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Ilan Kadarleft a comment
Would love to hear more feedback on the product and interesting use-cases
PluraiVibe-train evals and guardrails tailored to your use case
Ilan Kadarstarted a discussion
Where SLMs beat GPT-5
We’ve been seeing a consistent pattern across agent systems: GPT-5 works well as a judge on average cases— but breaks down on edge cases and policy boundaries. That’s exactly where reliability matters. In our recent work, we took a different approach: Generate adversarial edge cases from the spec Resolve ambiguity via multi-agent debate Train a task-specific small model (SLM) on that data...
Vibe training for AI agent reliability. Describe what your agent should and should not do — Plurai generates training data, validates it, and deploys a custom model in minutes. It feels like vibe coding, but for evaluation and guardrails.
No labeled data. No annotation pipeline. No prompt engineering. Under the hood, small language models deliver sub 100ms latency, 8x lower cost than GPT as judge, and over 43% fewer failures. Always on, not sampled. Built on published research (BARRED).
PluraiVibe-train evals and guardrails tailored to your use case
Ilan Kadarleft a comment
Hey Product Hunt, Ilan from Plurai here. We spent the last year on a research problem: can you train a production-grade eval or guardrail from just a task description, no labeled data, no annotation pipeline? Turns out you can. We call it vibe-training. Most teams today rely on LLM as a judge. It never fully converges, breaks on edge cases, and at 100ms per call it collapses economically at...
PluraiVibe-train evals and guardrails tailored to your use case