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Research#llm📝 BlogAnalyzed: Jan 3, 2026 08:10

New Grok Model "Obsidian" Spotted: Likely Grok 4.20 (Beta Tester) on DesignArena

Published:Jan 3, 2026 08:08
1 min read
r/singularity

Analysis

The article reports on a new Grok model, codenamed "Obsidian," likely Grok 4.20, based on beta tester feedback. The model is being tested on DesignArena and shows improvements in web design and code generation compared to previous Grok models, particularly Grok 4.1. Testers noted the model's increased verbosity and detail in code output, though it still lags behind models like Opus and Gemini in overall performance. Aesthetics have improved, but some edge fixes were still required. The model's preference for the color red is also mentioned.
Reference

“The model seems to be a step up in web design compared to previous Grok models and also it seems less lazy than previous Grok models.”

Permalinkr/singularity
Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:08

Why are we still training Reward Models when LLM-as-a-Judge is at its peak?

Published:Dec 30, 2025 07:08
1 min read
Zenn ML

Analysis

The article discusses the continued relevance of training separate Reward Models (RMs) in Reinforcement Learning from Human Feedback (RLHF) despite the advancements in LLM-as-a-Judge techniques, using models like Gemini Pro and GPT-4. It highlights the question of whether training RMs is still necessary given the evaluation capabilities of powerful LLMs. The article suggests that in practical RL training, separate Reward Models are still important.

Key Takeaways

    Reference

    ““Given the high evaluation capabilities of Gemini Pro, is it necessary to train individual Reward Models (RMs) even with tedious data cleaning and parameter adjustments? Wouldn't it be better to have the LLM directly determine the reward?””

    PermalinkZenn ML
    Research#llm📝 BlogAnalyzed: Dec 29, 2025 06:06

    From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

    Published:May 13, 2025 22:10
    1 min read
    Practical AI

    Analysis

    This article from Practical AI discusses how Reinforcement Learning (RL) is being used to improve AI agents built on foundation models. It features an interview with Mahesh Sathiamoorthy, CEO of Bespoke Labs, focusing on the advantages of RL over prompting, particularly in multi-step tool use. The discussion covers data curation, evaluation, and error analysis, highlighting the limitations of supervised fine-tuning (SFT). The article also mentions Bespoke Labs' open-source libraries like Curator, and models like MiniCheck and MiniChart. The core message is that RL offers a more robust approach to building AI agents.
    Reference

    “Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities.”

    PermalinkPractical AI
    Software Development#AI-Assisted Coding👥 CommunityAnalyzed: Jan 3, 2026 09:37

    Show HN: Adding Mistral Codestral and GPT-4o to Jupyter Notebooks

    Published:Jul 2, 2024 14:23
    1 min read
    Hacker News

    Analysis

    This Hacker News article announces Pretzel, a fork of Jupyter Lab with integrated AI code generation features. It highlights the shortcomings of existing Jupyter AI extensions and the lack of GitHub Copilot support. Pretzel aims to address these issues by providing a native and context-aware AI coding experience within Jupyter notebooks, supporting models like Mistral Codestral and GPT-4o. The article emphasizes ease of use with a simple installation process and provides links to a demo video, a hosted version, and the project's GitHub repository. The core value proposition is improved AI-assisted coding within the popular Jupyter environment.
    Reference

    “We’ve forked Jupyter Lab and added AI code generation features that feel native and have all the context about your notebook.”

    PermalinkHacker News
    Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:49

    Weaviate 1.2 Release: Transformer Models

    Published:Mar 30, 2021 00:00
    1 min read
    Weaviate

    Analysis

    Weaviate v1.2 adds support for transformer models, enabling semantic search. This is a significant update for vector databases, allowing for more sophisticated data retrieval and analysis using models like BERT and Sentence-BERT.
    Reference

    “Weaviate v1.2 introduced support for transformers (DistilBERT, BERT, RoBERTa, Sentence-BERT, etc) to vectorize and semantically search through your data.”

    PermalinkWeaviate