An entrepreneur with zero technical background earned $17,000 in 5 days with Vibecoding and an AI agent, and shared a practical framework and guide to avoiding pitfalls for 5 encryption tools.
Written by: Ronin
Compiled by: AididiaoJP, Foresight News
2026 will be a turning point, when AI truly becomes an irreplaceable tool. We will be able to build things that previously required at least 10 developers, single-handedly and with almost no cost (excluding credits purchased in Claude Code). All you need is a concept and a clear understanding of what you want to build.
For a long time, I've been passionate about building my own products. I even earned $17,000 in 5 days by building my first app.
While conducting my research, I compiled a list of five of the best encryption tools currently available for building with Vibe Coding. This article won't offer ready-made hints or anything you can simply "Ctrl-C & Ctrl-V" for. But it will offer something far more valuable: the framework and structure of each idea. This is exactly what you need to understand how to build a truly useful application.
Let's break down each idea step by step and figure out how to extract maximum efficiency from it—whether for personal use or for sales.
The first step in building an application
Personally, I always start by looking at competitors to see if an idea is truly feasible. This is essential if you're not a developer and don't have much technical experience.
Then, I would open the existing online products, browse their documentation, and understand how each one works. In most cases, it involves connecting the necessary APIs, using them in a structured way, returning the data we need, or using specific filters, such as conducting a broad search for potential arbitrage opportunities.
Once you understand your competitors and their business volume, you'll have a clear picture of your potential revenue. It's pointless to jump straight into building on the first idea that pops into your head with zero exposure. It's like trading your first memecoin on pump.fun—what will happen? You'll lose everything, be disappointed, and think the space is doomed, but that's not the case; you're just doing it the wrong way.
Okay, we've analyzed our competitors, their technology stack, and their marketing methods. What's next?
Next, you'll need to do at least a minimal amount of structuring of everything in Notion or any application you find convenient. I also frequently build dashboards in Figma containing all the necessary elements, including the visual portion. Most products can function as Telegram or Discord bots, so visual design is irrelevant at this stage.
If we're talking about cryptocurrency products, don't waste time on UI/UX. The user base is already used to bad UI/UX. Build a working product first. Then, once you've earned your first revenue, you can spend money to buy proper UI/UX from designers.
Our focus is on building a robust technology stack that can efficiently execute your functions, using the APIs and other tools you need, depending on what you are specifically building.
Personally, I'll provide very clear examples here. You've certainly heard of them before, but I haven't seen anyone fully analyze what the technical concept should look like (specific promotional methods from a marketing and consulting perspective).
Let's get down to business.
1: Arbitrage bots between centralized and decentralized exchanges
Core Idea
The core idea behind this product is to find situations where there is a significant price difference between centralized and decentralized exchanges, so that you can still remain profitable after deducting all fees, gas, funding rates, and latency risks.
However, it's important to clarify that it's not as simple as tracking prices and then buying on a centralized exchange and selling on a decentralized exchange. Most of the time, deposits are closed or have long waiting times, and the price may have already leveled off before your deposit arrives.
In most cases, this strategy is achieved through hedging. For example, you buy on a decentralized exchange while simultaneously short a perpetual contract (or vice versa) on a centralized exchange instead of spot selling. You lock in the price difference and then either deliver the asset or rebalance.
These kinds of products typically look like a Telegram bot that sends you all sorts of similar messages, which you then manually analyze. While this is a fairly heavy product, it also has many potential internal issues.
Potential problems
- Price update delays and out-of-sync issues can return incorrect data.
- Fees: Order placement fee, order taking fee, funding rate if you use perpetual contracts for hedging, Gas, aggregator fees.
- Slippage: On decentralized exchanges, it depends on liquidity; on centralized exchanges, it depends on order book depth.
- Part of the trading volume on centralized exchanges and the need to chase the second price spread.
- Centralized exchanges may impose limits or API bans if you are too aggressive in acquiring and placing orders.
