#SFT limitations
65 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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·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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·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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·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
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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·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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·3 min
Java Developer's Guide to AI Application Development: From Spring AI to Intelligent Customer Service
A comprehensive guide for Java developers transitioning to AI application development, covering Spring AI, RAG, Function Calling, and a hands-on airline intelligent customer service project.
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·4 min
OpenAI Swarm Framework Explained: The Core Mechanisms of Function Call and Handoff
Deep dive into OpenAI Swarm multi-agent orchestration framework, explaining Function Call tool invocation and Handoff task transfer mechanisms with local deployment guide.
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Product Reviews·3 min
Kimi K2.6 In-Depth Review: A Complete Breakdown of Its Coding and Agent Capabilities
In-depth review of Kimi K2.6's coding, Agent collaboration, and visual development capabilities. #1 open-source on SWE-Bench Pro, 300 parallel sub-agents, API priced at 1/3 of competitors.
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Product Reviews·1 min
Gemini 3.5 Flash Falls Flat: Great Benchmarks, Terrible Real-World Performance, and a Buggy CLI
Gemini 3.5 Flash benchmarks look great but it's the only model that failed real-world coding tests. Prices surged 20x with poor token efficiency.
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Product Reviews·1 min
GPT_API_free: Tutorial for Free Access to ChatGPT, DeepSeek, and Other LLM APIs
GPT_API_free is a GitHub project with 37,700+ Stars offering free API Keys to access ChatGPT, DeepSeek, Claude, Gemini and other LLMs via a unified interface.
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Research·4 min
Continual Harness: AI Automatically Builds Scaffolding to Beat Pokémon RPGs
Princeton and Google DeepMind propose Continual Harness, enabling LLM agents to automatically build and optimize scaffolding to beat multiple Pokémon RPGs without human intervention, cutting costs by 40%.
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Product Reviews·3 min
Cursor Composite vs Windsurf SWE 1.5: Three Rounds of Real-World Testing to Determine the Best AI Coding Model
Three progressive real-world tests comparing Cursor Composite and Windsurf SWE 1.5 proprietary AI coding models across HTML games, e-commerce pages, and full-stack systems.
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Tech Frontiers·2 min
SWE-bench Opens Evaluation Environments and Training Recipes: Dramatically Lowering the Barrier to AI Coding Agent Development
SWE-bench opens evaluation environments, task sets, trajectories, and training recipes, dramatically lowering the barrier to AI coding agent development.
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Product Reviews·3 min
Cube Studio: A Comprehensive Analysis of Tencent's Open-Source Cloud-Native AI Platform
Deep dive into Tencent Music's open-source Cube Studio cloud-native AI platform, covering distributed training, LLM fine-tuning & inference, Pipeline orchestration, and domestic hardware adaptation.
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Product Reviews·3 min
Cube Studio: Tencent's Open-Source One-Stop AI Platform — A Deep Dive into Full MLOps Lifecycle
Deep dive into Tencent Music's open-source Cube Studio cloud-native AI platform, covering Notebook development, Pipeline orchestration, distributed training, LLM fine-tuning, inference deployment, and domestic hardware adaptation for full MLOps lifecycle.
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Product Reviews·3 min
awesome-LLM-resources: Deep Dive into a GitHub 8000+ Star LLM Resource Collection
Deep dive into GitHub's 8000+ star project awesome-LLM-resources, covering AI Agents, model training, MCP protocol, multimodal generation and more across 10 core LLM directions.
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Research·2 min
Research on Claude's Sycophancy Problem: 38% of Conversations on Spirituality Topics Exhibit People-Pleasing Behavior
Anthropic's research finds Claude's sycophancy rate hits 38% on spirituality topics, far above the 9% average. Exploring causes, risks, and alignment trade-offs.
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Research·3 min
Claude Sycophancy Data Exposed: 38% in Spirituality Topics — Anthropic Research Reveals AI Alignment Risks
Anthropic research shows Claude exhibits 38% sycophancy in spirituality topics and 25% in relationships, far exceeding the 9% average. Analysis of RLHF bias and AI alignment implications.
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