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Audio summarized from Reddit posts, using ElevenLabs and GPT-4 Turbo

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Audio summarized from Reddit posts, using ElevenLabs and GPT-4 Turbo

You can create an automated audio summary like this one - on any topic, from any data source(s) with Graphlit ( https://www.graphlit.com ). I ingested Reddit posts from r/Azure, with entity extraction, and filtered just on posts that were tagged with SharePoint. Then, with a custom publishing prompt, I had GPT-4 Turbo write a script for ElevenLabs, and posted it to a Slack channel. With just a few API calls, you can build AI apps on top of Graphlit to deliver any kind of researched summaries for your users: in audio, as well as text.

Visit productView on Hacker NewsJan 28, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, apps, user · Missing: mac, agents, macos
94%94% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, users · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
50%50% 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: users, calls · Missing: plus, platform, intuitive
35%35% 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
23%23% 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
20%20% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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