- Withdrawals and deposits: Suspension, delay, compliance, fees.
- MEV and sandwich attacks: If you expose your transactions to a public mempool (only relevant if you are a whale, nobody cares about below $1,000, and sometimes even below $100,000).
Technical Architecture
Let's look at it objectively, as a set of modules. What do you need for it to work properly? Even so, all of these still need to be tested, and you'll have to fix all sorts of problems that will arise.
1. Data collector (data scraping)
Basically, you need to always have real-time data from both sides.
- Centralized exchange collectors should connect to exchanges via API to obtain the best bid/ask prices, order book depth, and funding rates for perpetual contracts.
- The decentralized exchange collector should run a simulation: if you exchange X dollars now, how many tokens will you actually get?
- If the results show a price difference, that's what you need. Even a delay of 1 to 5 seconds can make a huge difference.
2. Brain/Opportunity Calculator
This module should acquire data and answer the question: "After deducting all fees, gas, slippage, and latency risks, can we still remain profitable?"
It needs to calculate the actual net profit margin:
- The difference between prices on centralized exchanges and exchange results on decentralized exchanges.
- Subtract the order placement fee or order taking fee from centralized exchanges.
- Subtract funding costs (if hedged through perpetual contracts).
- Subtract the gas and aggregator fees of the decentralized exchange.
- Subtract the expected slippage.
- Subtract the safety buffer (in case the price changes while you are executing).
If the profit margin is small, ignore it. It's crucial that it's real; almost all "pretty spreads" disappear once your robot correctly calculates the net profit margin.
3. Risk Filter (Double Check)
Even if a deal looks profitable, the bot should double-check everything before sending it to you:
Check if the gas, centralized exchange limits, and transaction size are too large for the order book or liquidity pool.
4. Telegram delivery layer
You need to set it up so that transactions are sent to you in real-time via a user-friendly Telegram format. Ideally, this should include direct links to perpetual contracts and decentralized exchanges, so you don't waste time. Also, check for any suspicious activity, ensuring the bot completes everything instantly and displays it in the logs.
5. Security
- Adding an "emergency stop" feature is important so that the robot can shut down and send you error logs when something goes wrong.
- Most importantly, disable all permissions on your centralized exchange API key except for read-only data access, and turn off "trading" and "withdrawal". This way, even if someone obtains your API key, they can't do anything about it.
- Of course, extensive testing is required. When you build it via Vibecoding, a large number of issues will arise that you'll need to fix. Even experienced developers building this will still fix many problems—be prepared for that.
Promotion methods
The most common example is real-life instances where you profit through arbitrage. Build your own community, start building your personal brand, and share your trades. If the product truly works, people will naturally become interested in it.
In my experience, you hardly need to spend money to promote a good product. All the good products I've built had budgets under $50,000, or even zero cost. Liquidity and paid advertising budgets are only really needed when they start generating their first $100,000 to $1 million in revenue.
2: Market Prediction Arbitrage Robot
Core Idea
The core idea behind this product is to find situations where the same event is priced differently on different prediction markets. You can lock in profits by buying the cheaper "yes" on one platform and the mispriced "no" on another platform (or selling the "yes" on a platform where the price is too high).
However, it's crucial to clarify that this isn't traditional arbitrage where prices immediately level off. The inefficiency here can persist for hours or even days. The main challenge lies not in "discovering the price difference," but in determining, according to the rules of adjudication, whether they truly represent the same event, and whether you can exit smoothly or must hold until final settlement.
In most cases, it operates in two ways:
- Default arbitrage: This is conducted between two markets that adjudicate the exact same event (or true arbitrage if the settlement conditions are the same).
- Semi-arbitrage: The markets are "almost identical," but the wording differs (greater potential, but also greater risk).
These kinds of products often look like a UI/UX dashboard displaying these price differences, or a Telegram bot sending you all the incorrect pricing information. You then manually determine whether it's a genuine opportunity or an exaggerated offer.
This product is heavy, just like the previous one. There are many places inside where you might make a mistake.
