5 Golden Opportunities I Found in the Crypto World Using Vibe Coding
TechFlow Selected TechFlow Selected
5 Golden Opportunities I Found in the Crypto World Using Vibe Coding
Build a robust tech stack capable of efficiently executing your functionality, using the APIs and other tools you need—this depends on exactly what you’re building.
Navigating Web3 tides with focused insightsBy Ronin
Translated by AididiaoJP, Foresight News
2026 will be a turning point—the year AI truly becomes an indispensable tool. We’ll be able to build things that previously required at least ten developers—single-handedly, and for almost no cost (excluding credits purchased for Claude Code). All you need is a concept and absolute clarity about what you want to build.
I’ve long been passionate about building my own products. In fact, I earned $17,000 in five days with my first app.
But while doing research, I identified five of the best crypto tools you can build right now using vibe coding. This article won’t provide ready-to-use prompts or anything you can simply “Ctrl-C & Ctrl-V.” Instead, it offers something far more valuable: the framework and structure behind each idea—the very foundation you need to understand how to build a genuinely 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 sale.
Step One: Building Your Application
Personally, I always begin by researching competitors—to assess whether the idea is truly viable. If you’re not a developer—or lack deep technical experience—this step is non-negotiable.
Then, I open existing live products, browse their documentation, and study how they work. Most often, this involves connecting to required APIs, structuring usage, retrieving needed data, or applying specific filters—for example, scanning broadly for potential arbitrage opportunities.
Once you understand your competitors—and their volume—you can realistically estimate your potential revenue. Building your first idea straight out of your head, with zero exposure, is meaningless. It’s like trading the first memecoin you see on pump.fun—what happens? You lose everything, feel disappointed, conclude the space doesn’t work—when in reality, it’s not the space—it’s your approach.
Alright—we’ve analyzed competitors, their tech stacks, and their marketing methods. What next?
Next, you must minimally structure everything in Notion—or any app you find convenient. I also frequently build dashboards in Figma containing all essential elements—including visual components. Most products can run as Telegram or Discord bots, so visual design is irrelevant at this stage.
If you’re building a crypto product, don’t waste time on UI/UX. Your user base is already accustomed to poor UI/UX. Build something that works first. Then—after earning your first revenue—you can hire a designer to polish the UI/UX.
Our focus is clear: build a robust tech stack capable of efficiently executing your functionality—using the APIs and other tools you need, depending on exactly what you’re building.
Personally, I’ll lay out extremely concrete examples here—ones you’ve almost certainly heard of before—but I haven’t seen anyone fully deconstruct what its technical architecture should look like (including specific go-to-market and consulting strategies).
Let’s dive in.
1: CEX-DEX Arbitrage Bot
Core Idea
The core idea is to identify large price discrepancies between centralized exchanges (CEXs) and decentralized exchanges (DEXs)—large enough that, after accounting for all fees, gas, funding rates, and latency risk, you still retain profit.
But critically: this isn’t as simple as tracking prices and buying on a CEX while selling on a DEX. Often, deposits are disabled—or take too long—and by the time your funds arrive, the price has already converged.
Most such strategies rely on hedging—for example: buying on a DEX while simultaneously shorting perpetual contracts on a CEX (or vice versa). You lock in the spread, then deliver assets or rebalance.
Typically, these products resemble Telegram bots that notify you of all such opportunities—and you manually analyze them. Though seemingly lightweight, this is actually a heavy product with many internal pitfalls.
Potential Issues
- Price update delays and desynchronization, returning inaccurate data.
- Fees: maker/taker fees, funding rates (if hedging via perpetuals), gas, aggregator fees.
- Slippage: dependent on liquidity on DEXs, order book depth on CEXs.
- Partial fills on CEXs—and the need to chase secondary spreads.
- CEX rate limits or API bans—if your data fetching and order placement is too aggressive.
- Withdrawals and deposits: suspensions, delays, compliance hurdles, fees.
- MEV and sandwich attacks: only relevant if your trades are exposed to the public mempool—and even then, only meaningful for whales (no one cares about sub-$1,000 trades; sometimes even sub-$100,000 trades aren’t targeted).
Technical Architecture
Let’s view this objectively—as a set of modular components. What do you actually need to make it function? Even then, all components require rigorous testing—and you’ll inevitably need to fix numerous issues that emerge.
1. Data Collector (Web Scraping)
You need real-time data from both sides—continuously.
- CEX collector: connects to exchange APIs to fetch best bid/ask, order book depth, perpetual funding rates.
- DEX collector: runs simulations—e.g., “How many tokens would I receive if I swapped $X right now?”
