by ai-in-pm · Agent Tool · ★ 291
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git clone https://github.com/ai-in-pm/Titans---Learning-to-Memorize-at-Test-Time.gitTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 291 |
| Forks | 53 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 57/100 |
| Last Updated | 2026-06-15 |
| Created | 2025-01-16 |
| Platforms | python |
| Est. Tokens | ~13k |
🧠 Titans: Learning to Memorize at Test Time An interactive multi-agent demonstration platform for the landmark Titans architecture — the first neural network to learn how to memorize at test time. ✨ What Makes This Special The Titans paper introduces a groundbreaking memory architecture that learns what to remember during inference — no more fixed context windows.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: