Tag: generation

Source URL: https://github.com/Cinnamon/kotaemon Source: Hacker News Title: Kotaemon: An open-source RAG-based tool for chatting with your documents Feedly Summary: Comments AI Summary and Description: Yes Summary: The provided text details the functionalities and features of the `kotaemon` project, which is a tool designed for building RAG (Retrieve and Generate) pipelines focused on document Question Answering…

Hacker News: Meta Wants More AI Bots on Facebook and Instagram

Jan 2, 2025

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Source URL: https://nymag.com/intelligencer/article/meta-wants-more-ai-bots-on-facebook-and-instagram.html Source: Hacker News Title: Meta Wants More AI Bots on Facebook and Instagram Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses Meta’s new strategy to integrate AI-generated characters into its social media platforms like Facebook and Instagram. This initiative aims to enhance user engagement and compete with rising…

Hacker News: OpenAI failed to deliver the opt-out tool it promised by 2025

Jan 1, 2025

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Source URL: https://techcrunch.com/2025/01/01/openai-failed-to-deliver-the-opt-out-tool-it-promised-by-2025/ Source: Hacker News Title: OpenAI failed to deliver the opt-out tool it promised by 2025 Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses OpenAI’s development challenges with its Media Manager tool aimed at addressing intellectual property concerns by allowing creators to manage how their works are utilized in…

Hacker News: Large Concept Models: Language modeling in a sentence representation space

Jan 1, 2025

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Source URL: https://github.com/facebookresearch/large_concept_model Source: Hacker News Title: Large Concept Models: Language modeling in a sentence representation space Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the implementation and experiments related to Large Concept Models (LCMs) as part of language modeling in a semantic representation space. By utilizing SONAR embeddings for multiple…

Unit 42: Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability

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Source URL: https://unit42.paloaltonetworks.com/?p=138017 Source: Unit 42 Title: Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability Feedly Summary: The jailbreak technique “Bad Likert Judge" manipulates LLMs to generate harmful content using Likert scales, exposing safety gaps in LLM guardrails. The post Bad Likert Judge: A Novel Multi-Turn Technique to…

Hacker News: Things we learned out about LLMs in 2024

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Source URL: https://simonwillison.net/2024/Dec/31/llms-in-2024/ Source: Hacker News Title: Things we learned out about LLMs in 2024 Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses significant advancements and trends in Large Language Models (LLMs) throughout 2024, highlighting new technologies, efficiency improvements, cost reductions, and issues such as model usability and environmental impact. It…

Simon Willison’s Weblog: Things we learned out about LLMs in 2024

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Source URL: https://simonwillison.net/2024/Dec/31/llms-in-2024/#atom-everything Source: Simon Willison’s Weblog Title: Things we learned out about LLMs in 2024 Feedly Summary: A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying…

Hacker News: Letting Language Models Write My Website

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Source URL: https://nicholas.carlini.com/writing/2025/llms-write-my-bio.html Source: Hacker News Title: Letting Language Models Write My Website Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents an engaging exploration of the capabilities and limitations of large language models (LLMs) through a creative project where the author generates a new homepage and biography each day using different…

Hacker News: Coconut by Meta AI – Better LLM Reasoning with Chain of Continuous Thought?

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