- If simulation reveals a spread—that’s your signal. Even 1–5 seconds of delay can eliminate the opportunity entirely.
2. Brain / Opportunity Calculator
This module ingests data and answers one question: “After deducting all fees, gas, slippage, and latency risk—do we still profit?”
It calculates true net profit margin:
- Difference between CEX price and simulated DEX swap result.
- Minus CEX maker/taker fees.
- Minus funding rate (if hedging via perpetuals).
- Minus DEX gas + aggregator fees.
- Minus expected slippage.
- Minus safety buffer (to absorb price movement during execution).
If the margin is tiny—ignore it. Crucially, it must be *real*. Almost all “pretty spreads” vanish once your bot correctly computes net profit margins.
3. Risk Filter (Double-Check)
Even if a trade appears profitable, the bot should re-check everything before notifying you:
Verify gas costs, CEX limits, and whether trade size is too large relative to order book or liquidity pool depth.
4. Telegram Delivery Layer
Set up notifications to deliver trades in user-friendly Telegram format—in real time. Ideally, include direct links to the relevant perpetual contract and DEX—so you waste zero time. Also verify all suspicious touchpoints, letting the bot complete instantly and log results.
5. Security
- An “emergency stop” feature is critical—so the bot shuts down on error and sends you logs.
- Most importantly: restrict your CEX API keys to read-only access only. Disable “trade” and “withdrawal” permissions. Even if someone steals your key, they can’t act.
- And of course—test extensively. When built via vibe coding, countless issues will surface—many requiring fixes. Even experienced developers building this would still fix dozens of issues—be prepared.
Go-to-Market Strategy
The most effective promotion is real-world proof: your own arbitrage wins. Build your own community, establish your personal brand, share your trades. If the product delivers, interest follows organically.
From my experience, promoting a strong product rarely requires spending money. Every successful product I’ve launched was built on under $50,000—or even zero budget. Only once first revenue hits $100k–$1M does liquidity and paid ads become truly necessary.
2: Prediction Market Arbitrage Bot
Core Idea
The core idea is to identify the same event priced differently across prediction markets—and lock in profit by buying the cheaper “Yes” on one platform and the mispriced “No” on another (or selling “Yes” on the overpriced platform).
But critically: this isn’t traditional arbitrage where prices converge instantly. Here, inefficiencies may persist for hours—or even days. The main challenge isn’t “finding the spread”—it’s verifying whether markets truly resolve the same event, and whether you can exit cleanly—or must hold until final settlement.
Most commonly, it operates in two modes:
- Pure arbitrage: Between two markets resolving the *exact same event* (true arbitrage if settlement conditions match).
- Semi-arbitrage: Markets are “almost identical,” but wording differs (more room for profit—and higher risk).
Typically, such products appear as a UI/UX dashboard displaying spreads—or a Telegram bot sending all mispricing alerts. You then manually judge whether it’s a genuine opportunity—or just exaggerated conditions.
Like the previous bot, this is a heavy product—with many points of failure.
Potential Issues
- Same event ≠ Same market: Wording, resolution criteria, time zones, volume, who resolves and how—this is the #1 reason people get wrecked.
- Liquidity is usually tiny: You see huge spreads—but deploying just $50–$100 moves price against you.
- Mechanism differences: Order book vs. AMM—some platforms depend on depth, others on automated market makers—price behavior differs.
- Long wait times: Sometimes you must wait for event resolution, with no price movement or trading pressure.
- Fees: Trading fees, order-book slippage.
- Manipulation & fake orders: Fake depth in order books shows illusory profit—then price drops when you execute.
Technical Architecture
Objectively, it resembles the prior bot—but with several crucial distinctions. Still, extensive testing is mandatory—reality breaks theory.
1. Data Scraping
You need real-time data from every supported market. Platform-specific requirements include:
- Order books: Best bid/ask, depth, recent trades.
- AMMs: Price simulation.
- Metadata: Rules, resolution source, expiry time.
2. Event Matching Layer
This is the most critical module. It must answer: “Is this *truly* the same event?” Essentially, it should:
- Group markets by theme (politics, crypto, esports, etc.).
- Identify candidate markets that “appear about the same thing.”
- Assign confidence scores.
- Deep-compare metadata—catching subtle resolution wording traps.
3. Brain / Opportunity Calculator
This module computes *actual* profit after fees and real execution—because prediction markets often display low-liquidity “decorative” order books. It calculates net profit margin:
- Effective entry price on Market A for $X size (accounting for slippage/depth).
- Effective entry price on Market B for $X size.
- Fees + gas + bridging fees (if needed).
- Optimal structure: Buy “Yes” here, “No” there—or sell “Yes” where overpriced.
