Chrome Extension to Preview Escaped Strings

Hacker News

Hi HN! I got tired of squinting at escaped strings when debugging LLM API calls, so I built a Chrome extension that instantly renders them. When working with OpenAI/Claude/etc APIs, the request/response logs are full of escaped strings like "You asked:\n\n\"What is recursion?\"\n\nRecursion is when...". This extension lets you select any text containing these escape sequences and instantly see what it actually looks like. How it works: Select any escaped string on a webpage → See the rendered output in a floating panel. Works great on API logs. No more copy-pasting into console or online decoders. Everything runs locally in your browser. GitHub: https://github.com/aeft/escaped-string-viewer Built this to scratch my own itch - would love to know if others find it useful!

Visit product View on Hacker NewsAug 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, openai, code · Missing: mac, agents, macos

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

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

32%32% 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 · Missing: arr, mrr, revenue

22%22% 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.

Correct prediction on native model

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