Potential problems
- Same event ≠ same market: wording, ruling standards, time zone, trading volume, who rules and how—these are the number one reasons people lose miserably.
- Liquidity is typically very low: You may see huge price differences, but you might only be able to invest $50 to $100 before the price moves against you.
- Different mechanisms: Order book vs. Automated market maker – Some platforms are based on depth, while others are based on automated market makers, and their pricing behavior also differs.
- Long waiting time: Sometimes you need to wait for the outcome of an event, the price doesn't move, and there is no buying or selling pressure.
- Fees: Transaction fees, slippage within the order book.
- Manipulation and spurious orders: There is false depth in the order book, making you see a non-existent profit margin, and then the price falls back when you execute the trade.
Technical Architecture
Objectively speaking, it's similar to previous robots, but with several crucial differences. Even so, extensive testing is still necessary, as reality can shatter theories.
1. Data scraping
You need real-time data for each market you support. Depending on the platform, this includes:
- Order book: Best bid/ask price, depth, recent transactions.
- Automated Market Maker: Price Simulation.
- Metadata: rules, source of adjudication, expiration date.
2. Event Matching Layer
This is the most important module. It must answer: Is this really the same event? Basically, it should:
- The market is grouped by theme (politics, cryptocurrency, esports, etc.).
- Look for candidate markets that seem to be "about the same thing".
- Partial configuration reliability score.
- Delve into comparative metadata to capture tricky rulings.
3. Brain/Opportunity Calculator
This module calculates the true profit after deducting all fees and actual transaction details. This is because the prediction market often displays a "window dressing" order book with low liquidity. It must calculate the net profit margin:
- In market A, the effective entry price for a size of X dollars (after considering slippage/depth).
- In market B, the effective entry price is X dollars.
- Cost + Gas + Bridging Fee (if required).
- Optimal structure: Buy "yes" here, buy "no" there, or sell where the price is too high.
- You can actually deploy at the maximum scale without compromising profit margins.
4. Risk Filter (Double Check)
The most important factors here are maximum scale and settlement risk indicators. I've heard many cases where rulings contradicted the facts simply because of vague wording. Of course, maturity dates also need to be assessed to avoid locking up liquidity in a single transaction for too long. An interesting approach is to sell before the ruling if you see prices moving in your favor in a market.
5. Telegram delivery layer
As before, don't build a UI/UX at the beginning; just create a Telegram bot. The bot should send:
- Event name + links to the two markets.
- The "yes" and "no" prices on both platforms.
- Net profit margin after deducting expenses.
- Maximum security size based on order book depth.
- Warning messages, such as low liquidity or ambiguous ruling rules.
Promotion methods
The best promotional case studies are real-life examples of how you can profit from your own bot. Include screenshots from both platforms, a clear explanation of your strategy, your plan, and the execution process. Just look at the attention @the_smart_ape received when he announced his bot, and you'll understand…
Once you've built a community around "market inefficiency," even selling the product becomes easy if it truly works. This is especially true in the context of Polymarket and prediction market hype. Compared to many other products, this one should be easier to promote.
3: An aggregator for crawling useful data for traders, creators, and Web3 users.
Core Idea
The core idea of this product is to grab the data you often need to search manually—basic news summaries and other raw information—and deliver it in a clear format to Telegram or a suitable UI/UX dashboard.
So this isn't just another news bot, but an aggregator that consolidates jumbled information from all sources into one place. A crucial point to clarify is that web scraping is more than just parsing websites. If you're simply pulling HTML and forwarding text, it's essentially worthless. The real value, and where AI shines, lies in standardizing and extracting truly important content.
Technical Architecture
The primary goals are stability and providing real value to users. In most cases, it can be used for summary channels—if a piece of code is pushed multiple times, or for important news that you need to know promptly.
Many "trench traders" use bots to see who changed their profile picture or edited something first. It all depends on the sources you care about, and everyone's use case will be different. You can apply it based on your imagination and what you find useful.
