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1000 results for “WetHat”

  1. @WetHat
    Mm. I read over it.
    @sjl
    Since it's #springlispgamejam2024 this is wrong:
    "
    Lisp doesn't have any engine as full-featured as Unity, but several people are currently working on making 3D game engines.
    "
    Naughty Dog's scheme game engines written in CL, eg.

    This touches on the fundamental problem: People have no idea what lisp software is out there. The article overemphasises the trivial- package stuff, which resembles poppy languages. Lisp sw are normally big systems with new languages

  2. CW: Pretty Printing Table Data in Common Lisp

    has a powerfull but byzantine string formatting language which can do all sorts of magic.

    In this the mighty 'format' function is used to produce human readable pipe tables.

    nbviewer.org/gist/WetHat/a49e6

    GitHub: gist.github.com/WetHat/a49e6f2

  3. CW: Pretty Printing Table Data in Common Lisp

    #CommoLisp has a powerfull but byzantine string formatting language which can do all sorts of magic.

    In this #JupyterNotebook the mighty 'format' function is used to produce human readable #Markdown pipe tables.

    nbviewer.org/gist/WetHat/a49e6

    GitHub: gist.github.com/WetHat/a49e6f2

    #Lisp #CommonLisp #Lisplang #Jupyter #SBCL #GithubGist

  4. CW: Pretty Printing Table Data in Common Lisp

    #CommoLisp has a powerfull but byzantine string formatting language which can do all sorts of magic.

    In this #JupyterNotebook the mighty 'format' function is used to produce human readable #Markdown pipe tables.

    nbviewer.org/gist/WetHat/a49e6

    GitHub: gist.github.com/WetHat/a49e6f2

    #Lisp #CommonLisp #Lisplang #Jupyter #SBCL #GithubGist

  5. CW: Pretty Printing Table Data in Common Lisp

    #CommoLisp has a powerfull but byzantine string formatting language which can do all sorts of magic.

    In this #JupyterNotebook the mighty 'format' function is used to produce human readable #Markdown pipe tables.

    nbviewer.org/gist/WetHat/a49e6

    GitHub: gist.github.com/WetHat/a49e6f2

    #Lisp #CommonLisp #Lisplang #Jupyter #SBCL #GithubGist

  6. Top 5 AI-Based Code Editors for Coding in 2025 - DEV Community

    The article covers:
    ➡️ VSCode
    ➡️ Cursor
    ➡️ Tabnine
    ➡️ Amazon CodeWhisperer
    ➡️ JetBrains AI Assistant

    dev.to/aneeqakhan/top-5-ai-bas

  7. How to Master SQL Joins | HackerNoon

    Various SQL join types with clear explanations and practical examples

    hackernoon.com/how-to-master-s

  8. Seeing AI as a collaborator, not a creator | MIT Technology Review

    The key message of this article is: "AI can be, will be, and already is a tool for creative expression, but that true art will always be something steered by human creativity, not machines"

    technologyreview.com/2025/04/2

  9. OpenAI launches o3 and o4-Mini - gHacks Tech News

    The new OpenAI o3 and o4-Mini models:
    ➡️ integrate multimodal reasoning
    ➡️ provide improved transparency and enhanced responsiveness.

    ghacks.net/2025/04/17/openai-l

  10. Simple Prompt Tweaks Derail LLM Reasoning - MarkTechPost

    ➡️ MIT researchers analyzed how input changes impact the response quality of 13 prominent LLMs.
    ➡️Prompt perturbations included irrelevant contexts, misleading (pathological) instructions, and a mix of additional yet unnecessary details.
    ➡️Quality dropped substantially, with average declines of up to 55.89% for irrelevant contexts.

    marktechpost.com/2025/04/15/fr

    #AI #PropmtEngineering #LLMReasoning

  11. Simple Prompt Tweaks Derail LLM Reasoning - MarkTechPost

    ➡️ MIT researchers analyzed how input changes impact the response quality of 13 prominent LLMs.
    ➡️Prompt perturbations included irrelevant contexts, misleading (pathological) instructions, and a mix of additional yet unnecessary details.
    ➡️Quality dropped substantially, with average declines of up to 55.89% for irrelevant contexts.

    marktechpost.com/2025/04/15/fr

  12. Simple Prompt Tweaks Derail LLM Reasoning - MarkTechPost

    ➡️ MIT researchers analyzed how input changes impact the response quality of 13 prominent LLMs.
    ➡️Prompt perturbations included irrelevant contexts, misleading (pathological) instructions, and a mix of additional yet unnecessary details.
    ➡️Quality dropped substantially, with average declines of up to 55.89% for irrelevant contexts.

