Join us

ContentUpdates and recent posts about Gemini 3..
 Activity
@pixel_og started using tool Azure , 1 week ago.
 Activity
@pixel_og started using tool AWS EKS , 1 week ago.
 Activity
@pixel_og started using tool Argo , 1 week ago.
 Activity
@pixel_og started using tool Ansible , 1 week ago.
 Activity
@pixel_og started using tool Amazon Web Services , 1 week ago.
 Activity
@tairascott gave 🐾 to Helm 4 or Nelm? What's the difference , 1 week, 1 day ago.
 Activity
 Activity
 Activity
@encodedots created an organization EncodeDots , 1 week, 1 day ago.
Link
@anjali shared a link, 1 week, 2 days ago
Customer Marketing Manager, Last9

How to Track Down the Real Cause of Sudden Latency Spikes

Sudden latency spikes rarely have a single cause. This blog shows how to uncover the real source using traces, histograms, and modern debugging signals.

track_latency
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.