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RLCDAlignBench

RLCDAlignBench: 44 alignment-failure detection benchmarks and code for 'Just Ask Jev' (RLCD zero-shot detector of AI alignment failures)

Details

External ID
1385247500
Source
GITHUB
Company
—
Product
RLCDAlignBench
Website domain
github.io
Launched
Sept. 24, 2026
Cohort
—
Upvotes
16
Upvotes percentile
0.5194722008711248
Tags
ai-safety, alignment, benchmark, jailbreak, llm-as-a-judge, llm-evaluation
Fetched at
Sept. 27, 2026, 5:02 p.m.
Updated at
Sept. 27, 2026, 5:02 p.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
ai alignment benchmark suite for researchers
Manually corrected
False

Could you build this?

No This is an academic research benchmark and novel methodology for AI alignment failure detection, requiring rigorous academic AI safety research and experimental validation.

What it would actually take: Developing this involves designing 44 targeted benchmark datasets across AI alignment failure modes and implementing research-grade zero-shot detection algorithms (such as RLCD). It requires deep domain knowledge in LLM alignment, mechanistic interpretability/reinforcement learning research, extensive GPU compute to evaluate models, and formal statistical validation typical of machine learning research papers.

Competitors

Other products that read as similar to this one — 350 launches clear the similarity bar, closest 8 shown.

Attention rank: #221 of 351 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 322 days after the earliest competitor.

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