#OpenAI reasoning model
3189 related articles
·3 min
Andrew Ng's New Course Explained: A Practical Guide to Using OpenAI's O1 Reasoning Model
Deep dive into Andrew Ng and OpenAI's Reasoning with O1 course covering test-time scaling, new prompting paradigms, multi-model orchestration, and practical applications for developers.
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Tech Frontiers·4 min
IBM Think 2025 Deep Dive: Rising Hallucination Rates in Reasoning Models and OpenAI's Acquisition of Windsurf
Deep dive into IBM Think 2025's Generative Computing and Granite 4, why reasoning model hallucination rates are rising, and OpenAI's $3B Windsurf acquisition strategy.
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·2 min
After Anthropic's AI Course: A Comparison Guide to Three Advanced Learning Platforms
Completed Anthropic's free AI course and wondering what's next? This guide compares Udacity, DeepLearning.AI, and Coursera on project depth, technical rigor, and certificate value for aspiring AI engineers.
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·3 min
LangChain Tool Calling in Practice: Adding Your First Tool to an AI Agent with the @tool Decorator
Complete guide to LangChain AI Agent tool calling: from defining tools with @tool decorator to automatic Agent invocation, with calculator examples, security tips, and naming conventions.
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·3 min
LLM Learning Roadmap: A Three-Phase Practical Guide
A systematic LLM learning roadmap: from Python basics to LangChain & LlamaIndex frameworks, RAG, Agent, and fine-tuning core skills, plus hands-on projects to master LLM app development in 3 months.
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·2 min
Embabel: A Deep Dive into the AI Agent Framework Built for the JVM
Embabel is a JVM agent framework written in Kotlin, enabling Java/Kotlin developers to build AI Agents within their familiar tech stack. A deep analysis of its positioning, technical advantages, and synergy with the Spring ecosystem.
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·5 min
Harness: The Operating System That Takes LLM Agents from Demo to Production
Deep dive into Harness technology: how context engineering, memory management, and multi-agent architecture transform LLM agents from stochastic demos into stable production systems.
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·2 min
Grok's Hidden Settings Exposed: 33 Internal Models and Elon-Only Access Privileges Revealed
A Reddit user uncovered Grok's hidden Elon-Only Settings selector, revealing 33 internal models. We analyze the model iteration scale, tiered access design, and implications for AI transparency.
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·3 min
Java Backend to AI: A Practical Guide to Multi-Agent Collaboration with Spring AI
How can Java backend engineers transition to AI Agent development? This guide covers the evolution from Chat to Agentic AI, ReAct decision-making, MCP tool calling, and multi-Agent orchestration with Spring AI.
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·4 min
Getting Started with LangChain 1.3: Ecosystem Architecture & Agent Development Complete Guide
Complete guide to LangChain 1.3 ecosystem: four core modules (LangChain, LangGraph, DeepAgent, LangSmith), from setup to building your first Agent with tools, prompts & memory.
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·3 min
Tutorial: Connect Claude, Codex, and Other LLMs to Copilot in VSCode
Learn how to connect Claude, Codex, and other LLMs to VSCode's Copilot Chat via a third-party API proxy plugin. Four steps: get a Key, install plugin, manage models, and switch freely.
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·3 min
What Are AI Agents? A Deep Dive into the Three Core Components: Perception, Decision, and Action
A deep dive into AI Agents: their definition and three core components—Perception, Decision, and Action. Learn what distinguishes real AI agents from chatbots and automation scripts.
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·2 min
Struggling to Choose a Gemini Model? Analyzing UX Pain Points in the Multi-Model AI Era
Facing Gemini Pro, Flash, Ultra and many variants, users often suffer choice paralysis. This article analyzes AI model naming confusion, information asymmetry, and proposes UX solutions like smart defaults and intent-first design.
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·3 min
Sanders Writes to AI Giants: Pause Development or the Senate Will Act
Senator Bernie Sanders sent an open letter to OpenAI's Altman, Anthropic's Amodei, and Meta's Zuckerberg demanding an immediate AI development pause or face Senate legislation. Analysis of the political signals and regulatory trends.
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·2 min
LFM2.5 Released: How a 2.6B Small Model Rivals Models 4x Its Size
Liquid AI releases LFM2.5: a 2.6B parameter model rivaling 10B-class models on multiple benchmarks. Exploring its architectural innovation, training strategy, and implications for AI efficiency.
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·3 min
The Truth Behind Cheap Cursor Pro Services: Risks and Alternatives
In-depth analysis of cheap Cursor Pro subscription services, revealing three major risks—account bans, code leakage, and service disappearance—plus compliant alternatives.
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·3 min
Scaling Laws for Pre-training and RL: Chess Experiments Reveal Post-Training Compute Allocation Principles
Chess experiments systematically study compute allocation across pre-training, SFT, and RL, revealing that pre-training sets the downstream ceiling and RL mainly boosts pass@1 reliability, not exploration breadth.
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·4 min
Cursor's Frequent Updates Spark Controversy: How to Balance Rapid Iteration with Stability?
Cursor AI code editor faces backlash over near-daily updates that break core features for months. This article analyzes the tension between rapid iteration and stability in AI dev tools.
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·3 min
How a Senior Data Scientist Navigates Job Hunting After Being Laid Off: A Survival Guide for the AI Job Market
A senior data scientist with a Physics PhD and 4.5 years of experience gets laid off, revealing the AI job market's shift from traditional ML to Agent engineering. Practical advice on bridging skill gaps.
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·2 min
Muse Glimmer Lands on Arena.ai: What Rankings of #24 in Text and #26 in Code Really Mean
Muse Glimmer ranks #24 in Text and #26 in Code on Arena.ai. This article explains the blind-test scoring mechanism and analyzes what these rankings mean in the competitive LLM landscape.
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