API to Use NotebookLM Programmatically

Hacker News

Hey HN, Miguel here, I am the founder of from AutoContent API ( https://autocontentapi.com ). You can test it for free by entering the 100% off coupon YC100. Content creation, even with the tools we have today, can be very time consuming. My goals is to make it easier for developers to create meaningful content. With AutoContent API you can leverage Google's NotebookLM capabilities, which has the following features: - Available sources: websites, pasted text and YouTube videos. - Available outputs: podcast (audio), plan text (single answer), Q&A document, briefing doc, summary, timeline, table of contents and study guides. I'm also developing a feature to be make it easier to import content from popular websites such as YouTube (import all videos from a channel/topic), X (entire threads and profile posts) or Reddit (popular/new/hot threads). The API docs are here https://docs.autocontentapi.com I would love to get feedback from you, I'll be available to respond to any questions!

Visit product View on Hacker NewsNov 22, 2024

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

86%86% 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 · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video, google · Missing: mobile apps, ios, personal

51%51% 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

27%27% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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