#Loop Engineering
2628 related articles
Loop Engineering: The Paradigm Shift f…
·3 min
Loop Engineering: The Paradigm Shift from Prompt Engineering to Automated Programming Agents
Deep dive into Loop Engineering's five core components including worktree isolation, skill files, and sub-agent separation. Explore why loop design is harder than prompt engineering.
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Tutorials·2 min
Harness Engineering Explained: A Guide to Building a Four-Step Closed-Loop AI Agent Work System
A deep dive into the Harness Engineering four-step closed-loop principle (Goal, Action, Verification, Memory), clarifying its relationship with Prompt Engineering, Context Engineering, and MCP.
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Deep Dives·4 min
Deep Dive into Claude Code Source Code: A Complete Guide to Agent Loops, Context Engineering, and Architecture Design
Deep analysis of Claude Code's source architecture: Agent loop mechanism, context engineering strategies, tool system design, and permission controls. Based on the viral GitHub project how-claude-code-works.
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·3 min
Why Does the Chess Engine Community Resist AI-Assisted Development? A Technical and Cultural Analysis
The chess engine dev community openly resists AI-assisted coding. This article analyzes the technical logic and cultural roots behind Stockfish's rejection of AI-generated code.
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·3 min
PyTorch vs TensorFlow: How Beginners Should Choose a Deep Learning Framework
Should deep learning beginners choose PyTorch or TensorFlow? This article compares both frameworks on research trends, ecosystem, and deployment, with practical switching advice.
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·3 min
My First Open Source Contribution: A Non-Engineer's Complete Experience Submitting a Documentation PR
A non-engineer shares their first documentation PR to an active open source project. From discovering unclear install docs to a successful merge, learn how newcomers can start contributing.
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·3 min
ARYA: Building a Voice AI Assistant That Controls Real Applications from Scratch
Developer builds ARYA, a voice AI assistant that controls real apps like WhatsApp and Spotify with vector memory. Deep dive into its technical implementation, AI Agent trends, and opportunities for builders.
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·3 min
OpenAI's Internal Model 'Astra' Leaked: Ten Advances in Mathematics Spark Heated Discussion in the AI Community
Reddit leaks OpenAI's internal model codenamed Astra, claiming ten advances in math and theoretical CS. We analyze the rumor's credibility and its implications for AI reasoning.
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·3 min
Explorative Modeling: How Best-of-K Training Strategies Improve Model Learning Efficiency
Explorative modeling lets models generate K candidate predictions and learn from the best one, introducing exploration into training. This article analyzes Best-of-K training strategy principles, applications, and challenges.
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·4 min
Flycast WASM JIT: A Technical Breakthrough for Running Dreamcast Emulator at Full Speed in the Browser
Flycast WASM JIT v1 achieves full-speed Dreamcast emulation in browsers by generating complete WebAssembly modules at runtime, bypassing WASM's architectural limitations and boosting from 2FPS to full frame rate.
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·3 min
Building a GitHub Code Review Bot from Scratch: A Hands-On Guide to AI Agent Automation
A detailed guide on building a GitHub code review bot from scratch, covering cloud deployment, secure sandboxes, Vercel AI SDK, and multi-agent collaboration for automated development workflows.
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·4 min
17M-Parameter Transformer for Blood Glucose Prediction: Open-Source Diabetes AI Model Explained
An open-source blood glucose prediction model using BERT-style Transformer architecture with only 17M parameters, running on mobile devices with DILATE and Pinball loss for 2-hour glucose forecasting.
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·2 min
unquestion: AI Conversational Form Tool That Eliminates Low Completion Rates
unquestion is an AI-powered conversational form tool that replaces static fields with smart dialogue, supports adaptive follow-ups, and outputs structured data. Learn how it boosts survey completion rates.
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·3 min
Building an AI Agent from Scratch: A Full Development Process Retrospective and Pitfall Guide
A complete guide to building AI Agents from scratch based on real developer experiences: task selection, tool comparison (no-code vs frameworks vs hand-written), stability challenges, and evaluation criteria.
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·6 min
AI Coding Boosts Productivity 10x: Are Programmers Writing Their Own Pink Slips?
A senior developer admits 95% of work is done by Claude Code, with 10x productivity gains. From coding to architecture, AI is eroding programmers' core skill moats. Deep analysis of AI coding's impact on tech employment.
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·2 min
NudgeForMe: AI Email Follow-Up Agent That Ensures Silent Emails Don't Cost You Deals
NudgeForMe is an AI email follow-up agent that auto-scans unanswered emails and drafts natural follow-ups in draft mode, ensuring users stay in control while never missing business opportunities.
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·3 min
Enterprise RAG Development Team Evaluation Guide: Retrieval Quality, Engineering Details & Production Readiness
When evaluating RAG development teams, enterprises should focus on retrieval quality metrics, hallucination detection, chunking strategies, hybrid retrieval, and production observability—not just model and framework support.
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·3 min
Analyzing the Claude Test Environment Escape Incident: Real Threats to AI Agent Security
Deep analysis of the Claude AI escape incident: how Anthropic's model was exploited in cyberattacks, the real security risks of AI agents, and strategies for permission control and regulation.
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·3 min
DeerFlow: Deep Dive into ByteDance's Open-Source Long-Horizon SuperAgent Framework
Deep analysis of ByteDance's open-source DeerFlow long-horizon SuperAgent framework, covering six core components, architecture design, use cases, and industry significance.
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·3 min
System Prompts Should Be Version-Controlled Like Code
System prompts drive LLM apps but often lack version control and regression testing. Learn how to manage them with versioning, structured separation, testing, and code review.
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