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Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:56

What is Gemini 3 Flash: Fast, Smart, and Affordable?

Published:Dec 27, 2025 13:13
1 min read
Zenn Gemini

Analysis

Google has launched Gemini 3 Flash, a new model in the Gemini 3 family. This model aims to redefine the perception of 'Flash' models, which were previously considered lightweight and affordable but with moderate performance. Gemini 3 Flash promises 'frontier intelligence at an overwhelming speed and affordable cost,' inheriting the essence of the superior intelligence of Gemini 3 Pro/Deep Think. The focus seems to be on ease of use in production environments. The article will delve into the specifications, new features, and API changes that developers should be aware of, based on official documentation and announcements.

Key Takeaways

Reference

“Gemini 3 Flash aims to provide 'frontier intelligence at an overwhelming speed and affordable cost.'”

PermalinkZenn Gemini
Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:38

How to deploy DeepSeek-R1 and distilled models securely on Together AI

Published:Jan 31, 2025 00:00
1 min read
Together AI

Analysis

This article likely focuses on the practical aspects of deploying large language models (LLMs) on the Together AI platform. It suggests a focus on security, which is a crucial consideration for AI deployments. The mention of DeepSeek-R1 and distilled models indicates the article will cover specific model types and potentially their optimized versions.

Key Takeaways

    Reference

    “”

    PermalinkTogether AI
    Research#AI in Agriculture📝 BlogAnalyzed: Dec 29, 2025 08:05

    AI for Agriculture and Global Food Security with Nemo Semret - #347

    Published:Feb 10, 2020 20:29
    1 min read
    Practical AI

    Analysis

    This article from Practical AI highlights the application of AI in agriculture, specifically focusing on Gro Intelligence and its CTO, Nemo Semret. The core of the discussion revolves around how Gro utilizes AI and machine learning to address global food security challenges. The article promises insights into Gro's data acquisition methods, the application of machine learning to various agricultural problems, and their modeling approach. The focus is on macro-scale application of AI, suggesting a broad, data-driven approach to understanding and improving food production and distribution globally. The article sets the stage for a discussion on how AI can contribute to solving critical issues related to food security.
    Reference

    “In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling.”

    PermalinkPractical AI