scrolling.
• Golden rule: the above-the-fold area must contain the input region, the core action button, and a one-line functional description.
• Cautionary example: many tool sites put a huge banner taking up half the screen at the top, followed by a long-winded spiel about "who we are" and "what our mission is." Users have to scroll two or three screens before they find the little tool itself. That is a colossal waste of users' lives and a suicidal squandering of traffic.
• Correct example: open TinyPNG. What do you see? A giant dashed box that says "Drop your .png or .jpg files here!" No fluff, no distraction. The UI is itself the instruction. That is the master's touch.

Reduce every unnecessary click and keystroke

• Minimize the user's cost of operation to the extreme. Every extra mouse click or extra key you make a user press will permanently cost you a portion of your users.
• Practical techniques:
  • Smart defaults: if your tool is a "random password generator," default to a strong 16-character password containing special characters. Do not make users select a stack of options first. Offer advanced options, but set the most common choice as the default.
  • Auto-paste/read the clipboard: if your tool is a "JSON formatter," write a small script that, with the user's permission, automatically reads the clipboard and fills the input box.
  • Real-time preview/calculation: do not make users fill in every field and then click "Submit" before they can see the result. The results area should update in real time with every stroke of input. A mortgage calculator should make the monthly payment figure dance like a heartbeat the moment the user drags the interest-rate slider.
  • One-click copy button: always put a "copy to clipboard" button beside the result. Do not make users manually select text, right-click, and copy. That tiny button lifts the user experience by an order of magnitude.

Mobile first, desktop compatible

• This is a well-worn refrain, yet 90% of people get it wrong. They design a pretty desktop site first, then try to "cram" it into a phone screen. The result is buttons smaller than ants and text squeezed into a jumble.
• The correct process is the reverse: first consider, on an iPhone 8 (a very small screen), how your tool can work perfectly. In that extremely constrained space, you will be forced to cut every unnecessary design element and keep only the core function. Once you finish that "bare-survival" design, you expand it to tablets and desktops — and discover the extra room lets you gracefully add secondary features and whitespace.
• Remember the order of magnitude: across all websites globally, mobile's share of visits has stayed above half for years, with most measurements around sixty percent (see StatCounter's platform share statistics). A given tool site's share varies by category, but a tool that is awkward to use on a phone has given up a substantial part of its market.

Performance is part of the UI

• A slow-loading website is meaningless no matter how beautiful the UI. Users will close the tab out of impatience before your fancy animations ever finish loading.
• Tech stack: this is why, in later chapters, I will emphasize modern front-end stacks like Next.js/Nuxt.js plus Vercel/Netlify. They deliver blistering load speeds through SSR/SSG and a global CDN. Abandon bloated WordPress and shared hosting — they are performance killers for tool sites.
• Optimization targets: your goal should be a mobile score of 90+ on Google PageSpeed Insights. Compress every image, minify the CSS and JavaScript, enable browser caching. Treat performance optimization as your most important feature.

In summary, the Golden Formula (public data source + minimal algorithm + minimal UI = hit tool) is the "first principle" of your tool-site selection and development.

It frees you from dependence on "genius ideas" and turns product development into a repeatable, predictable process. Your job is no longer "invention" but "discovery" — discovering a valuable public dataset, discovering an unmet pain point, then wiring them together in the simplest, most direct way.

In the infinite universe of the internet, this formula is your law of universal gravitation. It helps you part the fog and find those truly valuable stars that can cross the cycles of time.

1.3 Avoid Giants, Pursue Micro-Monopoly: Do Not Build a "General AI" — Build an "Amazon Listing Generator" or a "TikTok Hashtag Extractor"

In the coffee shops of San Francisco, you can always overhear the young people at the next table passionately discussing their "next Facebook" or "next Google." They are ambitious — they want to build platforms, set the rules, and rule the world.

I respect these young people, but my advice is: keep your distance from them.

We are independent developers and digital craftsmen, not tech giants armed with unlimited capital and thousands of PhDs. Competing head-on with giants on their home battlefield is like a mantis trying to stop a chariot. They command resources we can never match: brand, users, data, capital, talent.

