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Tag: parameter

  • Schneier on Security: AIs and Robots Should Sound Robotic

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    Kurt Seifried
    in

    Source URL: https://www.schneier.com/blog/archives/2025/02/ais-and-robots-should-sound-robotic.html Source: Schneier on Security Title: AIs and Robots Should Sound Robotic Feedly Summary: Most people know that robots no longer sound like tinny trash cans. They sound like Siri, Alexa, and Gemini. They sound like the voices in labyrinthine customer support phone trees. And even those robot voices are being made obsolete…

  • Simon Willison’s Weblog: S1: The $6 R1 Competitor?

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Feb/5/s1-the-6-r1-competitor/ Source: Simon Willison’s Weblog Title: S1: The $6 R1 Competitor? Feedly Summary: S1: The $6 R1 Competitor? Tim Kellogg shares his notes on a new paper, s1: Simple test-time scaling, which describes an inference-scaling model fine-tuned on top of Qwen2.5-32B-Instruct for just $6 – the cost for 26 minutes on 16 NVIDIA…

  • Simon Willison’s Weblog: o3-mini is really good at writing internal documentation

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Feb/5/o3-mini-documentation/#atom-everything Source: Simon Willison’s Weblog Title: o3-mini is really good at writing internal documentation Feedly Summary: o3-mini is really good at writing internal documentation I wanted to refresh my knowledge of how the Datasette permissions system works today. I already have extensive hand-written documentation for that, but I thought it would be interesting…

  • Irrational Exuberance: Our own agents with their own tools.

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    Kurt Seifried
    in

    Source URL: https://lethain.com/our-own-agents-our-own-tools/ Source: Irrational Exuberance Title: Our own agents with their own tools. Feedly Summary: Entering 2025, I decided to spend some time exploring the topic of agents. I started reading Anthropic’s Building effective agents, followed by Chip Huyen’s AI Engineering. I kicked off a major workstream at work on using agents, and I…

  • Hacker News: How to Scale Your Model: A Systems View of LLMs on TPUs

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    Kurt Seifried
    in

    Source URL: https://jax-ml.github.io/scaling-book/ Source: Hacker News Title: How to Scale Your Model: A Systems View of LLMs on TPUs Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the performance optimization of large language models (LLMs) on tensor processing units (TPUs), addressing issues related to scaling and efficiency. It emphasizes the importance…

  • Bulletins: Vulnerability Summary for the Week of January 27, 2025

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    Kurt Seifried
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    Source URL: https://www.cisa.gov/news-events/bulletins/sb25-034 Source: Bulletins Title: Vulnerability Summary for the Week of January 27, 2025 Feedly Summary: High Vulnerabilities PrimaryVendor — Product Description Published CVSS Score Source Info 0xPolygonZero–plonky2  Plonky2 is a SNARK implementation based on techniques from PLONK and FRI. Lookup tables, whose length is not divisible by 26 = floor(num_routed_wires / 3) always…

  • Hacker News: Show HN: Klarity – Open-source tool to analyze uncertainty/entropy in LLM output

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    Kurt Seifried
    in

    Source URL: https://github.com/klara-research/klarity Source: Hacker News Title: Show HN: Klarity – Open-source tool to analyze uncertainty/entropy in LLM output Feedly Summary: Comments AI Summary and Description: Yes **Summary:** Klarity is a robust tool designed for analyzing uncertainty in generative model predictions. By leveraging both raw probability and semantic comprehension, it provides unique insights into model…

  • Hacker News: TopoNets: High-Performing Vision and Language Models with Brain-Like Topography

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    Kurt Seifried
    in

    Source URL: https://toponets.github.io/ Source: Hacker News Title: TopoNets: High-Performing Vision and Language Models with Brain-Like Topography Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces “TopoNets,” a novel approach that incorporates brain-like topography in AI models, particularly convolutional networks and transformers, through a method called TopoLoss. This innovation results in high-performing models…

  • Simon Willison’s Weblog: OpenAI reasoning models: Advice on prompting

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Feb/2/openai-reasoning-models-advice-on-prompting/ Source: Simon Willison’s Weblog Title: OpenAI reasoning models: Advice on prompting Feedly Summary: OpenAI reasoning models: Advice on prompting OpenAI’s documentation for their o1 and o3 “reasoning models" includes some interesting tips on how to best prompt them: Developer messages are the new system messages: Starting with o1-2024-12-17, reasoning models support developer…

  • Hacker News: Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting

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    Kurt Seifried
    in

    Source URL: https://arxiv.org/abs/2501.16673 Source: Hacker News Title: Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses LLM-AutoDiff, a novel framework aimed at improving the efficiency of prompt engineering for large language models (LLMs) by utilizing automatic differentiation principles. This development has significant implications…