#post-training
13 related articles
The Five-Layer Evolution of Scaling La…
·2 min
The Five-Layer Evolution of Scaling Law: How Physical AI Opens the Next Growth Curve
Deep analysis of Scaling Law's five-layer evolution from Pre-Training to Multi-Agent, exploring Physical AI's World Models, edge inference, and emotional interaction.
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Claude Opus 4.8 Identifies Itself as D…
·3 min
Claude Opus 4.8 Identifies Itself as DeepSeek: Data Contamination or Distillation? A Technical Analysis
Anthropic's Claude Opus 4.8 failed within 2 hours of launch, identifying itself as DeepSeek and Tongyi Qianwen in Chinese. Deep analysis of data contamination vs distillation hypotheses and multilingual alignment gaps.
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Cursor Design Mode Launch and OpenAI C…
·3 min
Cursor Design Mode Launch and OpenAI Codex Updates: Latest Developments in AI Programming Tools
Cursor launches Design Mode for visual development, OpenAI Codex updates and Safety Lock Mode released, Anthropic doubles limits, AI agent leaderboards debut, Google DeepMind model compression breakthrough.
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·3 min
vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference
Deep dive into vLLM's core technologies for high-throughput LLM inference, including PagedAttention memory management, continuous batching, distributed deployment, and comparisons with TensorRT-LLM.
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·3 min
Codex in Practice: A Detailed Guide to AI Programming Workflows for Enterprise Code Review and Personal Projects
Explore how OpenAI Codex is used in enterprise code review at Alchemy and personal side projects, with insights on AI-assisted workflows, GPT-5.5, and Computer Use.
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Tech Frontiers·1 min
Gemini 3.5 Flash Achieves a Massive Leap on the GDPval Benchmark
Google Gemini 3.5 Flash surpasses Gemini 3.1 Pro on the GDPval benchmark. The lightweight Flash model leverages post-training techniques to approach frontier-level performance, redefining the balance between quality and cost.
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Deep Dives·4 min
AI Hallucinations: Why Large Language Models Inevitably "Make Things Up" and How to Deal With It
Deep dive into AI hallucination's three root causes: training objective flaws, exposure bias, and probabilistic generation. Covers classification and practical mitigation strategies including RAG.
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Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Deep Dives·3 min
How Multi-Agent Teams Solve AI Hallucination and Make AI Reliable
Deep analysis of how multi-agent architecture solves AI hallucination. From context rot to adversarial debate mechanisms, see how Anthropic, xAI, and Kimi reduce hallucination rates from 12% to 4.2%.
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Expert Opinions·3 min
The Salary Ceiling for Agent Engineers: Two Critical Dividing Lines
Agent engineer salary gaps hinge on two dividing lines: real production deployment experience and depth of foundational theory including deep learning, fine-tuning, and reinforcement learning.
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Product Reviews
Llama 3.3 70B In-Depth Review: Testing…
·3 min
Llama 3.3 70B In-Depth Review: Testing the Strongest Open-Source LLM with 13 Questions
Meta releases Llama 3.3 70B open-source model with just 70B parameters rivaling 405B performance. Tested on 13 logic, math, and coding questions, it passed 12 — reshaping the open-source model landscape.
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Tech Frontiers·2 min
Qwen Core Team Turmoil, OpenAI and Google Release New Models in Rapid Succession | AI Daily
Multiple core leaders depart Alibaba's Qwen team amid metric disputes. Same day: MiniMax Music 2.5+, OpenAI GPT 5.3 Instant, Google Gemini 3.1 Flashlight, and Seedance 2.0 pricing announced.
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