Our path to survival lies not in being "better," but in being "different." Not in fighting in the red ocean, but in finding those niche markets the giants look down on, cannot be bothered with, or are simply too big to serve well.

Our strategy is not to become an "empire," but to become the "local sovereign" of a remote yet prosperous province. That is what I call "Micro-Monopoly."

The definition of micro-monopoly: within an extremely narrow, specific, particular vertical, become the user's first choice — or even their only choice.

When a merchant selling gardening tools on Amazon needs a compelling product description for his new "stainless-steel weeding fork," he will not turn to generic ChatGPT, because ChatGPT's answer is far too broad. Instead he searches Google for "Amazon listing description generator for gardening tools." If your site appears first and can generate descriptions that comply with Amazon's rules, pack in high-conversion keywords, and are tailored specifically to gardening products, then at that moment, for that user, you are the center of the universe. You have achieved a monopoly over that micro-demand.

This strategic thinking has become especially important in the age of AI, because giants led by OpenAI are sweeping across all the general, broad needs with their large models.

Do not build a "general AI writing assistant." You will be drowned by Jasper, Copy.ai, and thousands of similar products. Worse, you will be undercut at the root by ChatGPT itself. Build a specialist doctor instead.

How do you find and seize these "crevices"?

Step 1: Put "handcuffs" on the giant — create a narrowing dimension

The giant's strength lies in breadth; ours in depth. What we must do is add one or more "constraints" to the general capability the giant provides, thereby creating a new, smaller market.

These "constraints" or "narrowing dimensions" usually include:

Targeting a specific "platform":

Generic tools serve everyone; our tools serve only the users of a specific platform.

• Examples:
  • Generic tool: an image editor.
  • Niche tool: a YouTube thumbnail generator. This tool's user goal is extremely clear. It does not need all the complex functions of a general editor; it only needs to match YouTube's specific thumbnail dimensions (1280x720), quickly add eye-catching large fonts, and conveniently combine with face detection for background removal. You can make it more focused and more convenient than Canva.
  • Generic tool: an AI writing assistant.
  • Niche tool: a LinkedIn profile optimizer. This tool has specifically studied tens of thousands of successful LinkedIn profiles; it knows how to use professional, forceful verbs, how to quantify your achievements, and how to arrange keywords to attract recruiters.
  • Other platform crevices: Etsy product-description generator, Substack newsletter headline analyzer, Shopify store theme color matcher, Twitter/X thread formatter.

Targeting a specific "audience/profession":

Generic tools try to satisfy every profession, and the result is that they serve none of them well.

• Examples:
  • Generic tool: project-management software (like Trello, Asana).
  • Niche tool: a case-management board designed for solo lawyers. This board pre-configures law-specific workflows such as "evidence gathering," "client communication," and "court preparation." It may be simpler than Trello, yet ten times more efficient for a lawyer.
  • Generic tool: a text editor.
  • Niche tool: a script-formatting writing tool for screenwriters. It automatically handles scene headings, character names, and dialogue indentation — things an ordinary text editor cannot do.
  • Other audience crevices: an audio-silencing tool that removes filler words for podcasters, a citation-format converter for academic researchers, a property-listing copy generator for real-estate agents.

Targeting a specific "task/workflow":

Generic tools provide a "toolbox"; our tool is the reinforced version of one particular function on a Swiss Army knife.

• Examples:
  • Generic tool: video-editing software (like Adobe Premiere).
  • Niche tool: a tool that automatically adds and translates subtitles to videos. Many content creators only need this one thing and do not want to learn a massive editing suite. Your tool: upload a video, choose a language, and download a subtitled video a few minutes later. Simple, blunt, effective.
  • Generic tool: spreadsheet software (like Excel, Google Sheets).
  • Niche tool: a tool that extracts tabular data from an image. Finance staff constantly need to enter invoices or reports locked in PDFs or images into Excel. Your tool saves them hours of manual typing.
  • Other task crevices: an abstract tool that automatically extracts the core points from an article, a tool that turns meeting recordings into structured meeting minutes, a visualization tool that compares the differences between two versions of a document.

