Based on this YouTube video's total view count of 17.1K views and industry-standard rates, the estimated total earning is $12 - $34 through ad revenue. Historical data is not yet available to calculate daily, weekly, or monthly averages.

Explore The 35B Model That Broke the AI Leaderboard (KAT-Coder V2.5) with 17,157 views, 337 likes, and 20 comments. Experience the impact of this video content that has captured audience attention.

Is KAT-Coder V2.5 the best open-source AI coding model you can run locally, or are vendor-run SWE-bench leaderboards completely misleading you? In this architectural deep dive, we break down Kuaishou's massive new 35-billion parameter model (built on top of Qwen3.6) and how their "AutoBuilder" sandbox fixed a massive reinforcement learning flaw where the model was punished for broken testing environments, not broken code. 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf We expose the dirty truth behind "Open Weights"—comparing the bloated 1.5-terabyte Kimi K3 (which requires a rack of H100 GPUs) against KAT-Coder V2.5, which can run natively on an 8GB RTX 3060 using 4-bit quantization. Discover why identical AI models swing by 17 points based entirely on the "agent harness" (and why Alibaba and DeepReinforce are calling out these discrepancies). If you're using vLLM, SGLang, or llama.cpp for local repository surgery, this guide reveals the exact limitations of 35B agents when faced with complex Terminal-Bench workflows. If you found this technical breakdown of AI infrastructure, local LLMs, and machine learning scaling helpful, drop a like and subscribe for more deep dives into software engineering and system architecture! 🔗 Resources Mentioned: 🔗 Models & Resources Mentioned: • KAT-Coder V2.5-Dev: https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev • Qwen 3.6 35B-A3B: https://huggingface.co/Qwen/Qwen3.6-35B-A3B • Ornith-1.0-35B: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B • SWE-bench Official Leaderboard: https://www.swebench.com/ ⏱️ TIMESTAMPS: 00:00 - The 21GB vs 1.5TB "Open" Model War 01:13 - What is KAT-Coder V2.5 & Kuaishou? 02:45 - The AutoBuilder Sandbox: Fixing Reinforcement Learning 04:36 - The Benchmark Lie: SWE-bench vs Terminal Bench 05:49 - The Harness Debate: Did They Rig the Scores? 07:16 - Running KAT-Coder Locally (RTX 3060 & 1M Context) 08:18 - The Reality Check: 41% on Terminal-Bench 09:03 - The Open Weights Leaderboard (Opus 5 vs DeepSeek V4) 09:38 - The Final Verdict: Local Downloads vs APIs #katcoder #localllm #machinelearning #artificialintelligence #systemdesign #softwareengineering #codingagents #qwen #cloudcodes #deepseek #llamacpp #rtx3060 User Queries: kat coder v2.5 local inference rtx 3060 setup how to fix swe bench ai coding benchmark harness kat coder vs claude opus 5 terminal bench comparison qwen 3.6 35b vs kat coder v2.5 performance benchmark run 35b model on 8gb gpu vram llama cpp kimi k3 1.5tb open weights download size what is autobuilder ai agent reinforcement learning how to run kat coder v2.5 locally vllm sglang best open source ai coding agent for software engineering false positives in ai model reinforcement learning testing

With SocialCounts.org’s view counter, track your YouTube video’s live view count and YouTube likes count in real time with fast, reliable updates.

Watch every YouTube video live view count rise with our real-time YouTube views tracker—built for accuracy and minimal delay.

Follow YouTube real time views as they happen, using our dedicated view counter for YouTube videos.

Get up-to-date live view count on YouTube and see real-time growth with SocialCounts.org’s smart tracking tools.