--- title: "stealing-ur-feelings vs OML-1.0-Fingerprinting" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/noahlevenson-stealing-ur-feelings-vs-sentient-agi-oml-1-0-fingerprinting" tools: ["noahlevenson-stealing-ur-feelings", "sentient-agi-oml-1-0-fingerprinting"] --- # stealing-ur-feelings vs OML-1.0-Fingerprinting *GraphCanon updated Aug 23, 2026* ## Verdict Pick stealing-ur-feelings if a deep learning-powered AR experience that analyzes facial expressions and emotions to highlight issues of privacy in emotional surveillance; pick OML-1.0-Fingerprinting if oML-1.0-Fingerprinting focuses on leveraging fingerprinting techniques for the creation of open-source, monetizable AI models that ensure user fidelity and loyalty. [stealing-ur-feelings](https://github.com/noahlevenson/stealing-ur-feelings) reports 927 GitHub stars, 32 forks, and 13 open issues, last pushed Apr 26, 2023. [OML-1.0-Fingerprinting](https://github.com/sentient-agi/OML-1.0-Fingerprinting) has 3.5k stars, 232 forks, and 11 open issues, last pushed Jan 23, 2025. Figures are from public GitHub metadata via [stealing-ur-feelings's repository](https://github.com/noahlevenson/stealing-ur-feelings) and [OML-1.0-Fingerprinting's repository](https://github.com/sentient-agi/OML-1.0-Fingerprinting). | | [stealing-ur-feelings](/tools/noahlevenson-stealing-ur-feelings.md) | [OML-1.0-Fingerprinting](/tools/sentient-agi-oml-1-0-fingerprinting.md) | | --- | --- | --- | | Tagline | Deep learning-powered AR experience analyzing facial reactions | OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI | | Stars | 927 | 3,498 | | Forks | 32 | 232 | | Open issues | 13 | 11 | | Language | JavaScript | Python | | Adopt for | A deep learning-powered AR experience that analyzes facial expressions and emotions to highlight issues of privacy in emotional surveillance. | OML-1.0-Fingerprinting focuses on leveraging fingerprinting techniques for the creation of open-source, monetizable AI models that ensure user fidelity and loyalty. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Computer Vision, Speech & Audio | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [stealing-ur-feelings](/tools/noahlevenson-stealing-ur-feelings.md) | [OML-1.0-Fingerprinting](/tools/sentient-agi-oml-1-0-fingerprinting.md) | | --- | --- | --- | | Days since push | 1192d | 577d | | Open issues (now) | 13 | 11 | | Stars delta | Unknown | -3 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/noahlevenson-stealing-ur-feelings/trust.md) | [trust report](/tools/sentient-agi-oml-1-0-fingerprinting/trust.md) | ## Decision facts: stealing-ur-feelings - **Adopt for:** A deep learning-powered AR experience that analyzes facial expressions and emotions to highlight issues of privacy in emotional surveillance. ## Decision facts: OML-1.0-Fingerprinting - **Requirements:** Min 4 GB RAM; Should be used with Python environment due to its primary language being Python. - **Adopt for:** OML-1.0-Fingerprinting focuses on leveraging fingerprinting techniques for the creation of open-source, monetizable AI models that ensure user fidelity and loyalty. ## Choose when ### Choose stealing-ur-feelings if… - stealing-ur-feelings is primarily JavaScript; OML-1.0-Fingerprinting is Python. - Tags unique to stealing-ur-feelings: ai, ar, art, augmented-reality. - Also covers Computer Vision, Speech & Audio. - When planning to explore the ethical implications of AI-driven emotive surveillance technology. ### Choose OML-1.0-Fingerprinting if… - OML-1.0-Fingerprinting is primarily Python; stealing-ur-feelings is JavaScript. - Requirements: Min 4 GB RAM; Should be used with Python environment due to its primary language being Python.. - Tags unique to OML-1.0-Fingerprinting: fine-tuning, fingerprint, loyalty, oml. - Also covers Evaluation & Observability, Model Training. - When aiming to establish a direct connection with end-users through unique identification (fingerprinting) for enhancing personalized interactions. ## When NOT to use stealing-ur-feelings - If your project requires a robust tool for commercial facial emotion analysis without the context of privacy concerns and artistic interpretation. - When seeking real-time, production-grade emotional recognition software designed for user studies or market research