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[ICML 2025] Built a hallucination detector and editor that outperforms OpenAI o3 by 30% - now open-source as an AI trust and safety layer with 300+ GitHub stars
r/SideProject
7/21/2025
Opinion Analysis
The mainstream opinion in the post is positive towards the development and open-sourcing of Pegasi Shield. The author shares their journey from a weekend project to a well-received open-source tool with significant contributions to the field of AI safety. There are no conflicting or controversial opinions in the comments provided, as only one comment is present which is supportive. The author is open to feedback and contributions, indicating a community-oriented approach.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Pegasi Shield | https://github.com/pegasi-ai/shield | AI Safety and Trust Layer | Open-source safety toolkit for LLMs, detects and edits hallucinations, fact-checks outputs, and masks PII |
USER NEEDS
Pain Points:
- Hallucinations in LLMs for regulated use cases
- Need for reliable and safe LLMs
Problems to Solve:
- Ensuring the reliability and safety of LLMs
- Detecting and correcting hallucinations in LLM outputs
Potential Solutions:
- Using Pegasi Shield for prompt injection scanning, fact-checking, and PII masking
- Implementing FRED for enhanced detection and editing of hallucinations
GROWTH FACTORS
Effective Strategies:
- Open-sourcing the tool to gain community support and contributions
- Developing and sharing research to establish credibility and attract users
Marketing & Acquisition:
- Creating YouTube videos on related topics to increase visibility
- Engaging with Fortune 100 companies for production use
Monetization & Product:
- Raising funding to support development and expansion
- Continuously updating the repo based on feedback
User Engagement:
- Seeking feedback on roadmap priorities
- Encouraging contributors and stars on GitHub