From Code Completion to Autonomous SWE Agents: The 2025 Roadmap to Code Intelligence
20 hours ago 高效码农From Code Completion to Autonomous SWE Agents: A Practitioner’s Roadmap to Code Intelligence in 2025 What’s the next leap after 90 % single-function accuracy? Teach models to behave like software engineers—plan across files, edit with tests, verify with sandboxes, and keep learning from real merges. 0. One-Minute Scan: Where We Are and What to Do Next Stage Today’s Best Use 30-Day Stretch Goal IDE autocomplete 7B FIM model, temperature 0.3, inline suggestions Add unit-test verifier, GRPO fine-tune → +4-6 % on internal suite Code review Generic LLM second pair of eyes Distill team comments into preference pairs, DPO for one …
Revolutionize Your Dev Workflow: Autonomous Multi-Agent Code Generation Platform
8 days ago 高效码农CodeMachine: The Autonomous Multi-Agent Platform That Built Itself Have you ever imagined being able to automatically receive a complete, functional project codebase just by providing a requirements document? This might sound like science fiction, but today I’m introducing you to a tool that turns this fantasy into reality: CodeMachine. What Exactly is CodeMachine? CodeMachine is a command-line native autonomous multi-agent platform that operates locally on your computer, transforming specification files into production-ready code through coordinated AI workflows. Picture this: you have a project idea, write detailed specifications, and then CodeMachine functions like a well-trained development team, automatically handling system design, …
Code World Model: How Meta’s AI Revolutionizes Code Understanding and Debugging
2 months ago 高效码农“ What if an AI could not only write code but also simulate in its mind how that code will alter the state of a system? This is the paradigm shift offered by Code World Model (CWM). As developers, when a new code-generation model emerges, we ask two key questions: 1) How good is it at writing code? 2) Does it truly understand what happens when the code runs? Most large language models (LLMs) excel at the first but struggle with the second, leading to code that looks correct but fails at runtime or can’t reason about multi-step software engineering …
Mastering Qwen3-Coder-480B: The Ultimate Guide to Local Code Generation
4 months ago 高效码农The Complete Guide to Running Qwen3-Coder-480B Locally: Unleashing State-of-the-Art Code Generation Empowering developers to harness cutting-edge AI coding assistants without cloud dependencies Why Qwen3-Coder Matters for Developers When Alibaba’s Qwen team released the Qwen3-Coder-480B-A35B model, it marked a watershed moment for developer tools. This 480-billion parameter Mixture-of-Experts (MoE) model outperforms Claude Sonnet-4 and GPT-4.1 on critical benchmarks like the 61.8% Aider Polygot score. The groundbreaking news? You can now run it on consumer hardware. 1. Core Technical Capabilities Qwen3-Coder Architecture Diagram 1.1 Revolutionary Specifications Feature Specification Technical Significance Total Parameters 480B Industry-leading scale Activated Parameters 35B Runtime efficiency Native Context …
LLM Evaluation Framework Revolutionized: ArtifactsBench Bridges Visual-Interactive Code Generation Gaps
4 months ago 高效码农Bridging the Visual-Interactive Gap: Evaluating LLM Code Generation with ArtifactsBench Large Language Models (LLMs) are rapidly evolving from generating static code to creating dynamic, interactive visual artifacts. However, existing evaluation frameworks fail to assess the holistic quality of these outputs. This article explores ArtifactsBench, a groundbreaking benchmark designed to evaluate LLMs’ ability to generate visually faithful and interactive code artifacts. 1. The Critical Gap in LLM Evaluation Traditional code generation benchmarks like HumanEval and SWE-Bench focus on algorithmic correctness but overlook two crucial aspects of modern applications: 「Visual fidelity」 (layout integrity, color schemes, animations) 「Interactive integrity」 (button responsiveness, state transitions) …