Brainrot messed up my kid's attention span, so I built a tool to fix it

Hacker News

Hi HN! I am Dan (aadivar), one of two people who have built Kite, a tool with a simple goal: get rid of brain rot and internet addiction. Modern websites are built to keep you engaged using algorithmic feeds, infinite scroll, social proof counters, autoplay recommendations, urgency timers, etc. Even when you intend to check one thing, you get pulled into a 45-minute session you didn't choose. I personally noticed this with my daughter, who was getting pulled into YouTube Shorts rabbit hole addiction, while she was supposed to be using the platform for science and music lessons. Trying to limit her time on the app didn't help much, and blocking YouTube entirely would block the educational content as well. I wanted a way to block only YouTube Shorts while keeping the other longform video content. Unfortunately, a simple solution for my problem did not exist. Thus the idea for Kite was born. With Kite, anyone can use natural language to say: - "Hide shorts" - "Replace infinite scroll with pagination" - "Hide like counts" And the tool does exactly what you need, thus removing brainrot rabbit holes and other addictive elements. ** Kite is available under a closed beta right now, and we would especially love feedback from folks who spend a lot of time on YouTube, Twitter/X, Reddit, LinkedIn, or shopping platforms. Beta sign-up here: https://www.agentkite.com Happy to answer any technical questions about how the agent safely modifies websites on the client side. ** BTW, We also built AttentionGuard, which detects manipulation patterns like FOMO cues, engagement traps, social pressure signals etc. in real-time and flags them. This extension is open-source (MIT license) and live for Chrome and Firefox now. More details on our website!

Visit product View on Hacker NewsFeb 27, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% 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

14%14% 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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