100% LLM accuracy–no fine-tuning, JSON only

Hacker News

# Show HN: 100% LLM accuracy—no fine-tuning, JSON only *GitHub:* https://github.com/Mysticbirdie/hallucination-elimination-be... *Paper:* https://github.com/Mysticbirdie/hallucination-elimination-be... --- *Model-agnostic Triad Engine*: JSON domain guide → 100% accuracy across Mistral 7B/Claude/GPT—no fine-tuning. --- ## Results (222 questions, adversarial domain, Gemini 2.0 Flash judge) | Model | Raw | + Triad | ∆ | |-------|-----|---------|---| | Mistral 7B (local) | 22.5% | *99.5%* | +77pp | | Bielik 11B (local) | 21.6% | *88.7%* | +67.1pp | | GPT-5.2 | 26.1% | *100%* | +73.9pp | | Gemini 2.5 Pro | 42.3% | *95%* | +52.7pp | | Claude 4.6 | 45.0% | *100%* | +55pp | | Perplexity Sonar (RAG) | 64.4% | *93.7%* | +29.3pp | Perplexity has live web search—Triad still adds 29.3pp. Gemini 2.0 judge (cross-model). Claude Opus (stricter): raw Claude 14.9% → Triad 95.9%. Zero regressions. *Beyond accuracy:* - Adversarial: Raw Claude accepts false premises 25% → Triad 5% - Consistency: Raw Claude 0% agreement across personas → Triad 100% - Concise: 2.1× shorter responses (473 vs 1,015 chars) *Real-world (Windsurf live codebase):* | Phase | Context | Score | |-------|---------|-------| | No context | — | 40% | | Unstructured docs | — | 40% | | Triad JSON guide | — | *100%* | --- ## Triad Engine Multi-voice layer above any LLM: - λ: Character voice from domain guide - μ: Truth/false enforcement - ν: User calibration - ω: Voice compositor *Only input*: JSON domain guide (what exists/doesn't, agents, norms). No weight changes. Works with any LLM. Applies to medical, legal, compliance domains. --- ## Open source 222 questions, runners (Claude/GPT/Gemini/Mistral), JSON results, guide schema in repo. Happy to answer technical questions.

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, ide · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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