Pachinko Coding—What They Don't Tell You About Building Apps with Large Language Models | Alan Cyment
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🎯 Summary
Comprehensive Summary: Coding with AI – From “Vibe Coding” to “Mecha Coding”
This episode of the Scrum Master Toolbox podcast, featuring consultant and trainer Alan Simont, dives deep into the practical, often frustrating, and ultimately transformative experience of coding with Large Language Models (LLMs). The discussion moves beyond the hype to explore the psychological pitfalls and emerging best practices for leveraging AI in software development.
1. Main Narrative Arc and Key Discussion Points
The conversation follows Alan’s journey from initial skepticism and disappointment with early AI coding tools to developing complex applications rapidly. The arc moves from:
- Defining “Vibe Coding”: Alan critiques the term, equating it to “Thermomix coding”—where the user passively receives a result without understanding or thinking, leading to disappointment.
- The Addictive Cycle: Alan details his early, frustrating loop of prompting, receiving flawed code, copying errors back, and repeating—a cycle he likens to Pachinko addiction, driven by the unfulfilled promise of instant success.
- The Shift to Abstraction: The turning point came when Alan realized AI could function as a new, higher level of abstraction, similar to the evolution from assembly to high-level languages.
- “Mecha Coding” (Exoskeleton): He redefines productive AI use as “Mecha Coding,” where the developer acts as the pilot controlling a powerful external system (the LLM) to expand their capabilities, requiring proficiency in piloting (prompting/guiding).
- Actionable Success: Alan shares the successful development of a personalized iOS birthday reminder app in less than a day, illustrating how strategic prompting overcame technical hurdles.
2. Major Topics, Themes, and Subject Areas Covered
- AI-Assisted Coding Methodologies: Vibe Coding, Thermomix Coding, Pachinko Coding, Mecha Coding.
- Psychology of AI Interaction: Addiction cycles, cognitive load, the frustration of being “wise and stupid at the same time.”
- Software Development Evolution: AI as the next major level of abstraction (comparing it to the shift from bits to assembly to OOP).
- Practical Application: Developing personal tools (birthday reminder app) and client-side data processing (JIRA/Azure DevOps queries in Python).
- Tooling: Mention of ChatGPT (early versions), Adept (open-source preference), and Cloud Code (current use due to subsidized tokens).
3. Technical Concepts, Methodologies, or Frameworks Discussed
- Levels of Abstraction: The core framework used to understand AI’s role in development, allowing developers to bypass “accidental complexity.”
- Inherent vs. Accidental Complexity: AI helps developers bypass the accidental complexity (e.g., environment setup, specific language syntax) to focus on the inherent complexity of the business problem.
- TDD (Test-Driven Development): Alan explicitly instructs the LLM to adhere to TDD principles.
- YAGNI (You Ain’t Gonna Need It): A key principle used in later, successful prompting to force the AI to generate the simplest possible solution.
- Convention Files (e.g.,
conventions.md): Pre-prepared prompt instructions defining hygienic coding standards (linting, testing, refactoring checks).
4. Business Implications and Strategic Insights
- Productivity Gains for Non-Experts: Alan, despite not being an expert in Swift, rapidly built a functional iOS app, suggesting AI democratizes development for domain experts who lack deep coding proficiency in specific stacks.
- Cost vs. Value: The realization that expensive models ($2.50 per round) were not inherently better than cheaper ones, and that high token spending ($20+ per day) often resulted in unmaintainable, complex code (“drunk PhD with amnesia”).
- Focus Shift: Strategic value moves from mastering syntax and tooling overhead (accidental complexity) to defining clear requirements and architectural decisions.
5. Key Personalities, Experts, or Thought Leaders Mentioned
- Alan Simont: The guest, sharing his hands-on, evolving experience.
- Vasco: The host, framing the discussion and providing context.
- Yuval Noah Harari: Mentioned in reference to the concept of humans becoming “slaves” to technology (the potato analogy).
- Martin Fowler: Quoted regarding the potential for AI to represent a new level of abstraction in software development.
6. Predictions, Trends, or Future-Looking Statements
- Mecha Coding as the Future: The successful model involves the developer acting as a proficient pilot, guiding a powerful tool, rather than passively receiving output.
- Limitations for Enterprise: Alan explicitly states that this highly abstract, “pestering” approach might not work for enterprise-grade software, suggesting complexity requires more rigorous human oversight.
7. Practical Applications and Real-World Examples
- Personal Project: Developing a simple, custom birthday reminder app with “pestering mode” for gift reminders, which previously stalled when attempted with standard development practices.
- Client Work: Using Python agents to query and process data from JIRA/Azure DevOps, even when the developer wasn’t deeply familiar with the specific Python libraries required for the task.
8. Controversies, Challenges, or Problems Highlighted
- The Pachinko Trap: The primary challenge is the psychological loop where the user invests time and money chasing a successful output, leading to wasted effort on overly complex or incorrect code.
- Code Quality Degradation: Uncontrolled AI output often leads to “horribly complicated, complex” code that the user cannot easily fix or
🏢 Companies Mentioned
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💬 Key Insights
"What I think is that maybe the LLM just has less information, is less proficient in certain tech stacks than another."
"So, reminding the agent that he or she, it's its own CI, and that it should never let any kind of error appear because it starts bleeding, right?"
"For coding well with LLM, it actually pays off to take that level of abstraction, right? So that you're not worrying about renaming methods like you said or detailed refactoring solutions, but you're just saying, 'I want this done, make it simple,' right? Like YAGNI."
"I love the difference between inherent and accidental complexity. But the inherent complexity is what the real complexity of making an application... And then you have all the accidental complexities, which are like the details of the unnecessary complexity that are brought about by the UX of software development languages."
"I need to explicitly tell the LLM, 'Give me the options. Don't code,' and 'Explain me the pros and cons of each of the options.'"
"I call it mecha coding, mecha or exoskeleton. It's those robots where you need a person or a Lego—I mean, like a thinking being—to be in control, but you are like inside the robot, and the robot gives you powers like strength or weapons or speed that you wouldn't have on your own. But you need to be proficient in the use—I mean, in piloting a fighter aircraft, right?"
📊 Topics
#artificialintelligence
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#generativeai
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#startup
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#aiinfrastructure
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