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Chrome Store–featured extension that writes X replies via DOM observers

Hacker News

Chrome Store–featured extension that writes X replies via DOM observers

Hi HN, A couple of months ago I posted an early version of this Chrome extension. Since then I’ve refined it, fixed a bunch of stability issues, and it was recently featured on the Chrome Web Store’s “Featured” section, which was a nice surprise. What the extension does: – Detects the active tweet or thread directly in the browser – Collects relevant context (parent tweet, author info, thread shape) – Formats a prompt and sends it to the OpenAI API – Inserts the generated reply straight into Twitter’s native reply box All of this happens inside the X.com DOM, without storing any user data. Technical bits: – Uses MutationObserver to track X.com’s constantly changing DOM – Handles dynamically inserted tweet nodes, shadow DOM, and thread expansions – Debounces context extraction to avoid unnecessary re-runs – Simulates native input events to inject the reply so it feels built-in – Avoids backend state; everything is read client-side except the final API call Challenges: – X.com changes UI structure often, so selectors break unpredictably – Preventing duplicate injections when the DOM re-renders – Keeping prompt size small enough for fast generation – Reducing overhead so the extension doesn’t slow down the page Recent improvements: – More stable tweet/thread detection – Better context selection logic – Cleaner UI in the reply popup – Small performance fixes and race-condition fixes Chrome Store page: https://chromewebstore.google.com/detail/xinsightai-ai-reply... Would appreciate feedback from people who’ve built browser extensions or dealt with X.com’s DOM patterns. Happy to discuss any details.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, context · Missing: mac, agents, macos
78%78% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal
42%42% 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
38%38% 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: active · 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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