The new episode of Spilling The Source is out. this time I talk to Marco Vinciguerra about Scrapegraph AI. A site scraper that uses LLM models for processing. https://lnkd.in/eJ2m6APz
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Happy to share that I have had my MVP award renewed for a third year. It's a great program to be a part of, and everyone I've met in the (just over) three years of being involved are great people who really care about technology and community.
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The latest episode of Spilling the Source is out: https://lnkd.in/ekZXrest In this episode, I discuss Puter with Nariman J. - a web-based OS
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The latest episode of Spilling the Source is out: https://lnkd.in/eG4h7JXi In this episode I talk to Jamie Taylor about his OSS library OwaspHeadersCore - which helps you to be secure by default when using ASP.Net
OwaspHeadersCore with Jamie Taylor by Spilling The Source
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the new episode of Spilling the Source is out this Sunday, but here's a reminder of the first one with Jody Donetti https://lnkd.in/en5fapTG If you maintain or contribute to an OSS library, then I'd love to have you on the show - please get in touch.
Fusion Cache with Jody Donetti by Spilling The Source
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In the last version of Spilling The Source, I talk to Felipe Huici about his Linux Foundation project Unikraft: https://lnkd.in/eYn3h6Dw
Unikraft with Felipe Huici by Spilling The Source
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Listen to the most recent episode of my podcast: Unikraft with Felipe Huici https://lnkd.in/eYn3h6Dw
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The new episode of the Spilling the Source Podcast is released on Sunday. This episode was from a few weeks ago, discussing the Carbon Aware SDK: https://lnkd.in/eJXYf9uQ
Carbon Aware SDK with Chris Lloyd Jones by Spilling The Source
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Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
1moThe integration of LLM models into site scraping, as seen with Scrapegraph AI, signifies a significant advancement in data processing capabilities. You talked about Scrapegraph AI in your post. Considering the intricacies of natural language understanding, how do you address challenges like dynamic web content and semantic ambiguity when training LLMs for site scraping tasks? If, imagine a scenario where Scrapegraph AI needs to extract information from highly dynamic websites with complex layouts, how would you technically ensure robust performance and accuracy in data extraction?