Au

Audit-a11y – audit websites for accessibility issues

Hacker News

Audit-a11y – audit websites for accessibility issues

recently there was someone on my BBS who related a story about their impending disability that was already affecting their accessibility capabilities as a developer. this program was an idea that came from that conversation. audit-a11y is a program that checks for various infractions in web html and then creates a report - . i'm not disabled, but putting myself in their shoes, i figured the best way to get a look at the situation is to make a program to audit these concerns. this tool will help me know when im introducing less-than-accessible html into the world, maybe some day JS too. some things to know: - it doesn't do JS or CSS yet - written in the Go stdlib for fun - open to contributors Contributors: first, thank you :) - there are a list of TODO objectives in the Readme.md if you all see something you might want to add: - printing the offending html lines under each infraction category - count the lines seen vs the lines scanned - include an MDN link for each infraction type for more information on fixing it - spider/crawl a given domain for more pages - add more checks - add unit tests for existing checks

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
77%77% 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
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
51%51% 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: way · Missing: mobile apps, ios, personal
43%43% 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
42%42% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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