I Started a Podcast on How Companies Are Applying LLMs

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Hey HN, I’ve recently launched a podcast that dives into how companies are leveraging large language models (LLMs) to solve real-world problems in unique and creative ways. Each episode explores case studies, technical blogs and research papers to explain how LLMs are applied. Some of the cool episodes we’ve released so far: - Uber's Use of LLMs for Mobile Testing. - DoorDash Enriching Product Information with LLMs. Each episode dives deep into the why and how, breaking down technical implementations, business impact, and the broader implications of using LLMs in innovative ways. If you’re working with AI or just curious about how it's being applied in ways you’ve never thought of, I’d love for you to check it out and share your thoughts! Links to the podcast: Spotify - https://open.spotify.com/show/0Toon5UiQc5P7DNDjsrr9K?si=536d... Apple Podcast - https://podcasts.apple.com/us/podcast/ai-arxiv/id1768464164

Visit product View on Hacker NewsSep 17, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, models · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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: way · Missing: mobile apps, ios, personal

52%52% 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

32%32% 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 · Missing: web3, chat, crypto

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