Data scraping has always been an "alpha" (advantage), such as parsing specific data and building a dashboard around it. Everyone needs this, so you can also build your personal brand on the X platform.
1. Source Connector (Crawler + API)
You need to use a combination of APIs and web scraping, because some websites don't have APIs.
- Official API.
- RSS or public JSON.
- Web scraping (in areas without APIs).
Each connector must be able to:
- Acquire data at appropriate time intervals.
- Retry if an error occurs.
- Check if the source is down.
- There is a backup auxiliary source.
2. Normalization layer (unifies all content into a single format)
All content must be standardized into a format that is convenient for you. In particular, break it down by event type, such as deployment, unlocking, governance, and exploitation. This will make tracing much easier, while storing the source, entity ID, and other metadata. Without this layer, it's not an aggregator; it's just forwarding raw text.
3. Anti-spam + Clustering Layer
You need logic to merge identical events from different sources. This will eventually give you a clean update that includes all the collected details. If new information appears, update the previous message and send the update to the bot.
4. Scoring + Filtering Layer
This is more personalized, but extremely important. You can assign importance to each update. For example, configure priorities within your bot based on your current trading or focus. If you're trading newly listed projects or low-liquidity trading pairs, prioritize them.
Create a simple rating for each update: High, Medium, Low. The rating can be algorithmically adjusted based on previous ratings, allowing you to add your own manual ratings for each update and fine-tune the priority over time.
5. Storage + Delivery Layer
You need a clear history, plus quick searches via tags or a user-friendly UI/UX. Without a proper database, you won't be able to evaluate updates and filter personal preferences.
The delivery layer should be user-friendly—this is where UI/UX truly matters. Include commands, filters, quick links to the original source, and short summaries.
Promotion methods
The best approach is to build a truly useful dashboard, such as one that tracks closed airdrop sites or specific wallet tracking. All of these involve scraping and parsing data.
The key is to demonstrate how your aggregator correctly filters data and share real-world examples of how you've used it. Choose a niche and sell alerts for that niche—people are always willing to pay for quickly scraping relevant data. Private alerts, trader statistics from API-free platforms—everything can be incorporated into this structure. Finding use cases is up to you.
4: Based on the intelligent agents you build for automated private messaging and promotion.
Core Idea
The core idea of this product is to build an intelligent agent that can find the right people and send them private messages in your name.
A crucial point to clarify is this: this is not a spam bot. Mass mailings would result in account bans, reports, and negative reputation. Its true value lies in building a personalized intelligent agent with appropriate context and precise targeting.
I can attest to this from personal experience. As the founder of the accelerator @arcane_hq, this saves me a lot of time and money, because I often hire business development people just to send cold private messages in my tone.
Potential problems
- Spam risk: If the message is generic and lacks context.
- Location error: If the agent sends a message to the wrong person.
- No memory or no state: If the agent does not remember who it contacted or who replied.
- Data privacy: Because your AI will store a complete database of cases, clients, and scripts.
- The platform's limitations on AI agents: Just like X did. So currently, this idea only really applies to LinkedIn. Perhaps X will allow AI agents in the future, but for now, it's restricted.
Technical Architecture
Objectively speaking, this seems simple to everyone, without complex mathematics or algorithms. But certain aspects make it difficult for the robot to perform.
1. "Your Model" (Your Style and Brain)
Everything that makes you "you" must be placed here. If this foundation is weak, nothing will work. You need to feed everything to the agent:
- Your tone of voice.
- Your product (what you sell, who you sell it to, and what results it brings).
- Your case studies, data, and evidence.
- Forbidden phrases (what you absolutely must not say).
- What to do in uncertain situations.
In this way, private messages have a solid foundational context.
2. Positioning (Ideal Customer Profile + Rule Engine)
Determining who you're writing to is crucial: segment the target audience, their role, location, stage of development, and potential for collaboration. Also, add security rules.
- Do not send emails to competitors, customers, or sensitive categories.