- Maximum deployable size without breaking profit margin.
4. Risk Filter (Double-Check)
Here, max size and settlement-risk flags matter most. I’ve heard many cases where vague wording led to resolution outcomes contradicting reality. Also assess expiry time—don’t lock liquidity too long on a single trade. An interesting tactic: if price moves favorably pre-resolution on one market, sell early.
5. Telegram Delivery Layer
As before—skip UI/UX initially. Just build a Telegram bot that sends:
- Event name + links to both markets.
- “Yes”/“No” prices on both platforms.
- Net profit margin after fees.
- Maximum safe size based on order book depth.
- Warnings: Low liquidity, ambiguous resolution rules.
Go-to-Market Strategy
Best promotion: Real-world proof—your own bot’s winning trades. Side-by-side screenshots from both platforms, clear strategy explanation, your plan and execution. See @the_smart_ape’s announcement traction—and you’ll understand…
Once you build a community around “market inefficiency,” selling the product becomes easy—if it works. Especially amid Polymarket hype and broader prediction market enthusiasm. Compared to many other products, this one should be easier to promote.
3: Data Aggregator for Traders, Creators & Web3 Users
Core Idea
The core idea is to scrape data you routinely search manually—basic news summaries and raw information—and deliver it cleanly to Telegram or a well-designed UI/UX dashboard.
So this isn’t just another news bot—it’s an aggregator unifying chaotic information from all sources into one place. Critically: scraping isn’t just parsing websites. Pulling raw HTML and forwarding text adds virtually no value. Real value—the AI “spark”—lies in normalization and extracting what truly matters.
Technical Architecture
Primary goals: stability and delivering *real* user value. Most commonly used for summary channels—if code is pushed multiple times, or critical news breaks.
Many “trench traders” use scrapers to detect who changed their avatar or edited something first. It depends entirely on your sources—and everyone’s use case differs. Apply imagination and personal utility freely.
Data scraping has long been “alpha”—e.g., parsing specific data and building dashboards around it. Everyone needs this—so you can also build your personal brand on X.
1. Source Connectors (Scrapers + APIs)
You’ll need a hybrid of APIs and web scraping—since some sites offer no API.
- Official APIs.
- RSS or public JSON feeds.
- Web scraping (for sites lacking APIs).
Each connector must:
- Fetch data at appropriate intervals.
- Retry on failure.
- Detect if source is down.
- Have fallback backup sources.
2. Normalization Layer (Unify all content into one format)
All content must conform to a unified, convenient format—especially split by event type (e.g., launch, unlock, governance, exploit). This makes tracking vastly easier—and stores source, entity ID, and other metadata. Without this layer, it’s not an aggregator—it’s just raw-text forwarding.
3. Anti-Spam + Clustering Layer
Logic to merge identical events from different sources—delivering clean updates with all collected details. If new info emerges, update prior messages—and push updates via bot.
4. Scoring + Filtering Layer
Highly personalized—but critical. Assign importance scores per update. For example, configure priority inside your bot based on current trades or focus areas. Prioritize newly launched projects or low-liquidity pairs.
Create simple scoring: High/Medium/Low. Algorithmically adjust scores based on historical performance, add manual overrides, and fine-tune priorities over time.
5. Storage + Delivery Layer
Clear historical records + fast search via tags or intuitive UI/UX. Without a proper database, you can’t evaluate updates or filter by personal preference.
Delivery layer must be user-friendly—this is where UI/UX truly matters. Commands, filters, quick links to original sources, concise summaries.
Go-to-Market Strategy
Best approach: Build a genuinely useful dashboard—e.g., tracking shut-down airdrop sites or specific wallet activity. All involve scraping and parsing data.
Key: Demonstrate how your aggregator *correctly* filters data—share real execution cases. Pick a niche—and sell alert services for it. People always pay for fast, relevant data extraction: private alerts, trader stats from no-API platforms—all fit this architecture. Finding the use case is up to you.
4: AI Agent for Automated DMs & Outreach—Built Around *You*
Core Idea
The core idea is to build an agent that identifies the right people—and sends personalized DMs on your behalf.
Critically: This is *not* a spam bot. Mass-blasting equals account bans, reports, reputational damage. Real value lies in building a context-rich, precisely targeted, personalized agent.
I know this firsthand. As founder of accelerator @arcane_hq, this would save me massive time and money—I regularly hire BD reps just to send cold DMs in *my voice*.
Potential Issues
- Spam risk: Generic, context-free messages.
- Mis-targeting: Sending to wrong people.
- No memory/state: Not remembering who was contacted or who replied.
