Kichan.ai, Chrome extension generates JavaScript to augment any website

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Hey HN! I've been working on https://kichan.ai , a Chrome extension that lets you modify any website using prompts. It uses Google Gemini to generate JavaScript that runs directly on your browser web page. How it works: Click the extension to open a side panel. Type what you want to do like "Remove all ads", "Add a share button", or "Make this table sortable". KICHAN generates JavaScript using Gemini 2.5 Flash and executes it immediately. Your custom scripts are saved and can run automatically on specific websites. Key features include context-aware generation where you can right-click any element to add it to a "context buffer" that helps the AI understand exactly what you want to modify. Scripts can also run automatically on matching URLs using glob patterns. Example use cases I've found useful include automating repetitive tasks like auto-reloading my GitHub PR page and beeping if there's something new, removing distracting elements from news sites, adding keyboard shortcuts to web apps, getting banned from browser games for scripting too much (twice), and customizing form behavior. The goal is to give users programmatic control over their web experience without needing to code. If you can describe it, KICHAN tries to build it. Caveats: Very beta! CSP/iframe/canvas heavy sites most often block injection. I am not a JavaScript or web developer so it is heavily vibecoded. Expect surprises. Would love feedback and especially: What would you use this for? --- Website: https://kichan.ai Extension: https://chromewebstore.google.com/detail/kichan-ai-web-augme... Twitter: https://x.com/kichan_ai

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, user · Missing: mac, agents, macos

97%97% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, google, 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: users · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, 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

22%22% 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

17%17% 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.

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