Entire’s AI-Native Platform: Why Legacy Git & PRs Fail in the Agent Era
12 days ago 高效码农Goodbye “Black Box” Programming: Former GitHub CEO Reshapes Human-Agent Collaboration with Entire Core Question Answered: As AI agents generate code at unprecedented speeds, why have traditional development toolchains like Git, Issues, and PRs failed, and what kind of new platform do we need to handle this revolution? On February 10, 2026, the tech world received a massive jolt: Thomas Dohmke, former CEO of GitHub, announced the launch of Entire, a brand-new developer platform backed by a landmark 60millionseedroundata300 million valuation. Led by Felicis, this financing round stands as one of the largest in developer tools history. It signals a definitive …
Trellis: The Architectural Framework That Makes AI Coding Controllable and Collaborative
19 days ago 高效码农★Trellis: The Architectural Framework for AI Coding – Making Claude Code & Cursor Controlled, Collaborative, and Persistent★ When using Claude Code or Cursor for AI-assisted development, have you ever faced this dilemma: Yesterday you taught the AI your project’s coding standards, but today, in a new session, it has forgotten everything? Or, when handling complex features, does the randomness of AI-generated code force you to conduct repetitive code reviews and corrections? This section answers the core question: Compared to using Cursor or Claude Code directly, what fundamental efficiency and quality pain points does introducing the Trellis framework solve? Trellis is …
Stop Failing at Vibe Coding: The AI Documentation-First System for Shipping Real Software
21 days ago 高效码农Stop Failing at “Vibe Coding”: The Documentation-First System for Shipping Real Software Why is it that despite using the most advanced AI coding agents like Cursor or Claude Code, you still end up with a pile of broken, non-functional code? The core answer is simple: The problem isn’t AI “hallucinating.” The problem is you, the operator, lacking structured thinking and constraints. AI is a translator that converts your intent into code; if your intent is vague and unstructured, the output will inevitably be chaotic. By establishing a strict “Documentation-First” system that pre-sets all specifications, workflows, and context, you can eliminate …
SWE-Pruner Breaks the Context Wall: How to Slash AI Coding Agent Costs by 54%
28 days ago 高效码农Breaking the “Context Wall” for Code Agents: A Deep Dive into SWE-Pruner’s Adaptive Context Pruning In the current landscape of software development, Large Language Model (LLM)-based agents are demonstrating remarkable capabilities, navigating codebases, running tests, and submitting patches end-to-end. However, as these capabilities grow, a critical “Context Wall” problem has emerged: the accumulation of long interaction contexts within LLMs is driving up API costs and introducing severe latency. Existing compression methods often compromise code syntax or discard critical debugging details. This article explores SWE-Pruner, a framework that mimics human “selective skimming” to provide task-aware, adaptive context pruning for coding agents. …
Distributed Agent Orchestration & AI: How the Engineering Bottleneck Has Shifted Forever
1 months ago 高效码农AI and Distributed Agent Orchestration: What Jaana Dogan’s Tweet Reveals About the Future of Engineering A few days ago, Jaana Dogan, a Principal Engineer at Google, posted a tweet: “Our team spent an entire year last year building a distributed Agent orchestration system—exploring countless solutions, navigating endless disagreements, and never reaching a final decision. I described the problem to Claude Code, and it generated what we’d been working on for a year in just one hour.” This tweet flooded my Timeline for days. What’s interesting is that almost everyone could find evidence to support their own takeaways from it. Some …
iFlow-ROME Explained: How Alibaba’s 30B AI Agent Mastered Real-World Coding Tasks
1 months ago 高效码农iFlow-ROME: A Complete Guide to Alibaba’s Next-Generation AI Agent Training System Snippet Summary: iFlow-ROME is Alibaba’s agentic learning ecosystem featuring a 30B MoE ROME model that achieves 57.40% task completion on SWE-bench Verified. The system generates over 1 million verified interaction trajectories through ROCK sandbox manager and employs a three-stage curriculum training methodology for end-to-end execution optimization in real-world environments. When you type a command in your terminal, expecting AI to help you complete complex software engineering tasks, traditional large language models often disappoint—they might generate code that looks reasonable but crashes when you run it, or they “lose the …
From Code to Content: How Software Engineers Can Build a Self-Evolving AI Content Creation System
1 months ago 高效码农From Code to Content: How Programmers Can Build a “Self-Evolving” AI Creation System Abstract This article provides programmers with a systematic framework for AI-powered content creation. It argues that the core challenge for programmers in content creation is a tooling problem, not a capability deficit. The piece details the three-stage evolution of content creation from the “Prompt Era” to the “Methodology Era” and finally to the “Self-Evolution Era.” The core solution is for programmers to leverage their systems thinking: encapsulate proven content methodologies into executable Skills, and establish a feedback and data闭环 (closed-loop) system akin to RLHF (Reinforcement Learning from …
Superpowers: How This AI Coding System Redefines Development Workflows
