Google Cloud Tech

Google Cloud Tech

US
@googlecloudtech
Science & Technology
2.0K
Video Count
54.1M
Video View
1.4M
Subscriber
#10,378
United States Rank
#43,129
Global Rank
Google Cloud Tech YouTube channel subscribers:1,350,000- Seelive statisticsand growth insights below.

Google Cloud Tech YouTube Statistics & Analytics

Subscribers
1.4M
Total Views
54.1M
Videos
2.0K
Activity
Unknown

Google Cloud Tech Content Analysis

Content Type Distribution

Long videosLong
73%
248 videos
ShortsShorts
27%
91 videos

📽️ This channel specializes in long-form videos. Deep dives and comprehensive content perform well here.

Content Categories

Primary CategoryScience & Technology
100%
Science & Technology
339(100%)

🎯 Primary focus: Science & Technology with 339 videos (100% of categorized content).

Latest Video

Long video
How to design a multi-agent system that skips the LLM
29:09
New

How to design a multi-agent system that skips the LLM

2.4K
Views
131
Likes
1 day ago
Published

Github repo → https://goo.gle/race-condition Previous episode → https://goo.gle/marathonagent A thousand AI agents run a marathon, and almost none of them ever call the LLM. In this multi-agent system deep dive, Casey West breaks down the one architectural decision behind Race Condition: a 1000-agent system built on Google's Agent Development Kit (ADK). The question every AI engineer is wrestling with: when do you let an LLM decide, and when do you just write the code in a multiagent system? We trace one decision end to end, planning a marathon route, then show how the same idea (skip the LLM where you don't need it) scales to a thousand agents running on deterministic code. What you'll learn: * When to use an LLM vs deterministic logic * The before_model_callback trick, keep the agent, skip the model * Why route planning is deterministic (NP-hard + the Spine & Sprout algorithm) * How 1,000 autopilot runners make 0 LLM calls * Where the tokens actually go (the AI decides, the code runs) * Scaling 1,000 stateless sessions with Redis Chapters 00:00 - Intro: 1,000 AI agents that don't call the LLM 00:41 - When should an agent use an LLM? 01:02 - [Demo] Planning a marathon route 01:59 - Why Google Maps can't route a marathon 05:08 - Why the LLM Is the wrong tool (NP-hard) 05:40 - The deterministic spine & sprout algorithm 06:58 - Using AI Studio to choose the algorithm 09:00 - The trick: Skip the LLM with a callback 12:26 - before_model_callback — the reveal 17:50 - Autopilot runners: 1,000 agents, 0 LLM calls 21:31 - How many tokens? Where they actually go 23:28 - The second cost: Session state & redis 29:05 - Wrap up More resources: Google Agent Development Kit (ADK) → https://goo.gle/3PItVzL Google ADK Community (Redis session service) → https://goo.gle/4ugzmUw Agent Runtime → https://goo.gle/4nXDhnX Google Cloud Memory Store → https://goo.gle/4nXxBtT Agent2Agent Protocol (A2A) protocol → https://goo.gle/4u5x8HF Casey West on LinkedIn → https://goo.gle/4dXnsJr Annie Wang on LinkedIn → https://goo.gle/43GCXAo Watch more Hands on AI → https://www.youtube.com/playlist?list=PLIivdWyY5sqKnJOvP89yF8t9mWuzMTcbM 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #AIAgent #GoogleADK #Gemini #MultiAgentSystem #AgenticAI #GoogleCloud Speakers: Casey West, Annie Wang Products Mentioned: Google Agent Development Kit, Gemini API, Agent Runtime, Google Cloud Pub/Sub, AlloyDB, Agent2Agent Protocol

Top Wissenschaft & Technologie YouTube Kanäle in Vereinigte Staaten ansehen

Vergleiche diesen Kanal mit den führenden Wissenschaft & Technologie-Kanälen in Vereinigte Staaten.

Ranking: Vereinigte StaatenKategorie: Wissenschaft & TechnologieKategorie-Schwerpunkt: 100%
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Google Cloud Tech Channel Snapshot

Score: 6.1/10

A high-level snapshot of content cadence, library size, and consistency derived from this channel's recent uploads.

Overall Score
6.1
Consistency
95%
Cadence
2-3/wk
Library
50

Growth Potential

5.5/10

Library of 50 videos with ~5.3K avg views per upload. Combined size + reach signal suggests steady building.

Audience Engagement

6.7/10

Avg engagement rate of 4.03% (likes + comments / views) across 47 videos. Healthy — at or above the ~3% baseline.

Niche Specialization

6/10

54% of recent videos cluster in Technology. Moderate focus — could tighten the niche for more compounding.

Suggested Actions

Recommendations grouped by typical impact for channels at this stage

  1. 1
    Increase upload frequency to 2-3 videos per week
    High ImpactCadence
  2. 2
    Focus on SEO optimization for better discoverability
    High ImpactSEO
  3. 3
    Analyze top-performing content for pattern replication
    MediumStrategy
  4. 4
    Increase community engagement through comments and polls
    MediumEngagement

Frequently Asked Questions About Google Cloud Tech

Data Source & Accuracy

Source: YouTube Data API v3
Accuracy: Real-time statistics from official YouTube API
Data is updated hourly and sourced directly from official APIs to ensure accuracy and reliability.

Data from YouTube Data API v3 • Updated hourly • Last updated: 04:40 AM