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I stopped learning while coding with AI — so I’m building a tool to help devs learn while shipping
r/indiehackers
6/30/2025
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| CodeRed | https://codered.yashv.me | Developer Tools | Analyzes commits and patterns to help developers understand mistakes, spot scalability issues, identify anti-patterns, suggest learning topics, and evaluate feature value. |
| PeerPush | https://peerpush.net | Community Building | Helps connect builders and get more eyes on products. |
USER NEEDS
Pain Points:
- Developers lose deeper understanding and technical growth when relying on AI tools for coding.
- Lack of reflection and deeper questioning unless something breaks.
- Difficulty in identifying knowledge gaps and learning contextually while shipping code.
- Privacy concerns when sharing codebases with third-party tools.
Problems to Solve:
- How to maintain learning and technical growth while using AI for productivity.
- How to identify anti-patterns, scalability issues, and knowledge gaps in AI-assisted coding.
- How to receive personalized learning suggestions without disrupting workflow.
- How to build trust when sharing codebases for analysis.
Potential Solutions:
- CodeRed: A tool that analyzes commits and patterns to provide contextual learning suggestions.
- Building a profile of developer knowledge over time to spot gaps.
- Focusing on solo devs and open-source projects initially to address privacy concerns.
- Creating product demos and videos to clarify functionality and build trust.
GROWTH FACTORS
Effective Strategies:
- Building in public to share progress and gather feedback.
- Focusing on a relatable problem to ensure product-market fit.
- Iterative development with an early waitlist to gauge interest.
Marketing & Acquisition:
- Leveraging community platforms like Reddit for initial outreach.
- Using waitlists to build an early user base and keep them informed.
- Partnering with community tools like PeerPush for cross-promotion.
Monetization & Product:
- Potential freemium model implied by early free access and future plans.
- Personalization as a key differentiator from generic AI tools.
- Addressing privacy concerns by targeting solo devs and open-source projects initially.
User Engagement:
- Actively responding to feedback and questions on social platforms.
- Encouraging waitlist sign-ups with no-spam promises.
- Planning product demos and videos to engage potential users.