:HUGO_URL: https://quantcodedenny.com/posts/vibe-coding/

Expert mindset for vibe coding

• Embrace imperfection: treat the LLM as a co-pilot, not a guarantee.
• Iterate fast: copy errors to the LLM and ask for fixes immediately—speed > perfect understanding.
• Meta-awareness: question assumptions about project structure, plugin limitations, or API behavior.
• Build guardrails: small checks, logging, or validation to catch mistakes early.
• Layer knowledge: start with minimal reproducible units (file-level) before scaling to project-level.
• Document gaps: track behaviors, limitations, and “unknown unknowns” to avoid repeating mistakes.
• Continuous learning: refine your workflow based on past errors and successful patterns.
• Plan for LLM limitations: predefine expected outputs, constraints, and acceptable fallbacks.

Technical challenges

• Multiple versions: functions may be undefined or unsupported across versions.
• Understanding conventions: e.g., Hugo generates files into the `docs` folder, not `content`.
• Lack of defensive coding: errors propagate, making debugging harder.
• ox-hugo 0.12.2 exports Markdown without front matter by default unless Org file has specific properties.
• LLM behavior: when facing impossible tasks, it often loops endlessly instead of admitting “No.”
• Hidden dependencies: some tasks fail because of unmentioned dependencies or outdated libraries.
• Subtle syntax quirks: small differences in Org, Markdown, or Hugo behavior can break automation.

Gaps, blind spots & workflow caveats

• Works well for individual files, but not full project structures.
• [#A] You don’t know what you don’t know—and the LLM may not tell you.
• Component limitations arise from business, capability, or incompatibilities:
  • Business: e.g., Twitter free API only allows pulling 100 posts/day.
  • Capability: e.g., Emacs plugin (ox-hugo) only supports Markdown blocks in Org files.
  • Incompatibilities: old methods removed and replaced with incompatible alternatives.
• Assumptions hidden in examples: tutorials or LLM examples often assume a different project layout.
• Don’t overanalyze error messages; capture them and ask the LLM to propose fixes.
• Recognize impossible tasks early—stop LLM loops.
• Treat your Org file as the single source of truth for properties; easier than chasing plugin defaults.
• Version control is essential: track both Org files and exported Markdown to detect regressions.
• Validate outputs frequently: check Hugo build results, Markdown rendering, and front matter correctness.
• Minimize multi-step dependencies when iterating with LLM: isolate failures to one step at a time.
• Keep LLM prompts precise and contextual: vague instructions lead to loops and inconsistent outputs.

:HUGO_URL: https://quantcodedenny.com/posts/blogging/

Basic Intro && Set the context for LLM’s collobration

I am an experienced infra engineer (~20 years) with a personal project: https://quantcodedenny.com, hosted on GitHub Pages (repo: https://github.com/dennyzhang/quantcodedenny.com/).

I focus on long-term investing in high-tech US stocks. I want to explore LLM techniques to improve trading decision quality and provide a free, reusable toolkit for engineering-driven indie traders.

Target Audience

• Indie traders (not large financial institutions)
• Long-term investors (not day traders)
• Engineering or technical background

Current Progress

• Blog hosted on GitHub Pages with an initial feature: stock sentiment analyzer(https://quantcodedenny.com/posts/llm-stock-sentiment/)
• Targeting long-term tech investors with engineering backgrounds

Task

You are a world-class entrepreneur, market analyst, and product strategist. Generate 10 specific, creative, and executable startup ideas based on this context.

:HUGO_URL: https://quantcodedenny.com/posts/llm-tools/

0 Intro

This tool empowers engineers to automate stock sentiment analysis with precision and speed.

It combines two core capabilities: parsing recent news headlines to extract market sentiment and insights, and parsing SEC filings to surface key financial and risk information. Both streams are fed into a configurable LLM pipeline, allowing you to run fast local tests with lightweight models or perform high-accuracy production analysis. Designed for modularity and reuse, it integrates seamlessly into your workflows—turning raw data into actionable insights without manual reading.