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Link2Slide – Free tool to turn any URL/text into LinkedIn Carousels

Hacker News

Link2Slide – Free tool to turn any URL/text into LinkedIn Carousels

Hello HN, I'm a solo developer working on design systems and frontend engineering. I built Link2Slide because I found existing tools for creating LinkedIn carousels were either too expensive, required signups, or were just too complex for quick edits. Link2Slide is a simple, free tool that lets you: 1. Paste a URL (article, blog) or raw text. 2. Auto-generate summarized slides using AI. 3. Customize the styling (inspired by high-end design systems). 4. Export directly to PDF. There are no ads, no VC, and no paywalls—just a project built with passion to help the community. I’d love to hear your feedback on the UX and the generated output quality. Thanks!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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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.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, 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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