ProductSignals API – product reliability signals from public sources

Hacker News

Hi HN, I built ProductSignals API, a small API that aggregates publicly available signals (Trustpilot, Reddit, ArnaqueOuFiable, Gridinsoft) to surface potential reliability or trust-related signals about products or merchants. The goal is not to give verdicts or advice, but to provide deterministic, machine-consumable signals that can be used as an input in automated workflows (onboarding checks, internal tooling, monitoring, etc.). Key points: - Single JSON endpoint (POST /v1/product-signals) - Signals only (no recommendations, no scoring guarantees) - Public data sources only - Subscription-based, quota-limited - No customization per customer Docs and examples: https://productsignalsapi.analyses-web.com/docs.html Pricing: https://productsignalsapi.analyses-web.com/pricing.html This is part of a small suite of similar APIs (risk / abuse signals). I’m mainly interested in feedback from people who build automated systems around trust, risk, or onboarding. Happy to answer technical questions.

Visit product View on Hacker NewsJan 28, 2026

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

83%83% 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

66%66% 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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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 · Strong signals: subscription · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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