In this tutorial, we’ll build a serverless Product Hunt thread summarizer using Large Language Models (LLMs). You’ll learn how to scrape, process, and summarize Product Hunt threads using LLM into concise summaries, highlighting key insights. By creating this application, you’ll help users save time and quickly grasp community sentiments on topic.
WebScraper
class to fetch the discussion thread HTML from the provided URL and convert it into text using BeautifulSoup.--gpu A100
: Specifies the GPU type for deployment. Available options include A10
, A100
, and T4
.--runtime inferless-runtime-config.yaml
: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.Scenario | On-Demand Cost | Serverless Cost |
---|---|---|
100 requests/day | $28.8 (24 hours billed at $1.22/hour) | $3.73 (3.06 hours billed at $1.22/hour) |