purposes. ## When NOT to use OML-1.0-Fingerprinting - If strict privacy policies and regulations prohibit the implementation of fingerprinting techniques, as this tool specifically utilizes such methods. - When focusing on non-loyalty-based customer relationships, considering OML-Fingerprinting is tailored for establishing loyal user bases through unique identification technologies. - In environments where monetization isn't a priority; if your project aims to avoid any form of pay-per-use or subscription models that this tool could support. ## Common questions ### What is the difference between stealing-ur-feelings and OML-1.0-Fingerprinting? stealing-ur-feelings: Deep learning-powered AR experience analyzing facial reactions. OML-1.0-Fingerprinting: OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose stealing-ur-feelings over OML-1.0-Fingerprinting? Choose stealing-ur-feelings over OML-1.0-Fingerprinting when stealing-ur-feelings is primarily JavaScript; OML-1.0-Fingerprinting is Python; Tags unique to stealing-ur-feelings: ai, ar, art, augmented-reality; Also covers Computer Vision, Speech & Audio; When planning to explore the ethical implications of AI-driven emotive surveillance technology. ### When should I choose OML-1.0-Fingerprinting over stealing-ur-feelings? Choose OML-1.0-Fingerprinting over stealing-ur-feelings when OML-1.0-Fingerprinting is primarily Python; stealing-ur-feelings is JavaScript; Requirements: Min 4 GB RAM; Should be used with Python environment due to its primary language being Python.; Tags unique to OML-1.0-Fingerprinting: fine-tuning, fingerprint, loyalty, oml; Also covers Evaluation & Observability, Model Training; When aiming to establish a direct connection with end-users through unique identification (fingerprinting) for enhancing personalized interactions. ### When should I avoid stealing-ur-feelings? If your project requires a robust tool for commercial facial emotion analysis without the context of privacy concerns and artistic interpretation. When seeking real-time, production-grade emotional recognition software designed for user studies or market research purposes. ### When should I avoid OML-1.0-Fingerprinting? If strict privacy policies and regulations prohibit the implementation of fingerprinting techniques, as this tool specifically utilizes such methods. When focusing on non-loyalty-based customer relationships, considering OML-Fingerprinting is tailored for establishing loyal user bases through unique identification technologies. In environments where monetization isn't a priority; if your project aims to avoid any form of pay-per-use or subscription models that this tool could support. ### Is stealing-ur-feelings or OML-1.0-Fingerprinting more popular on GitHub? OML-1.0-Fingerprinting has more GitHub stars (3,498 vs 927). Stars measure visibility, not whether either tool fits your constraints. ### Are stealing-ur-feelings and OML-1.0-Fingerprinting open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to stealing-ur-feelings or OML-1.0-Fingerprinting? GraphCanon lists graph-backed alternatives at [stealing-ur-feelings alternatives](/tools/noahlevenson-stealing-ur-feelings/alternatives) and [OML-1.0-Fingerprinting alternatives](/tools/sentient-agi-oml-1-0-fingerprinting/alternatives) ([stealing-ur-feelings markdown twin](/tools/noahlevenson-stealing-ur-feelings/alternatives.md), [OML-1.0-Fingerprinting markdown twin](/tools/sentient-agi-oml-1-0-fingerprinting/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/noahlevenson-stealing-ur-feelings-vs-sentient-agi-oml-1-0-fingerprinting.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, stealing-ur-feelings or OML-1.0-Fingerprinting? stealing-ur-feelings: Dormant. OML-1.0-Fingerprinting: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for stealing-ur-feelings and OML-1.0-Fingerprinting? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [stealing-ur-feelings trust report](/tools/noahlevenson-stealing-ur-feelings/trust); [OML-1.0-Fingerprinting trust report](/tools/sentient-agi-oml-1-0-fingerprinting/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=noahlevenson-stealing-ur-feelings`](/api/graphcanon/graph?tool=noahlevenson-stealing-ur-feelings) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_