Open-Source Accessibility Testing for the Web

Hacker News

Our team at Clym built an open-source Accessibility Testing tool to help developers and auditors identify and fix web accessibility issues, combining automated and manual testing in one place. Our tool integrates the open-source axe-core[1] library for automated testing against WCAG 2.0, 2.1, 2.2 and EN 301 549 standards. For issues that automation can't reliably detect, it includes manual review workflows that guide testers through contextual checks (like keyboard navigation and visual contrast validation). It can also generate Accessibility Conformance Reports (ACRs) in multiple formats and allows teams to define their own rules, mappings and remediation steps through a simple configuration layer. You can view and contribute to the project on our github[2] or download the app for Mac, Windows and Linux on our website[3]. We'd love feedback from developers or accessibility testers — especially around how you combine automated and manual checks today, and what integrations would make this more useful. [1] https://github.com/dequelabs/axe-core [2] https://github.com/clymio/accessibility-tools [3] https://www.clym.io/products/accessibility-tools

Visit product View on Hacker NewsOct 23, 2025

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

81%81% 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

76%76% 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: ide, io · Missing: https docs, excited, just released

48%48% 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 · Missing: mobile apps, ios, personal

40%40% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

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

Incorrect prediction on native model

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