    marktechpost.com/2025/04/15/fr

    #AI #PropmtEngineering #LLMReasoning

  13. Simple Prompt Tweaks Derail LLM Reasoning - MarkTechPost

    ➡️ MIT researchers analyzed how input changes impact the response quality of 13 prominent LLMs.
    ➡️Prompt perturbations included irrelevant contexts, misleading (pathological) instructions, and a mix of additional yet unnecessary details.
    ➡️Quality dropped substantially, with average declines of up to 55.89% for irrelevant contexts.

    marktechpost.com/2025/04/15/fr

    #AI #PropmtEngineering #LLMReasoning

  14. Simple Prompt Tweaks Derail LLM Reasoning - MarkTechPost

    ➡️ MIT researchers analyzed how input changes impact the response quality of 13 prominent LLMs.
    ➡️Prompt perturbations included irrelevant contexts, misleading (pathological) instructions, and a mix of additional yet unnecessary details.
    ➡️Quality dropped substantially, with average declines of up to 55.89% for irrelevant contexts.

    marktechpost.com/2025/04/15/fr

    #AI #PropmtEngineering #LLMReasoning

  15. How to Use Wireshark Filters to Analyze Your Network Traffic

    This tutorial covers both foundational and advanced skills in using Wireshark:
    ➡️ Wireshark is a leading network protocol analyzer for capturing and dissecting packets.
    ➡️Wireshark filters dramatically reduce analysis time by isolating relevant packets.
    ➡️Mastering filter syntax enables identification of unusual traffic patterns and security threats.

    freecodecamp.org/news/use-wire

  16. Elon Musk has merged X (formerly Twitter) with xAI in an all-stock deal, valuing the combined entity at $113 billion. This merger promises to leverage advanced AI capabilities and distribution. Almost certainly this AI will be trained on X posts, resulting in an extremely biased AI. Just another step towards Elon's pipe dream.

    decrypt.co/312221/elon-musk-x-

  17. claims to revolutionize 3D scene generation by directly creating renderable 3D representations from one or more images. It achieves unprecedented speed and quality without requiring computationally expensive optimization or augmentation steps.

    arxiv.org/abs/2503.14445v1

  18. #Bolt3D claims to revolutionize 3D scene generation by directly creating renderable 3D representations from one or more images. It achieves unprecedented speed and quality without requiring computationally expensive optimization or augmentation steps.

    arxiv.org/abs/2503.14445v1

    #ComputerVision #VirtualReality #3DModeling #GoogleResearch #LatentDiffusion #FeedForwardModels

  19. #Bolt3D claims to revolutionize 3D scene generation by directly creating renderable 3D representations from one or more images. It achieves unprecedented speed and quality without requiring computationally expensive optimization or augmentation steps.

    arxiv.org/abs/2503.14445v1

    #ComputerVision #VirtualReality #3DModeling #GoogleResearch #LatentDiffusion #FeedForwardModels

  20. #Bolt3D claims to revolutionize 3D scene generation by directly creating renderable 3D representations from one or more images. It achieves unprecedented speed and quality without requiring computationally expensive optimization or augmentation steps.

    arxiv.org/abs/2503.14445v1

    #ComputerVision #VirtualReality #3DModeling #GoogleResearch #LatentDiffusion #FeedForwardModels

  21. #Bolt3D claims to revolutionize 3D scene generation by directly creating renderable 3D representations from one or more images. It achieves unprecedented speed and quality without requiring computationally expensive optimization or augmentation steps.

    arxiv.org/abs/2503.14445v1

    #ComputerVision #VirtualReality #3DModeling #GoogleResearch #LatentDiffusion #FeedForwardModels

  22. The article offers a fresh perspective on improving AI data reliability by using Decentralized Physical Infrastructure Networks (DePINs):

    ➡️By using blockchain, DePINs create secure, verifiable real-world data streams for AI systems.
    ➡️Applications range from smart farming to bridges that predict stress.

    hackernoon.com/ai-is-running-o

  23. The article provides good insights into industry leaders such as Waymo, DeepMind, and Amazon demonstrate the transformative power of Reinforcement Learning (RL).

    Takeaways:
    ➡️ RL drives autonomy and innovation across industries, but challenges like interpretability remain pivotal.
    ➡️ Hybrid systems that blend RL and symbolic reasoning hint at breakthroughs in high-level decision-making.

    computer.org/publications/tech

  24. Takeaways:
    ➡️ User time is the ultimate currency: respect it by crafting intuitive and efficient interfaces.
    ➡️ Simplify complex tasks through step-by-step processes and on-demand details.
    ➡️ Leverage familiar design patterns to reduce mental effort and boost intuitiveness.
    ➡️ Provide immediate feedback.
    ➡️ Simplify choices to prevent analysis paralysis.

    hackernoon.com/sneaky-ux-trick