Step 2: Become a master of the "AI Wrapper," not an AI model trainer

Many people labor under a misconception: to build an AI tool, you must train your own model. That is a game for giants, requiring vast data, computing power, and talent.

Our play is to build an "AI Wrapper."

The concept is simple: take a powerful, general AI large model (like GPT-4) and, through clever prompt engineering and a focused UI, wrap it into a "foolproof" tool that solves the specific niche problems above.

Your core competitiveness lies not in the model itself, but in your deep understanding of the needs of users in a specific domain.

The AI Wrapper construction workflow:

1. Lock the niche: on, say, we choose "generate Instagram post copy for independent coffee shop owners."

2. Research the "recipe" (prompt engineering): this is your "secret formula." You must design an exquisite, exhaustively detailed prompt to "tame" the general large model into thinking like a professional coffee-shop marketing expert.

   • A bad prompt: "Write an Instagram caption about a latte." → the result will be mediocre.
   • An exquisite prompt (your backend code):

   "You are now a professional social media marketing expert, especially skilled at building brand image for boutique independent coffee shops. Your style is warm, artistic, with a touch of life's philosophy. Please write three Instagram post captions for the following product.
   Product name: {user input, e.g., 'Autumn-limited Osmanthus Latte'}
   Product features: {user input, e.g., 'House-roasted Ethiopian beans, with hand-made osmanthus syrup'}
   Target audience: {user selection, e.g., 'White-collar workers in the surrounding office towers, artistic young people'}
   Caption requirements:
   1. Within 150 characters.
   2. Must include a thought-provoking open-ended question to encourage comments.
   3. Must include 3-5 relevant, niche but precise hashtags (e.g., #CityCoffeeMap #OsmanthusLatte #AutumnLimited, rather than #coffee).
   4. The tone should be like sharing an afternoon secret with an old friend.
   Please output the result in JSON format, containing three independent caption options."
   

3. Design the minimal UI: your front-end page only needs two input boxes ("Product Name," "Product Features") and one dropdown ("Target Audience"), plus a "Generate Copy" button. Users have no idea such a complex prompt sits behind it. To them, it feels like casting a spell.

See? You did not train a single cent of a model. You merely called OpenAI's API. All of your value is condensed in that carefully designed prompt and that focused UI. You shield users from the complexity of communicating with the large model and hand them a "one-click, quality work every time" deterministic experience.

This is the best path for the little guy to defeat the giant in the age of AI. The giant provides the power plant (the large model); you only need, in the community you know best, to lay the last mile of wiring and install the lamp that suits your users best.

Always remember: do not go dig for gold; go sell shovels. In the age of AI, the best "shovels" are the "AI Wrapper" tools that help specific people, on specific platforms, use AI more efficiently to complete specific tasks.

There, you need not face the giant's shadow. You only need to serve your 1,000 core users well to build a profitable, unshakable micro-monopoly kingdom.

All right, let us complete the final section of Chapter 1. This section will serve as the closing, condensing all the theory above into one extremely concrete, immediately actionable hands-on technique.

1.4 Reverse-Thinking Product Selection: Wherever ChatGPT Answers Poorly Is Your Opportunity

By now, we have built a complete theoretical framework for product selection: find high-"urgency" "painkillers," build them with the "Golden Formula," and position them in "micro-monopoly" niche markets.

This framework is very powerful. But for many beginners, one most primitive question remains: "Where do I start? How do I discover those specific, unmet pain points?"

You may have already tried mining startup forums for inspiration, or using SEO tools to dig up keywords. Those methods still work, but they are like hunting for seashells on a beach that thousands have already combed through.

Today I will teach you a more direct method, one more in tune with the spirit of the times. I call it "reverse-thinking product selection," or more plainly: "wherever AI shows its weakness is where we make our stand."

The core idea: treat large language models (LLMs) like ChatGPT as an omniscient-but-not-omnipotent "intern." Your job is to watch closely the tasks on which it messes up, hesitates, gives ambiguous answers, or flat-out admits it cannot do. Those places where it "exposes its weakness" are treasure maps hiding golden opportunities.

