MTP Explained: How Qwen and Gemma Predict Several Tokens Ahead

Ran Qwen 3.8 27B 4-bit quantization benchmarks on RTX 4090 and M-series Mac for local AI agents. Don't fall for 1-bit — here's why.

Qwen3.8-Flash-Next

Discover the top 4 permissive open-weight coding models for local developer workstations: Muse Spark 1.2, Qwen3.8-27B, Muse Glimmer, and Qwen3.8-Flash-Next.

Qwen4 still isn’t officially here, but Qwen3.8-Flash-Next gives us something more useful than...

I have always liked RSS because it gives me a easy way to decide for myself which sources I want to...

I spent a weekend benchmarking Qwen2.5 at five sizes — 0.5B, 1.5B, 3B, 7B, 14B — through five real...

I recently completed a project as part of my AI Engineering learning journey: an Agentic RAG AI Agent...

Compare Qwen3.8-27B and Muse Glimmer 30B: Apache 2.0 licensing, 24 GB GPU memory requirements, video modalities, and local agent serving benchmarks.

本文面向零代码用户，提供一套使用国产大模型（Kimi、GLM、Qwen等）从零开发并上线产品的完整流程。核心步骤包括购买Coding Plan、下载官方Agent编程产品、注册域名与服务器并同步做ICP备案，然后通过Agent的Plan模式描述需求并让AI自动执行开发。上线后建议建立分支保护与测试流程，并强调即使不懂代码，也必须对系统架构了如指掌。

首届"点亮乡村课堂"赛道一等奖作品"智绘科普"采用Qwen3.5-397B-A17B大语言模型与Manim开源数学动画引擎，构建多Agent分阶段协作流水线。系统通过规划、草稿、实现、审查、合成五个Agent，配合时序门控、几何审计、视觉审查三道质量门及自动修复回路，实现可控可编辑的教学动画生成。项目底层"多Agent协作+门控+自我修复"范式可迁移至养老陪伴、孤独症干预等场景。