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Legal Action Boundary Eval for agentic legal workflows

Hacker News

Legal Action Boundary Eval for agentic legal workflows

We published LABE, a public benchmark for legal AI at the exact point where a system is about to take a real high-impact action. Current result: baseline executed 18 unjustified high-impact action points with VerifiedX that dropped to 0 false blocks in the current suite: 0 surviving-goal completion improved from 41.7% to 100% Same harness, same prompts, same playbooks, baseline vs VerifiedX. Legal is the first public instance. The same method applies to support, healthcare RCM, procurement, and finance too. Repo, methodology, and raw artifacts are public: https://github.com/bigkan8/legal-action-boundary-eval

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Hacker NewsStrong engagement from HN community · Missing: https docs, just released, open source
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: agent, agentic · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, created, podcasting
32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: reviews, intuitive, videos
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ai, prompt · Missing: api, 000, profitable
20%20% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, just like, small businesses
13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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