Why is this method so effective?

Because hundreds of millions of users are hurling every problem from their lives and work at AI. AI has become the new "search engine" and "all-purpose consultant." When AI cannot answer a question perfectly and directly, the user's need does not disappear — it overflows into traditional search engines, where they go looking for a specialized tool to solve it.

We are here to catch that "overflow" of demand. Wherever AI admits "I cannot do this," we plant a sign that reads: "Come here. I can."

Let us look at where AI commonly "shows weakness," and how to find opportunities in each.

1. AI's "knowledge cutoff" blind spot: it knows nothing about "now"

This is one of the LLM's most famous and most fundamental flaws. Their training data all comes from the past, so they know nothing about what is happening "right now." When you ask a question that requires real-time information, you get a reply like: "My knowledge cutoff is April 2023; I cannot provide real-time information..."

That is your first goldmine. Any need that depends on real-time, dynamic, ever-changing data is one AI cannot satisfy directly.

• AI cannot answer: "What is the cheapest flight from New York to London right now?"
  • Your opportunity: a real-time flight-price monitoring and comparison tool. Aggregate API data from multiple airlines and provide a clean query interface.
• AI cannot answer: "What is the average gas fee on the Ethereum blockchain right now?"
  • Your opportunity: a real-time crypto gas-fee tracker. Show a clear chart of gas-fee changes over the past 24 hours and predict the next low point.
• AI cannot answer: "What is the ping latency from my location's server to AWS's Singapore node?"
  • Your opportunity: a global server ping tester. With one click, your front-end script tests the latency and speed from the user's local network to the nodes of major cloud providers worldwide.
• AI cannot answer: "What is the hottest topic of discussion on Twitter about the 'Apple event' in the past hour?"
  • Your opportunity: a real-time social-media trend analysis tool. Call platform APIs for real-time data-stream analysis and visualization.

Action guide: pay attention to the needs in your daily life that require a "refresh" to obtain the latest information. Stock prices, sports scores, product inventory, exchange-rate movements, public-transit arrival times... every single "now" is a tool-site idea.

2. AI's "calculation and interaction" shortfall: it is a "liberal arts student," not a "science student"

Although AI can handle simple arithmetic, it is fundamentally a language model, not a computation engine. For complex, multi-variable calculation tasks requiring interactive exploration, AI becomes quite clumsy. It can tell you a formula, but it cannot give you a sandbox to "play" with the data.

When AI can only hand you a "recipe," but the user truly wants a "kitchen," your opportunity has arrived.

• AI can do: tell the user the compound-interest formula.

• AI cannot do well: provide an interactive compound-interest calculator where the user can drag sliders for "initial principal," "annual rate," and "investment term," watching the wealth-growth curve change in real time.

  • Your opportunity: a powerful, visual financial-calculator suite (mortgage, retirement, investment returns, and more).

• AI can do: describe the basic principles of color matching, like "complementary colors" and "analogous colors."

• AI cannot do well: provide a palette tool where the user picks a main color and the tool automatically generates multiple harmonious pairings, with one-click copying of every HEX code.

  • Your opportunity: a smart color-scheme generator built for designers.

• AI can do: explain the syntax rules of regular expressions.

• AI cannot do well: provide a regex tester where the user enters a piece of text and a regex pattern, with real-time highlighting of the matches, plus syntax explanations and performance analysis.

  • Your opportunity: an online regex debugging and visualization tool (Regex Tester).

Action guide: look for the domains where knowing the "principle" alone is not enough — where the user must personally "try" and "explore." Any scenario involving "what-if" analysis ("what if I change this parameter...?") is fertile ground for interactive tools.

3. AI's "format and structure" fussiness: it understands language, but not the "rules"

AI excels at generating free-flowing natural-language text, but for tasks that must strictly follow a specific format, structure, or specification, it often makes mistakes. That is because it does not truly "understand" the rules behind these formats; it is only imitating examples it has seen.

