#RoPE positional encoding
122 related articles
·4 min
The More Complex the AI Agent, the More It Hallucinates? The Hidden Cost of Architectural Complexity
Why do AI Agents hallucinate more as they grow more complex? This article analyzes the causes from error accumulation, context noise, and model completion nature, with 5 practical production strategies.
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·2 min
Kimi-K3 Lands on HuggingFace: A Full Breakdown of Moonshot AI's Open-Source Strategy
Moonshot AI's Kimi-K3 model is set to launch on HuggingFace. A deep dive into its architecture, licensing, community reactions, and strategic implications for the global open-source AI landscape.
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·7 min
Getting Started with Claude Code: Five Dimensions to Understand Why It Outperforms Regular AI
Claude Code isn't just a chat AI—it can directly read projects, modify code, and run commands. This article compares Claude Code with regular AI across five dimensions to help you decide if it's worth trying.
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·7 min
DeepSeek's Mysteriously Retracted Paper: Breaking AI's Multimodal Reasoning Bottleneck with Visual Primitives
DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.
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·7 min
Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'
Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
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Kronos Financial Foundation Model: Usi…
·7 min
Kronos Financial Foundation Model: Using AI to Read the Market Language of Candlesticks
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.
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·3 min
Alibaba's 2.4 Trillion Parameter Qwen Model: Separating Technical Substance from Marketing Hype
Alibaba's Qwen releases a 2.4T parameter MoE model claiming to be 'second only to Gemini 2.5.' We break down what's real—and what's just hype.
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·7 min
Qwen3 235B A22B in Practice: A 2.4-Trillion-Parameter Open Model Surpasses Claude Opus
Hands-on with Alibaba Tongyi Qianwen's strongest Qwen3: a 2.4-trillion-parameter open weight model scoring 81.25% on KingBench, ranking second and beating Claude Opus 4.8 with perfect scores in game dev, math, and agent tasks.
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·6 min
The Boundaries of AI Coding: When Should You Show Yourself a Red Card?
As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.
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·2 min
Agent A1 Open Source: 35B MoE Local Inference Speed Test & Deployment Guide
Agent A1 is a 35B MoE open-source model from Shanghai's Intern Science, activating only 3B params. Hits 95 tok/s on M4 Mac Studio. Supports 256K context, tool calling, and free local deployment via Ollama.
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·3 min
Qwen's Six-Generation Evolution: A Complete Breakdown from Architecture Innovations to the Reasoning Race
From Qwen1 to Qwen3-2507: a complete breakdown of six generations of evolution over two years — GQA, MoE, GRPO, dynamic thinking, and the core shift from architecture to reasoning.
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·2 min
Zhipu GLM New Model Coming Soon: Decoding the Founder's Teaser
Zhipu AI's Z.ai founder teases a new GLM model just one month after GLM 5.2. We decode the announcement, explore likely upgrade directions, and analyze China's LLM iteration pace.
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·3 min
Bernini Tile-Based Generation: An Engineered Approach to Video Super-Resolution in ComfyUI
Bernini is a ComfyUI video super-resolution node package using Tile Split/Select/Merge to solve seams, drift, and VRAM overflow. Benchmarked at 325s for 39 frames at 1920×1080.
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·4 min
Understanding Transformer From the Ground Up: How Language Models Predict the Next Word
From Tokenization and Embedding to the Attention mechanism, this article systematically breaks down how Transformer works — and how ChatGPT turns input text into next-token probabilities.
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·3 min
Building a Transformer from Scratch: A PyTorch Hands-On Guide for Self-Taught Developers
A self-taught developer with 2 years of Python experience implements Transformer from scratch using plain PyTorch, following the original 'Attention Is All You Need' paper with a two-phase approach: inference first, then full training.
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·2 min
Kimi K3 Leads the Charge: Inside the AI Coding Agent Framework Transparency Race
Kimi K3 sets a new open-weight record at 2.8T params, GrokBuild pivots to local-first after a privacy crisis, Open Interpreter rewrites in Rust — a deep dive into five major AI coding agent developments and the shift toward harness transparency.
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·2 min
GLM Open-Source Model: Can a Million-Token Context Window Challenge Claude Code?
GLM open-source LLM claims 1M-token context, local deployment, and coding ability rivaling Claude Code. We break down the three key selling points and evaluate the hype vs. reality.
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·2 min
Variable-Length Matrix Classification: Choosing the Right Neural Network Architecture for Unordered Set Data
Why do CNNs and RNNs fail on unordered matrix data? Learn about permutation invariance, Deep Sets, and Set Transformer to pick the right architecture for set-based classification.
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·3 min
AI Coding Agents Explosion: CodeWell Goes Open Source, Kimi K3 Launches, Claude Gets Browser Access
CodeWell open-sources a multi-model terminal coding agent, Kimi K3 launches with ultra-long context, MiniMax Code 2.0 rebuilds its Agent architecture, and Claude gets browser access. AI is accelerating from content generation to task execution.
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·2 min
Long Conversations Are Draining Your AI Credits: A Deep Dive into Token Billing
Why do long AI conversations drain your credits so fast? This article explains LLM stateless architecture, Token billing mechanics, and practical tips like sliding windows and summary compression to cut API costs.
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