研究駆動型vibe codingで本当に稼ぐ方法

How to Vibe Code Something That Will Make You Rich Rich Everyone is vibe coding. That's a problem if you don't split yourself from the pack and be among the 0.1% who do it differently. That starts from building stuff that 99.9% of vibe coders won't even try. I want to make an argument that might be uncomfortable: the vast majority of vibe-coded apps will never be worth anything. Not because the builders lack talent. Because they're building in the most crowded, most replicable, most zero-moat space that has ever existed. Less than only 1% of society uses LLMs yet for vibe coding, imagine how things will be when a lot more people are here. You really gotta differentiate. Via building stuff that many people will find extremely hard. This article is about aiming much bigger and adopting a scientific, research-based process to build really hard stuff with a moat. I'll be sharing here, from direct experience, the process of building harder stuff after more than 12,500 commits. As I've done both the easy and the hard stuff, I can tell you that building easy stuff was just a waste of time (except during the initial learning period). PART I: THE MARKET NOBODY TALKS ABOUT When AI coding tools dropped the cost of building software to near-zero, the assumption was that this would democratize entrepreneurship. But not everything will have a moat / a differentiator that is easy to protect. If a weekend project can replicate your product, your product isn't defensible. Most products will be competing on marketing, not on substance. Look at what's actually getting funded, actually getting users, actually creating value. It's not the 500th AI writing assistant. It's things like: - Memory architectures for AI agents that actually persist and learn - Deep research-based infrastructure that will solve a big problem - Domain-specific AI systems that require deep knowledge of a field to even specify/prompt correctly These products have something in common: they're hard to build because they require knowledge that most builders don't have. The complexity isn't just in the code. But in the UNDERSTANDING that informs the code. The intersection papers are the goldmines. Because they will make you think of connected systems more, and WHAT YOU CAN CONNECT, that hasn't been done before. PART II: RESEARCH-DRIVEN VIBE CODING First of all, the strategy isn't "become a PhD researcher." You don't need to write papers, or be a professor, or have a background in the thing you'll be building. BUT You need to READ, in bits if you have to in the beginning, understand the core ideas, and connect the dots until you can translate those ideas into build specifications -> that your LLM can execute. When you try to give such complex ideas to AI, and if you don't know what you are doing, AI will not produce something great. AI just reflects how much you understand of complex problems and has a hard time going beyond that towards a great solution without your guidance. You must follow a process of self-growth about: > the problem that you're aiming to solve > how others tried to solve it > where others ran into problems > how others came closer to solving it > where AI is hallucinating when you try to solve it > and go into the depths of AI's understanding and non-understanding FIVE-PHASE WORKFLOW: Research-First Vibe Coding PHASE 1: DISCOVER — Find the Paper That Solves a Real Problem Start on Hugging Face Daily Papers (huggingface.co/papers). You're scanning for: - papers that solve problems you've personally encountered - papers that introduce new architectures (not just marginal benchmark improvements) - papers at the intersection of two fields (these are the goldmines) PHASE 2: READ — Understand the Paper in 15 Minutes Open the paper on arXiv (free, always). Don't read it linearly. Use this order: - Abstract (30 seconds): first sentence = problem, last sentence = claim - All figures and tables (2 minutes): the architecture diagram tells you 80% of what you need - Introduction + Conclusion (5 minutes): what's novel and what are the honest limitations - Methodology (5-7 minutes): focus on the system design, skip the math - GitHub check (30 seconds): is there code? Does it run? After this, you should be able to say: "This paper solves X by doing Y. To build it, I'd need components A, B, and C." OR USE AN LLM: Upload the PDF. Let those parts given to you by your LLM in your terminal or AI chat. See everything like a puzzle when doing research. Each discovery, methodology, understanding, and "aha" moment is a step toward being able to guide your AI to ship something 99.9% of people won't be able to ship. PHASE 3: CONNECT THE PUZZLE PIECES — Seeing the Entire Landscape Connected Papers (connectedpapers.com) becomes your secret weapon. Drop your paper's arXiv URL, and it builds a visual graph of related research. What you're looking for: - the biggest darkest nodes (recent, highly-cited papers) - tight clusters (sub-fields you could build in) - isolated nodes at the edges (techniques from adjacent fields nobody has applied yet) - Prior Works (foundational papers that started this research direction) - Derivative Works (the cutting-edge extensions and surveys) PRO TIP: You can also build your own graphs by creating a graph directly through your own terminal as you find more interesting research papers, and update it as you find more cool discoveries. PHASE 4: BUILD — Ship with Research-Backed Confidence Now you can start to vibe code that complex thing. The more you understand the field, the more your prompts will differ from the generic vibe coder. Instead of "build me a RAG system," you'll write: "Implement a retrieval pipeline based on the RAPTOR architecture. Use recursive abstractive processing to cluster and summarize chunks into a tree. Retrieval queries against all tree levels, not just leaf nodes." This will lead to completely different outputs. The specificity of your instructions, informed by your research, produces code that is architecturally distinct from what anyone else without that understanding ships. PHASE 5: DON'T QUIT If you're working on a hard problem, expect to be tested; it won't be easy. That will be your moat as you persist. PART III: THE TRANSFORMATION NOBODY EXPECTS Research-Driven Vibe Coding Changes YOU, Not Just Your Products. When you read a paper on memory architectures, you don't just learn how to build a memory system. You learn how the researchers THINK about memory. You start to understand the mind. You learn the vocabulary that lets you communicate with people working at the frontier. You'll grow expertise, and that expertise will carry you onwards towards solving harder problems. That's where the biggest opportunities are.