When what the user needs is not a "prose essay" but a precise "table," your opportunity has arrived.

• AI makes mistakes: generating a reference list in proper APA (American Psychological Association) citation format. It may drop a comma or put the year in the wrong place.

  • Your opportunity: an academic citation-format generator (like ZoteroBib, CiteThisForMe). The user just enters a book title or DOI, and the tool produces error-free citations in multiple mainstream styles.

• AI makes mistakes: writing a complex, deeply nested JSON or YAML configuration file. It may forget to close a bracket or introduce a syntax error.

  • Your opportunity: an online JSON/YAML validator and formatter. The user pastes in a jumble of messy code, and your tool beautifies it with one click and points out every syntax error.

• AI makes mistakes: producing a "privacy policy" document that complies with a specific legal template. It may use imprecise legal terms or omit key clauses required by the GDPR/CCPA.

  • Your opportunity: a website privacy-policy / terms-of-service template generator. Partner with a lawyer to modularize compliant legal text; users answer a few simple questions and get a substantially compliant document.

Action guide: focus on the domains that "have a standard answer" and "have fixed rules." Legal documents, academic conventions, programming languages, configuration files, official forms... in these domains, "creativity" is superfluous, and "precision" is the only value. Use the determinism of code to defeat the randomness of AI language models.

4. AI's "multimodal" processing limits: it lives mainly in the text world

Although AI has made enormous progress in image and audio processing, for the average developer the cost of calling these multimodal APIs is still high, and general models are still limited on specific professional tasks.

When the user's input is not text but an image, audio, video, or a file, your opportunity has arrived.

• AI cannot directly process: a user uploading a picture containing a table.

  • Your opportunity: an image-to-Excel OCR (optical character recognition) tool.

• AI cannot directly process: a user uploading a podcast recording with background noise.

  • Your opportunity: an AI audio-denoising and sound-enhancement tool (like a simplified version of Adobe Podcast).

• AI cannot directly process: a user uploading a 2GB 4K video file.

  • Your opportunity: an online video-compression and format-conversion tool.

• AI cannot directly process: a user uploading two PDF files.

  • Your opportunity: a PDF merge/split/watermark toolkit.

Action guide: break free of the text mindset and think about all the "files" people must handle in the digital world. Documents, images, audio, video... behind every file format hides a full set of "conversion," "editing," and "optimization" needs. These are territories that pure-text LLMs struggle to touch.

Chapter 1 Summary: Become a "Problem Connoisseur"

With this, we have completed the construction of the entire first section, "Strategic Anchoring." We have fundamentally reshaped the mindset of product selection:

• We no longer chase vanity keywords, but instead focus on the user intent that brings real money.
• We no longer fantasize about disruptive genius ideas, but instead follow a repeatable Golden Formula.
• We no longer gaze up at unreachable tech giants, but instead build our micro-monopolies in forgotten corners.
• We no longer search blindly for opportunities, but instead have learned to discover value in reverse from AI's weaknesses.

This entire framework ultimately points toward the cultivation of one core ability: becoming a "Problem Connoisseur."

A mediocre developer sees, "what can I do with this technology?" A good developer sees, "what problem does the user need solved?" An exceptional builder of digital assets tastes the "texture" of a problem the way a wine taster savors a vintage — just how high is this problem's "urgency"? How intense is its "pain"? Are the data and algorithm needed to solve it "simple" enough? Is this market small enough for the giants to "ignore"? Is AI "helpless" before this problem?

Once you begin looking at the digital world around you through this filter, you will find opportunities everywhere. In a colleague's complaint, in your frustration while handling a file, in a fruitless conversation with ChatGPT — any of them may hide the seed of next month's ten-thousand-yuan tool site.

Product selection is strategy. When the strategy is right, diligence on tactics finally means something.

Now, your strategic map is drawn. In the next chapter, we will move into the tactical execution layer, explaining how to take these "painkiller" ideas and, at maximum speed, turn them into ultra-lightweight, high-performance MVPs (Minimum Viable Products).

We will turn the blueprint into code.