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Tag: AI development

  • Slashdot: WSJ Finds ‘Dozens’ of Delusional Claims from AI Chats as Companies Scramble for a Fix

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    Kurt Seifried
    in

    Source URL: https://slashdot.org/story/25/08/10/2023212/wsj-finds-dozens-of-delusional-claims-from-ai-chats-as-companies-scramble-for-a-fix?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: WSJ Finds ‘Dozens’ of Delusional Claims from AI Chats as Companies Scramble for a Fix Feedly Summary: AI Summary and Description: Yes Summary: The Wall Street Journal has reported on concerning instances where ChatGPT and other AI chatbots have reinforced delusional beliefs, leading users to trust in fantastical narratives,…

  • The Register: How OpenAI used a new data type to cut inference costs by 75%

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/08/10/openai_mxfp4/ Source: The Register Title: How OpenAI used a new data type to cut inference costs by 75% Feedly Summary: Decision to use MXFP4 makes models smaller, faster, and more importantly, cheaper for everyone involved Analysis Whether or not OpenAI’s new open weights models are any good is still up for debate, but…

  • Slashdot: Initiative Seeks AI Lab to Build ‘American Truly Open Models’ (ATOM)

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    Kurt Seifried
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    Source URL: https://news.slashdot.org/story/25/08/09/1916243/initiative-seeks-ai-lab-to-build-american-truly-open-models-atom?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Initiative Seeks AI Lab to Build ‘American Truly Open Models’ (ATOM) Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the launch of the ATOM Project, aimed at enhancing U.S. open-source AI competitiveness, highlighting a significant gap in open-source AI development in the country compared to China.…

  • Docker: Remocal and Minimum Viable Models: Why Right-Sized Models Beat API Overkill

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    Kurt Seifried
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    Source URL: https://www.docker.com/blog/remocal-minimum-viable-models-ai/ Source: Docker Title: Remocal and Minimum Viable Models: Why Right-Sized Models Beat API Overkill Feedly Summary: A practical approach to escaping the expensive, slow world of API-dependent AI The $20K Monthly Reality Check You built a simple sentiment analyzer for customer reviews. It works great. Except it costs $847/month in API calls…

  • Simon Willison’s Weblog: Quoting Sam Altman

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Aug/8/sam-altman/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Sam Altman Feedly Summary: GPT-5 rollout updates: We are going to double GPT-5 rate limits for ChatGPT Plus users as we finish rollout. We will let Plus users choose to continue to use 4o. We will watch usage as we think about how long to offer…

  • The Register: Meta training AI on social media posts? Only 7% in Europe think it’s OK

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/08/07/meta_training_ai_on_social/ Source: The Register Title: Meta training AI on social media posts? Only 7% in Europe think it’s OK Feedly Summary: Privacy campaigner Max Schrem’s NOYB is back on Zuck’s back Meta’s enthusiasm for training its AI on user data is not shared by the users themselves – at least for some Europeans…

  • The Register: How to run OpenAI’s new gpt-oss-20b LLM on your computer

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/08/07/run_openai_gpt_oss_locally/ Source: The Register Title: How to run OpenAI’s new gpt-oss-20b LLM on your computer Feedly Summary: All you need is 24GB of RAM, and unless you have a GPU with its own VRAM quite a lot of patience Hands On Earlier this week, OpenAI released two popular open-weight models, both named gpt-oss.…

  • Simon Willison’s Weblog: Qwen3-4B Instruct and Thinking

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Aug/6/qwen3-4b-instruct-and-thinking/ Source: Simon Willison’s Weblog Title: Qwen3-4B Instruct and Thinking Feedly Summary: Qwen3-4B Instruct and Thinking Yet another interesting model from Qwen—these are tiny compared to their other recent releases (just 4B parameters, 7.5GB on Hugging Face and even smaller when quantized) but with a 262,144 context length, which Qwen suggest is essential…