- Send no more than N messages per day.
- If rejected, do not send again.
3. Lead generation (not just data scraping)
Define the sources of potential customers, giving them databases, communities, and structured search logic. Focus on fewer but more relevant leads. Interestingly, even a single lead search itself can be a marketable product.
4. Enriching information (humanized context)
The agent must gather at least 3 to 5 facts about this person. The difference between spam and genuine private messages lies in the context. Automated research:
- What they did.
- What they recently launched or released.
- Whom did they hire?
- What products or audience do they have?
This builds trust and makes people feel that you are a real person (p.S.: Haha, ironically, we are building an AI agent).
5. Message Generator (Private Message Draft)
Final stage: Generate actual messages. The generator takes your style, potential customer context, and your product, and generates 2 to 3 private message variations.
6. Security/Compliance Filter
A module must be added to check for illusory facts, over-selling, and misuse of personal data. AI loves to fabricate things, especially promises—filter those out.
7. Person in the loop (approval required)
Connect to a minimalist UI/UX or Telegram bot. The agent sends drafts, which you approve—this is how a minimum viable product must work. This allows you to understand its behavior and adjust prompts, filters, and lead generation.
8. Follow up on the engine (the most profitable part)
90% of the money is spent on follow-ups, not the first message. As someone with sales experience, this is essential: No response after N days → Follow up #1 → #2 → Then stop. Each follow-up must add small value, not just a simple "Are you there?"
9. Customer Relationship Management + Analytics
It stores who you've contacted, what messages you've sent, their status, replies, tags, and reasons for rejection. Most importantly, it tests prompts and collects and analyzes the data.
- Response rate.
- Positive response rate.
- Appointment call rate.
- Ban or risk signal.
- Which opening line is effective?
- Which call to action can translate into action?
Promotion methods
The best marketing is when you use it yourself—selling and networking can be your own job. You can help others find the right people, and this system works for any marketing agency. It's not just web3—the agent saves you over two hours a day and continuously generates leads. It's almost like setting up a sales process for Wall Street brokers back in the day.
5: Do you really believe I made $17,000 in 5 days?
Yes, there won't be a fifth idea. But there will be something more valuable than you expect.
False expectations are the worst thing you can face on your journey. Building a profitable app in 5 days is simply a pipe dream.
And consider this: if you have zero technical background and zero building experience, even six months would be very difficult. And you've read to the end hoping to hear some secrets, just because I'm Ronin?
I'm Ronin, and for the past 5 years, I've worked 7 days a week, 12 hours a day, sometimes even more. I've been learning programming since I was 12, it's my hobby, and I've won the Mathematical Olympiad multiple times. Even so, believe me, a true understanding has only recently begun to form, and building applications is still very difficult for me.
The desire to make money quickly is one of the biggest problems of this generation. Those who don't shake off this mindset will earn less in the long run than those who are slower but persistent.
How do you feel when you fail after 5 days? Disappointed?
Most successful founders don't really launch their businesses until they're in their 30s or 40s. Most of my audience isn't even 25 yet—what's the rush? Who's stopping you from joining a company to gain experience first?
No, I'm not discouraging you from vibecoding—you can do it 24/7, chase your goals, but it won't happen in 5 days. It won't happen in a week or two—it will take at least 3 to 6 months.
Then you'll face marketing challenges, and you might realize that nobody needs what you've built—no competitor research, no marketing analysis, no idea where to start, zero budget. And then what? Burnout, apathy, disappointment…
I can test products for free because I have my audience, and I'm incredibly grateful for that. Social capital is one of the most valuable assets of the 21st century. AI is developing much faster than humans are building genuine relationships.
Don't set unrealistic expectations; don't demand the same of yourself as a 40-year-old entrepreneur. Focus on quality, focus on gaining experience, focus on building strong connections. If you are ambitious and honest, people will stay with you after your first, second, and even third attempt.
Stay honest, stay consistent, keep trying, and remain open to new relationships.
