#small model training
2329 related articles
Tech Frontiers·2 min
Kimi K2.5 Fully Open-Sourced: Deep Dive into 1T Parameter MoE Architecture + Agent Cluster Capabilities
Deep dive into Moonshot AI's fully open-sourced Kimi K2.5: 1T parameter MoE architecture, Vision-to-Code capabilities, and 100-Agent parallel cluster system topping open-source benchmarks.
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Tutorials·3 min
Enterprise RAG Implementation: Architecture Principles and Production-Grade Optimization Guide
Complete guide to enterprise RAG architecture covering data indexing, vectorization, and retrieval optimization. Practical insights on chunking strategies, hybrid retrieval, and hallucination control for production-grade LLM applications.
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Tutorials·3 min
Getting Started with LLM Application Development from Scratch: A Complete Guide to Learning Paths and Career Directions
A complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
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Product Reviews·3 min
Local Deployment of Qwen 3.6 27B on 4×3080Ti: Real-World Coding Test with OpenCode
Real-world test of Qwen 3.6 27B FP8 deployed on 4×3080Ti 16GB modded GPUs with OpenCode for system tool development. Covers hardware setup, inference speed, context management, and productivity gains.
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Tutorials·3 min
Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference
Decode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.
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Deep Dives·3 min
Getting Started with RAG: A Complete Guide from LLM Hallucinations to Retrieval-Augmented Generation
A deep dive into RAG (Retrieval-Augmented Generation) technology, covering LLM hallucinations, data staleness, and limited expertise, plus RAG workflows, core components, and LangChain learning paths.
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Tutorials·3 min
Python Learning Path: A Three-Stage Systematic Approach from Zero to Practical Projects
A detailed guide to Python's three learning stages: syntax fundamentals, advanced concepts, and practical applications. Covers OOP, web scraping, data analysis, and automation with timeline planning.
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Tutorials·3 min
LLM Learning Roadmap: A Complete Guide from Beginner to Project Implementation Across Seven Core Modules
A systematic breakdown of seven core LLM learning modules covering environment setup, Prompt Engineering, RAG, Agents, dev frameworks, fine-tuning, and hands-on projects for developers.
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Tutorials·3 min
PyTorch Beginner Tutorial: A Complete Guide to Tensor Operations and Neural Network Construction
A detailed PyTorch beginner guide covering tensor operations, dynamic computational graphs, GPU acceleration, and building your first neural network with nn.Module, with learning path recommendations and code examples.
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Product Reviews·3 min
Trae Solo In-Depth Review: Is ByteDance's Free AI Coding Tool Actually Usable?
In-depth review of ByteDance's Trae Solo free AI coding tool, evaluating token quotas, 6-project concurrency, cross-platform sync, and model quality for domestic individual developers.
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Tutorials·2 min
Complete Guide to Local LLM Deployment with Ollama: AI That Works Offline
Complete guide to deploying open-source LLMs locally with Ollama. Covers installation, model selection, VRAM requirements, and performance comparison of Llama 3 and Qwen models. Free, offline-capable AI.
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Tutorials·3 min
Cloudflare AI Search in Practice: Building an Intelligent Knowledge Base Q&A System from Scratch
Complete guide to deploying Cloudflare AI Search managed RAG service, covering R2 data sources, AI Gateway, text chunking, Reranker, and semantic caching for production-grade intelligent search.
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Tutorials·3 min
Build AI Apps Without Code: A Practical Guide to Rapid Development with No-Code Platforms
Learn how to build AI apps with no-code platforms: visual UI building, LLM integration, data management, and payment monetization—no programming required.
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Product Reviews·2 min
AI Game Character Design in Practice: Complete Pipeline from Concept Art to Layered Assets in 30 Minutes
A practical case study of a wuxia swordswoman detailing the full AI-assisted game character design pipeline: concept art, hand repair, pose transfer, and asset splitting — a must-read for indie devs.
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Product Reviews·2 min
Hlopix AI Free Canvas Experience: A Complete Game Art Workflow from UI Generation to Sprite Slicing
In-depth experience with Hlopix AI Free Canvas: covering one-click game UI generation, automatic sprite sheet slicing, poster and Logo design for a complete game art AI workflow for indie developers.
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Product Reviews·2 min
Kiro AI IDE In-Depth Review: How Spec Mode Makes AI Coding More Reliable and Powerful
In-depth review of AWS's new Kiro AI IDE, covering Spec Mode, Agent Hooks, Steering, and a real WeChat mini program project comparing Kiro with Cursor.
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Tutorials·3 min
Developing a Game Auto-Farming Script: A Hands-On Guide with Template Matching + Behavior Trees
A complete walkthrough of building a game auto-farming script using template matching and behavior trees, covering asset capture, workflow design, and delay control.
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Research·3 min
Inside Cursor Composer 2 Training: A Complete Breakdown of Its Distributed Reinforcement Learning Architecture
Deep dive into how Cursor trained Composer 2 on Fireworks: async pipeline architecture, MoE numerical precision challenges, Router Replay, and global distributed GPU coordination.
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Expert Opinions·2 min
The Execution Paradox of AI Coding Agents: Why They Talk the Talk but Don't Walk the Walk
AI coding agents excel at explaining why to do something but often fail to actually do it. This article analyzes LLM analysis paralysis and offers strategies for developers.
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Tutorials·1 min
NVIDIA CUDA Tile: A Detailed Look at the New Tile-Based GPU Programming Paradigm
In-depth analysis of NVIDIA's new CUDA Tile programming model: how tile-based GPU kernel development integrates with C++, optimizes data locality, and accelerates Tensor Core computation for HPC, deep learning, and AI inference.
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