2 months ago 高效码农Superpowers: A System That Redefines the Workflow of AI Coding Agents The Core Question This Article Answers: What is Superpowers, and how does it fundamentally change how AI programming assistants work? Superpowers is not a single tool or plugin, but a complete software development workflow system built on top of composable “skills.” It aims to transform your coding agent (like Claude Code, Codex, or OpenCode) from a simple code completer into a “super collaborator” with systematic engineering thinking and rigorous development processes. This article will deconstruct its operational principles, detailed workflow, core skills, and underlying design philosophy. The Philosophy of …
AI-Assisted Engineering: The Production-Ready Path Beyond Vibe Coding
2 months ago 高效码农Beyond Vibe Coding: A Guide to AI-Assisted Development A new book by Google Engineering Lead @addyosmani aims to correct the prevalent “Vibe Coding” misconception and provide a rigorous framework for AI-assisted engineering in building production-grade software. I accessed it via O’Reilly’s online platform, and PDF versions are likely available too. Core Argument: From “Vibe Coding” to “AI-Assisted Engineering” 1. Definition and Limitations of “Vibe Coding” Andrej Karpathy once painted a future vision: “I just watch, speak, run code—mostly copy-paste—as long as the ‘vibe’ feels right.” This is “Vibe Coding”—a development approach that relies on high-level prompts, prioritizes rapid prototyping, and …
From Code Completion to Autonomous SWE Agents: The 2025 Roadmap to Code Intelligence
2 months 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 …
Vibe Coding Demystified: How AI is Revolutionizing Modern Software Development
6 months ago 高效码农Vibe Coding: A Guide to Modern AI-Assisted Development Note: This area is changing fast, and we’ll keep updating this guide as new methods and recommendations come up. Table of Contents What is Vibe Coding? Choosing and Using AI Development Clients Setting Up Requirements and Design Guidelines Mastering the Art of Prompting Testing and Validating Your Code Creating and Maintaining Documentation Working with AI to Co-Author Documentation Understanding the Limitations Managing MCP Servers and Tools Keeping Conversations Organized Building the Right Context Rules and Configuration Settings Using the Right Tools Best Practices for Version Control What is Vibe Coding? If you’ve …
LISP API Testing: How LLMs Are Revolutionizing Input Space Partitioning
6 months ago 高效码农LISP: Revolutionizing API Testing with LLM-Powered Input Space Partitioning A technical deep dive into the ICSE ’25 research breakthrough transforming how developers test library APIs What is LISP? LISP (LLM based Input Space Partitioning) represents a paradigm shift in API testing methodology. This innovative approach leverages Large Language Models (LLMs) to analyze library API source code and intelligently partition input spaces based on code semantics and domain knowledge. Core Capabilities Semantic Code Analysis: LLMs directly parse API implementation code Intelligent Input Partitioning: Automatically identifies critical input boundaries Knowledge Integration: Combines programming expertise with common sense reasoning Research Validation: Peer-reviewed at …
Claude Code Revolution: How AI Is Reshaping Software Development in 2025
7 months ago 高效码农Claude Code: How the Terminal AI Agent Is Transforming Software Development The Silent Revolution in Your Terminal Imagine having a developer on your team who never sleeps, reads every file in your repository, understands every edge case, and writes production-ready code from a single sentence. Now imagine summoning this engineer directly from your terminal. This is Claude Code – Anthropic’s AI agent that’s redefining how software gets built. Real-World Impact Engineers from companies like Intercom and Ramp report: 80% of Claude Code’s codebase was self-written by the AI Debugging tasks completed in one pass that previously took 45 minutes Multi-file …
OpenAI Codex vs. Google Jules vs. GitHub Copilot++: The 2025 AI Coding Assistants Showdown
9 months ago 高效码农In-Depth Comparison of AI Coding Assistants: OpenAI Codex vs. Google Jules vs. GitHub Copilot++ AI Coding Assistants Comparison Introduction: The Evolution from Code Completion to Autonomous Programming By 2025, AI-driven coding tools have evolved from basic autocomplete utilities to full-stack programming collaborators. Tools like OpenAI Codex, Google Jules, and GitHub Copilot++ now understand development tasks, run tests, submit code changes, and even generate voice-annotated changelogs. This article provides a detailed analysis of these three tools, exploring their technical innovations, use cases, and competitive advantages. 1. Core Capabilities of Modern AI Coding Assistants 1.1 From Tools to Collaborative Partners Traditional code …
How OpenAI Codex Is Redefining Software Engineering: The Future of AI-Powered Development
9 months ago 高效码农OpenAI Codex: Redefining the Future of Software Engineering In the rapidly evolving landscape of artificial intelligence, OpenAI’s Codex is quietly revolutionizing software development. This advanced AI-powered programming assistant not only enhances coding efficiency but also redefines the possibilities of human-machine collaboration. This comprehensive guide explores Codex’s technical innovations, practical applications, and industry implications through three key dimensions. 1. Technical Breakthroughs: From Code Completion to Intelligent Collaboration 1.1 Evolutionary Milestones 2021 Prototype: Basic code completion with 11% accuracy 2023 Overhaul: Cloud-based agent architecture using codex-1 model Current Version: Specialized o3 reasoning model achieving 75% accuracy 1.2 Architectural Insights Codex’s design combines …