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195 related articles
Amazon MTurk Closes to New Customers: …
·4 min
Amazon MTurk Closes to New Customers: The End of the Crowdsourcing Era and the AI Revolution
Amazon MTurk stops accepting new customers after nearly 20 years. Explore its legacy in AI training and academic research, and how LLMs are reshaping the data annotation industry.
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·3 min
Unsloth v0.1.45-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM
Unsloth v0.1.45-beta (PyPI: 2026.6.2) delivers 2x faster LLM fine-tuning and up to 70% VRAM reduction. Now at 67.9k GitHub stars, upgrade via pip install.
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·2 min
Zhipu GLM 5.2 Deep Dive: Can Open-Weight Models Challenge Top Closed-Source AI?
Zhipu GLM 5.2 review: open weights released within 24hrs, built for long-horizon Agent tasks. Strong benchmarks, standout writing & frontend design, at a fraction of closed-model pricing.
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·7 min
Agentic AI Reshapes Genomic Interpretation: A SOTA Case Study in Interstitial Lung Disease Diagnosis
How Agentic AI achieves SOTA performance in interstitial lung disease (ILD) genomic interpretation through autonomous planning, multi-step reasoning, and tool calling—and its clinical impact.
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·2 min
Training Just One Transformer Layer Can Match Full-Parameter RL Training?
A controversial study shows training just one Transformer layer can match full-parameter RL training. We analyze the technical principles, engineering value, and limitations of this approach.
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·5 min
Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital
A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.
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·1 min
Unsloth v0.1.47-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM
Unsloth v0.1.47-beta is out. This 67.9k-star open-source framework fine-tunes Llama, Mistral, and Qwen 2x faster with 70% less VRAM on consumer GPUs.
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·6 min
Java Developer's Guide to AI Application Development: Building an Airline Intelligent Customer Service System
A practical guide for Java developers to build AI apps without switching to Python. Learn LangChain4j, RAG, Function Calling, and MCP through an airline customer service project.
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·6 min
LangChain + MCP in Practice: A Pitfall-Avoidance Guide to LLM Selection and Local Deployment
DeepSeek R1 lacks Function Calling and JSON Output by default. Qwen3's programmable thinking modes make it the top open-source agent choice. Key LLM selection pitfalls and MCP protocol updates.
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·3 min
Claude Code Misclassification Appeals Guide: Three Feedback Channels for Optimizing AI Classifiers
Encountering false positives in Claude Code? Learn how to use the /feedback command, thumbs buttons, and other channels to appeal misclassifications and improve AI safety classifiers.
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Beware of Big Tech AI Agents: How to P…
·2 min
Beware of Big Tech AI Agents: How to Protect Your Research Intellectual Property
Are your research code, algorithms, or unpublished papers safe with Big Tech AI agents? This deep dive explores data risks and offers practical protection strategies.
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·3 min
Andrew Ng's Prompt Engineering Course: How Developers Call LLMs via API
A systematic overview of Andrew Ng's ChatGPT Prompt Engineering for Developers course: base vs. instruction-tuned models, API best practices, and two core prompting principles.
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GPT-5.6 Sol Deep Dive: Major Upgrades …
·3 min
GPT-5.6 Sol Deep Dive: Major Upgrades Across Coding, Science, and Cybersecurity
OpenAI previews GPT-5.6 Sol, featuring major upgrades in coding, scientific research, and cybersecurity alongside its most advanced safety stack yet.
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·7 min
AI Agent Development: A Four-Stage Competency Model from 15K to 40K RMB Salary Growth Path
A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).
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·7 min
Building an AI Agent from Scratch: Implementing Core Conversational Agent Features in Python
Learn AI Agent development from scratch. This tutorial covers LLMs and prompts, then builds a conversational agent in Python using the DeepSeek API with multi-turn dialogue and system prompts.
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·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
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·2 min
AI LLM Development Career Path: Four Stages from Zero to $60K+/Year
A four-stage learning path for AI LLM application development: from Python basics and RAG architecture to Agent cluster orchestration, helping developers transition into AI roles.
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·3 min
AI Agent Learning Path from Zero: A Complete Guide from Principles to Practice
A systematic AI Agent learning path covering core principles, dev environment setup, memory management, multi-agent collaboration, and hands-on projects for beginners.
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·2 min
The Biggest Weakness of Chinese AI Models: They Need Frameworks to Work Well
Deep analysis of why Chinese AI models (DeepSeek, Qwen) need opinionated tool frameworks to work properly, exploring training differences, function calling maturity, and framework selection advice.
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On-Policy Distillation Explained: Prin…
·3 min
On-Policy Distillation Explained: Principles, Advantages, and Applications — A Comprehensive Guide
A deep dive into On-Policy Distillation: core principles, key differences from Off-Policy methods, and applications in model compression, reasoning transfer, RLHF alignment, and self-improvement.
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