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81 related articles
Structured Information Extraction with…
·3 min
Structured Information Extraction with Local 7B Quantized Models: Root Cause Analysis and Engineering Optimization
Using Qwen 2.5 7B quantized locally to extract 60+ fields from insurance/financial contracts? Learn why it struggles and how task splitting, RAG, GBNF, and smarter chunking can fix it.
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·3 min
Distributed AI Systems in Practice: A Complete Guide to Training, Inference, and Production Deployment
A deep dive into Distributed AI Systems: a new book distilling 10 years of AI engineering experience covering distributed training, inference optimization, and production model serving.
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·3 min
PostgreSQL Lock Contention: The Root Cause and Solutions for High-Concurrency Scalability Bottlenecks
An in-depth analysis of PostgreSQL's lock scalability bottlenecks under high concurrency: LWLock contention, the cost of the multi-process model, negative scaling, and practical strategies like connection pooling, transaction optimization, and horizontal scaling.
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·5 min
The Five Unsolved Challenges of Production MLOps: A Deep Dive into GPU Scheduling, Fault-Tolerant Training, and Model Observability
A deep dive into the five genuinely tough challenges of production MLOps: fault-tolerant training on Spot instances, cross-team GPU scheduling, data reproducibility, model observability, and inference cost optimization.
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·3 min
Multiple AI Agents Sharing a Codebase: Parallel Collaboration Conflicts and Practical Solutions
When multiple AI coding agents work on the same codebase simultaneously, how do you avoid interface conflicts and coordination chaos? A deep dive into Git worktree isolation, contract-first design, and intent declaration.
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·5 min
GPT-5.6 In-Depth Field Test: A Real-World Log After Burning Through $200K
A developer stress-tested GPT-5.6 for six weeks across 67 projects, burning $180K-$240K in inference. Real cases of task persistence, Rust rewrites, autonomous browser control — plus honest frontend and 3D shortfalls.
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·5 min
Browser Use: A Deep Dive into the Event-Driven Browser AI Agent Framework
Browser Use is an open-source AI Agent framework that lets LLMs autonomously drive browser operations via natural language. This article breaks down its four-layer architecture, core Agent loop, CDP perception layer, dynamic Tools dispatch, and its Skills, Sandbox, and MCP extension capabilities.
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FL Studio CEO Goes Straight to Reddit:…
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FL Studio CEO Goes Straight to Reddit: How Community Feedback Drives DAW Product Evolution
Why did Constantin Koehncke, CEO of FL Studio's parent Image Line, choose Reddit as a core feedback channel? A deep dive into the community-driven product philosophy behind this top-tier DAW.
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·5 min
Nothing Phone 4b In-Depth Review: The Ultimate Value Play at €330
Nothing Phone 4b review: 6.77-inch high-refresh AMOLED, 5200mAh battery, Snapdragon 6 Gen 4. What are the trade-offs at €330? An in-depth look at its screen, camera, performance, and software.
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·6 min
Tencent Hunyuan HY3 Official Version Open-Sourced: The Strongest Under 300B, at 1 Yuan per Million Tokens
Tencent Hunyuan HY3 official version is open-sourced under Apache 2.0, priced as low as 1 yuan per million input tokens, with major gains in agents, reasoning, coding, and long context. On the same day, Meituan open-sourced its trillion-parameter LongCat 2.0.
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·5 min
TensorRT Multi-Device Inference: Engineering Practices for Breaking Through Single-GPU Memory Bottlenecks
NVIDIA TensorRT now supports multi-device inference via pipeline and tensor parallelism, distributing large models across multiple GPUs to break through single-card memory limits.
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·7 min
21-Day Python Automation Testing Crash Course: A Complete Breakdown of the Three-Stage Learning Path
Learn automation testing from scratch! This article breaks down a three-stage path: Selenium/Appium tools, Requests+PyTest API testing, performance testing and CI/CD, with real projects—build a complete skill set in 21 days.
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·7 min
From Vibe Coding to Engineered AI Programming: A Practical Guide to Enterprise Development
AI coding tools are changing development, but Vibe Coding hides risks in code quality and maintenance. This article explores Engineered AI Programming, compares Codex and Claude Code, and reveals real enterprise development paths.
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·7 min
The Prompt is the Platform: How AI Agents Deeply Participate in System Design and Specification
Resonate's founder proposes "The Prompt is the Platform": as AI agents generate production-grade implementations from abstract specs, engineers' value shifts to specification. A deep dive into deterministic simulation and forbidden-fruit debugging.
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·7 min
Getting Started with Dify: A Complete Guide from Local Deployment to Building Enterprise-Grade AI Applications
A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.
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·7 min
Harness Engineering: The Six-Layer Engineering Framework for Enterprise AI Agent Deployment
From Prompt Engineering to Harness Engineering, a deep dive into the core challenge of truly deploying AI Agents in enterprises. This article breaks down the six-layer architecture and shares real-world Hermes Agent practice.
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·7 min
Getting Started with OpenAI LLM Development: A Practical Guide to Model Selection, Token Pricing, and API Usage
A systematic guide to the three cores of OpenAI LLM app development: GPT-4/GPT-3.5 model selection, token billing and cost-saving tips, and practical use of the Models, Completion, and Chat Completion APIs.
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·7 min
Geosql: A Vertical Skill Pack That Teaches AI Geospatial Data Analysis
Geosql is a geospatial SQL skill pack designed for AI coding assistants like Claude and Codex, enabling LLMs to accurately generate PostGIS queries and handle coordinate transformations and spatial analysis.
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·5 min
Turn PRD into XMind Test Cases with One Click Using Cursor — 10x Efficiency Boost
A must-read for test engineers: use Cursor's custom Skills to auto-generate test cases covering positive, negative, and boundary values from PRDs, output as Markdown linked to XMind, adapting to functional, interface, and performance testing.
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·5 min
Context Graphs: The Key Technology for AI Agents to Remember and Reuse Past Decisions
Context Graphs use graph structures to store decisions, causal relationships, and outcomes, enabling AI agents to accumulate experience and reuse historical decisions without modifying model weights.
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