#fine-tuning vs pre-trained
47 related articles
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Meta Muse Glimmer: A Deep Dive into the 30B Open-Source Local Coding Model
Deep dive into Meta Muse Glimmer, a 30B open-weight coding model for local deployment. Covers technical specs, use cases, hardware requirements, and comparisons with Code Llama and DeepSeek Coder.
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SAM3 + RTMPose Zero-Shot on 1950s Factory Footage: A Real-World Test of Foundation Model Generalization
A developer applied SAM3 and RTMPose to 1950s black-and-white factory footage with zero fine-tuning and got accurate results. We analyze the technical logic and implications.
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Why Do AI Assistants Always Say "Absolutely"? The Technical Truth Behind Excessive Flattery
Why do AI chatbots always start with "Absolutely" and agree with everything? A deep dive into LLM sycophancy, RLHF training side effects, and how to get honest feedback from AI.
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Pothole Detection Model Misclassifying Roadsides? A Practical Guide to Object Detection Optimization
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A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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GenAI Job Interview Gone Wrong: Interviewer Googles Gemini for Questions on the Spot — What's Broken in AI Hiring?
A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.
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0 Lines of Hand-Written Code, 3 Hours to Build an AI Product That Can Take Payments
A real case: a creator launched an AI photo generation product in 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.
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Zero Lines of Hand-Written Code, 3 Hours to Build a Revenue-Generating AI Product
A real case: a creator launched an AI photo generation product in under 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.
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Getting Started with Codex: Installation and Usage Guide for OpenAI's AI Coding Agent
A comprehensive guide to OpenAI's new AI coding agent Codex: from concept and comparison of its four forms, to installing Git/Node.js/VS Code, configuring the API Key, and creating a workspace.
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The AI Whale Fall: How Big Players' Tr…
·2 min
The AI Whale Fall: How Big Players' Training Costs Nourish the Open Source Ecosystem
The "AI Whale Fall" reveals a hidden industry symbiosis: giants like OpenAI and Meta spend billions training models, and their open weights and research continuously nourish the open source ecosystem.
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Vibe Coding in Practice: A Comprehensive Review and Selection Guide for 6 AI Coding Tools
In-depth comparison of 6 Vibe Coding tools — Claude Code, Trae, Zhipu, Cursor, and more — covering setup cost, Chinese support, pricing, and code quality.
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AI Large Language Models for Beginners: Core Concepts, History, and China's Model Landscape
A beginner's guide to AI large models: clarify the relationships between AI, ML, deep learning, and LLMs, trace the journey from Deep Blue to ChatGPT and DeepSeek, and explore China's model landscape.
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How Machine Learning Engineers Can Adapt in the AI Era: A Guide to Skill Trade-offs and Career Reinvention
In the AI wave, ML engineers' work is quietly shifting: from building models to using them, from feature engineering to LLM app development. This article outlines the new skills to prioritize, fading old ones, and how to turn AI into career leverage.
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·6 min
Is AI Really Smart? The Future Directions and Capability Boundaries of Next-Generation Artificial Intelligence
Are large language models truly intelligent? This article analyzes core AI limitations — pattern matching, hallucinations, reasoning deficits — and explores next-gen directions like inference-time compute, neuro-symbolic AI, and embodied intelligence.
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·7 min
Getting Started with AI Agents: A Deep Dive into the Perception-Decision-Action Loop
A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.
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OpenAI Build Week Kicks Off: Turning Your Ideas Into Reality with Codex
OpenAI launches Build Week, a global developer event centered on Codex AI coding tool, featuring live sessions and community events to help developers ship ideas fast.
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Security Swarm Evaluation in Practice: Validating AI Security Tools with Real Vulnerabilities
A deep dive into Security Swarm's evaluation methodology: building test sets from real, recent vulnerabilities to avoid training data contamination and validate its ability to find more bugs at lower cost.
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·7 min
Complete Guide to Training Small Models on Kaggle's Free Compute: GPU Quotas and Practical Tips
In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.
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·7 min
Prompt Engineering in Practice: A Programmer's Core Methodology for Prompt Tuning
A deep dive into the underlying logic of prompt engineering from a programmer's perspective: understand token-probability generation, master the three principles of specific, rich, and low-ambiguity, and learn iterative prompt tuning.
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·6 min
Prompt Engineering in Practice: A Programmer's Core Methodology for Tuning Prompts
An in-depth analysis of prompt engineering from a programmer's perspective: understand token probability generation, master the three principles—specific, rich, low-ambiguity—and learn iterative prompt tuning.
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