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ARES Dashboard – Open-Source AI Red-Teaming and Governance Platform

Hacker News

ARES Dashboard – Open-Source AI Red-Teaming and Governance Platform

Hi HN, I’m sharing ARES Dashboard v1.0.0, an open-source platform for AI red-teaming, evaluation, and governance. ARES is built to support structured red-team campaigns against LLMs, with a focus on safety, misuse resistance, and enterprise-style controls rather than one-off prompt testing. Key features: -Campaign-based AI red-teaming workflows Multi-tenant architecture with role-based access control -Audit logging and compliance-oriented design -Demo mode for users without API keys -Extensive documentation covering security boundaries, threat models, and operational practices The project is early but intentionally designed with production and enterprise readiness in mind. Current gaps are mostly operational (monitoring, load testing, production deployments), which are the next priorities. GitHub: https://github.com/Arnoldlarry15/ARES-Dashboard I’d really appreciate feedback from folks working in AI safety, security, infra, or governance — especially around real-world red-teaming workflows and operational concerns. Thanks for taking a look!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
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 · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
18%18% 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.

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