We Put AI in Copy and Paste

Hacker News

Hi all, We are gyani and vasin. We have been building TabTabTab, it puts AI in your clipboard. You can copy any thing and we will transform the content as you paste it. You can copy a page with Airbnb listings, and paste it on a spreadsheet to get a table. You can copy a LinkedIn profile and paste it on email to write an outreach message. You can copy your resume and get a cover letter. We've seen people build all kinds of spreadsheets with the app. We have also seen people paste on LinkedIn, Reddit, email apps mostly doing outreach. Developers have also been using us to get AI in their clipboard to create SQL queries in AWS, GCP, Hasura, Supabase, PostHog and more. I personally use it write shell commands. Would love to get some feedback!

Visit product View on Hacker NewsJun 26, 2025

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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: apps, email, using · Missing: mac, agents, macos

82%82% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

56%56% 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: personal, apps · Missing: mobile apps, ios, entrepreneurs

46%46% 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

22%22% 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

20%20% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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