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PrismShine
Self-hosted anti-hallucination verdict for LLM apps
PrismShine is a self-hosted anti-hallucination verdict engine. It runs cause-side forensics on runtime evidence, then grounds the answer against your preload — one auditable ShineVerdict with a named resolution_gate. Apache-2.0. pip install prismshine.
Hey Product Hunt — maker here.
I got tired of “sounds right” answers slipping past score-only checkers. Most detectors only see the final text. A lot of failures start earlier — empty retrieval, a swallowed tool error, a stale cache — then the model fills the gap fluently.
PrismShine is a self-hosted anti-hallucination verdict engine: cause-side forensics on runtime evidence + grounding against your preload → one auditable ShineVerdict (decision + named resolution_gate + evidence_hash).
Not a prompt-injection firewall. Not an agent runtime. It verifies answers against evidence.
Public receipt vs HHEM-2.1-Open on HaluEval (2026-07-20_run4_onnx): B1 F1 0.831 vs 0.746 · fabricated numbers B2 1.0 / 0 FP · ~90 ms · 0 LLM on the fast path.
Try it:
pip install "prismshine==0.2.2"
prismshine verify --demo
Landing: https://www.insightits.com/produ...
Interactive demo (no API key): https://insightitsgit.github.io/...
GitHub: https://github.com/insightitsGit...
Honest limit: PASS ≠ world-true — grounded in the preload you provided, not the whole world.
Question for the community: where would you wire this first — after the LLM node, before generation (halt empty retrieval), or both? And what’s the worst false positive you’ve hit with other checkers?
About PrismShine on Product Hunt
“Self-hosted anti-hallucination verdict for LLM apps”
PrismShine was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #45 on the daily leaderboard. PrismShine is a self-hosted anti-hallucination verdict engine. It runs cause-side forensics on runtime evidence, then grounds the answer against your preload — one auditable ShineVerdict with a named resolution_gate. Apache-2.0. pip install prismshine.
On the analytics side, PrismShine competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how PrismShine performed against the three products that launched closest to it on the same day.
Who hunted PrismShine?
PrismShine was hunted by Amin Parva. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of PrismShine including community comment highlights and product details, visit the product overview.
Amin Parva