Why Senior Engineers Are Abandoning AI Coding: The Hidden Dangers of Agentic Programming
4 days ago 高效码农Two Years of Vibecoding: Why I Returned to Writing Code by Hand Core Question: After relying heavily on AI-assisted coding (Agentic Coding) for a long period, why do senior engineers ultimately decide to return to writing code manually? In the realm of software development, the journey most people share with AI coding follows a strikingly similar script. Initially, you tentatively assign it a simple task. The results impress you. Emboldened, you give it a massive task. The results leave you even more stunned. This instant gratification easily fosters an illusion that the barriers to programming have been leveled. Immediately following …
Daily 100+ Commits: How to Build an Enterprise-Grade AI Agent System Like Moltbot
7 days ago 高效码农Daily 100+ Commits: How Moltbot Built an Enterprise-Grade Agent System at Breakneck Speed The core question this section answers: How can a single developer maintain a commit frequency of over 100 times a day while building a blockbuster open-source project without sacrificing code or product stability? In the software development realm, speed and quality are often viewed as irreconcilable contradictions. However, the birth of Moltbot (formerly Clawdbot) shatters this conventional wisdom. Initiated by Peter Steinberger, this project accumulated 8,297 code commits in just 66 days, achieving a daily commit frequency of 127. Even more staggering is that Peter contributed 86.5% …
Fast Agentic Search (FAS) Cuts Code Search Time 4× with Claude-Level Accuracy: A Deep Dive
1 months ago 高效码农4× Faster Code Search with Claude-Level Accuracy: Deep Dive into Relace AI’s Fast Agentic Search (FAS) Featured Snippet Answer (67 words): Fast Agentic Search (FAS) is a specialized small agent model released by Relace AI that dramatically accelerates codebase navigation. By combining parallel tool calling (4–12 files at once) with on-policy reinforcement learning, FAS achieves the same precision as traditional step-by-step Agentic Search while being 4× faster. Real-world SWE-bench integration shows 9.3% lower median latency and 13.6% fewer tokens. If you’ve ever watched an AI coding assistant spend two full minutes just “looking for the right file” in a 5 …
Code World Model: How Meta’s AI Revolutionizes Code Understanding and Debugging
4 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 …
Kimi-Dev-72B: The Open-Source AI Revolutionizing Code Debugging & Software Engineering
7 months ago 高效码农Kimi-Dev-72B: The Open-Source Coding LLM Revolutionizing Software Engineering “ In software development, debugging and testing consume significant developer time. A groundbreaking open-source tool is transforming this landscape—Kimi-Dev-72B, an advanced large language model specifically engineered for software engineering tasks. AI-assisted programming transforming development workflows Breakthrough Performance Benchmarks Kimi-Dev-72B achieves a remarkable 60.4% accuracy rate on the industry-standard SWE-bench Verified evaluation, setting a new record among open-source models. This accomplishment demonstrates capabilities approaching professional developer proficiency and represents three critical advancements: Problem-solving capacity: Correctly resolves over half of software engineering issues Open-source parity: First community-driven solution rivaling commercial alternatives Efficiency transformation: Revolutionizes …