- Data privacy: Your agent stores full case studies, client lists, script databases.
- Platform restrictions on AI agents: Like X’s current policy. So currently, this idea only works on LinkedIn. X *may* allow AI agents later—but not yet.
Technical Architecture
Objectively, this seems simple—no complex math or algorithms. Yet certain aspects make execution surprisingly heavy.
1. “Your Model” (Your Voice & Brain)
Everything that makes you *you* must live here. Weak foundations = total failure. Feed the agent everything:
- Your tone.
- Your product (what you sell, to whom, results delivered).
- Your case studies, data, proof points.
- Banned phrases (absolutely never say these).
- What to do when uncertain.
This grounds DMs in solid contextual foundation.
2. Targeting (ICP + Rule Engine)
Defining *who* you message is critical: niche, role, geography, stage, partnership potential. Add safety rules:
- Never message competitors, clients, sensitive categories.
- Max N messages/day.
- Stop after rejection.
3. Lead Sourcing (Beyond Scraping)
Define lead sources: databases, communities, structured search logic. Focus on fewer—but highly relevant—leads. Interestingly, lead sourcing alone can be a sellable product.
4. Enrichment (Humanizing Context)
Agent must gather at least 3–5 facts about each person. Spam vs. real DMs hinges on context. Automated research includes:
- What they’ve done.
- What they recently launched/released.
- Who they hired.
- What products/audiences they have.
This builds trust—making you feel human (ironic footnote: we’re building an AI agent).
5. Message Generator (DM Drafting)
Final stage: generating actual messages. The generator takes your voice, lead context, and your product—and outputs 2–3 DM variants.
6. Safety / Compliance Filter
Must include a module checking for hallucinated facts, over-promising, PII misuse. AI loves fabricating things—especially promises—so filter them out.
7. Human-in-the-Loop (Approval First)
Connect a minimal UI/UX or Telegram bot. Agent sends drafts—you approve. MVP *must* work this way. This lets you observe behavior—and tune prompts, filters, lead sources.
8. Follow-Up Engine (The Most Profitable Part)
90% of revenue comes from follow-ups—not first messages. As a salesperson, this is mandatory: No reply after N days → Follow-up #1 → #2 → Stop. Each follow-up must add micro-value—not just “Hey, are you there?”
9. CRM + Analytics
Store who you contacted, what you sent, status, replies, tags, rejection reasons. Most importantly: test prompts and collect analytics:
- Reply rate.
- Positive reply rate.
- Meeting booking rate.
- Ban/risk signals.
- Which opener works.
- Which CTA converts.
Go-to-Market Strategy
Best marketing: Use it yourself—sales and outreach become your own job. You help others find the right people; this system fits any marketing agency. Not just Web3—agents save 2+ hours daily and generate consistent leads. Almost like setting up sales workflows for Wall Street brokers back in the day.
5: Do You Really Believe I Made $17,000 in 5 Days?
No—there won’t be a fifth idea. But there *will* be something more valuable than you expect.
False expectations are the scariest obstacle on your path forward. Building a profitable app in 5 days is pure fantasy.
Think about it: If you have zero technical background and zero building experience—even six months would be difficult. And yet you read to the end hoping for secret tips—just because I’m Ronin?
I’m Ronin because for the past five years, I’ve worked seven days a week, twelve hours a day—sometimes more. I started programming at age 12 as a hobby—and won multiple math Olympiads. Even so—believe me—true understanding only began crystallizing recently—and building apps remains hard for me today.
The hunger for fast money is one of this generation’s biggest problems. Those who don’t shed this mindset will earn less long-term than those who move slowly—but steadily.
How do you feel when you fail after five days? Disappointed?
Most successful founders launch their businesses between ages 30–40. Most of my audience hasn’t even turned 25—why the rush? Who stops you from joining a company first to gain experience?
No—I’m not discouraging vibe coding. Do it 24/7. Chase your goal. But it won’t happen in five days. Not in one week—or two. It takes *at least* three to six months.
Then comes marketing—and you might realize no one needs what you built: no competitor research, no go-to-market analysis, no idea where to start, zero budget. What next? Burnout. Apathy. Disappointment…
I can test products for free—because I have an audience. I’m deeply grateful for that. Social capital is one of the most valuable assets of the 21st century. AI advances faster than humans build real relationships.
Don’t set unrealistic expectations. Don’t hold yourself to the same standard as a 40-year-old founder. Focus on quality. Focus on gaining experience. Focus on building strong connections. If you’re ambitious *and* honest—people will stay with you through your first, second, even third attempt.
Stay honest. Stay consistent. Keep trying. Stay open to new relationships.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@DeRonin_
