1littlecoder

1littlecoder

US
@1littlecoder
Science & Technology
1.4K
Video Count
14.6M
Video View
110.0K
Subscriber
#26,590
United States Rank
#138,690
Global Rank
1littlecoder YouTube channel subscribers:110,000- Seelive statisticsand growth insights below.

1littlecoder YouTube Statistics & Analytics

Subscribers
110.0K
Total Views
14.6M
Videos
1.4K
Activity
Unknown

1littlecoder Content Analysis

Content Type Distribution

Long videosLong
87%
90 videos
ShortsShorts
13%
13 videos

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

Content Categories

Primary CategoryScience & Technology
100%
Science & Technology
103(100%)

🎯 Primary focus: Science & Technology with 103 videos (100% of categorized content).

Latest Video

Long video
GLM 5.2 is the New AI Code King 👑!!!
8:57
New

GLM 5.2 is the New AI Code King 👑!!!

737
Views
32
Likes
6 days ago
Published

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. https://z.ai/blog/glm-5.2 https://huggingface.co/zai-org/GLM-5.2 ❤️ If you want to support the channel ❤️ Support here: Patreon - https://www.patreon.com/1littlecoder/ Ko-Fi - https://ko-fi.com/1littlecoder 🧭 Follow me on 🧭 Twitter - https://twitter.com/1littlecoder

ai machine learning artificial intelligence

See Top Science & Technology YouTube Channels in United States

Compare this channel with the leading Science & Technology creators in United States.

Ranking: United StatesCategory: Science & TechnologyCategory Focus: 100%
Open ranking

1littlecoder Channel Snapshot

Score: 6.6/10

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

Overall Score
6.6
Consistency
95%
Cadence
2-3/wk
Library
50

Growth Potential

4.9/10

Library of 50 videos with ~1.9K avg views per upload. Combined size + reach signal suggests steady building.

Audience Engagement

7.6/10

Avg engagement rate of 4.56% (likes + comments / views) across 50 videos. Healthy — at or above the ~3% baseline.

Niche Specialization

7.2/10

59% 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 1littlecoder

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: 10:19 AM