#LLM naming conventions
140 related articles
Tutorials·3 min
Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference
Decode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.
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·2 min
Bun Now Generates CLAUDE.md by Default: The AI-Native Development Era Has Arrived
Bun runtime now generates a CLAUDE.md file by default with bun init, signaling AI assistants becoming first-class citizens in the development toolchain.
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·4 min
Are AI Coding Tools Making Developers Dumber? The Efficiency vs. Competence Dilemma
After heavy use of AI coding tools like Cursor and Claude, an indie developer discovers his debugging and code comprehension skills are eroding. Exploring the skill atrophy risks behind AI-boosted productivity.
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·4 min
Getting Started with Claude Code: Environment Setup, Common Commands, and Git Workflow in Practice
A detailed guide to Claude Code installation, domestic model switching, project analysis commands, and Git workflow practice to help developers quickly master this AI programming collaboration tool.
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·3 min
Kimi K3 Real-World Testing in 3D Modeling & Game Development: Can Open-Source Models Overtake Closed-Source Giants?
In-depth testing of Kimi K3 in 3D modeling, physics simulation, animation rigging, and game development vs Fable 5 and GPT Solve 5.6. Open-source model delivers top-tier results at one-quarter the price.
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·2 min
GPT-5.6 Free Trial Is a Scam: How to Spot AI Marketing Traps
Exposing the "GPT-5.6 free trial" scam on social platforms, analyzing data security risks of third-party AI mirror sites, and providing practical tips to identify AI marketing traps.
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·2 min
GPT-5.6 Free Trial Is a Scam: How to Spot AI Marketing Traps
Exposing the "GPT-5.6 free trial" scam circulating on social media. Learn about data risks of third-party AI mirror platforms and how to identify AI marketing traps.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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·4 min
Opus 5 Real-World Testing: Cheaper Yet Stronger — Why It's Now My Default Coding Model
In-depth testing of Claude Opus 5's coding abilities vs Fable 5 and 5.6 Sol. Why Opus 5 outperforms pricier models at half the token cost, plus selection guide and distillation explained.
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·3 min
SlopCodeBench: Why AI Code Benchmarks Are Failing
SlopCodeBench sparks deep reflection on AI code evaluation. From benchmark contamination to pass-rate pitfalls, exploring why current benchmarks fail to measure real code quality.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: Architecture, Long Context, and Reasoning Capabilities
Deep analysis of Moonshot AI's Kimi-K3 technical report covering long context processing, MoE architecture, reasoning capabilities, and China's position in the global AI competition.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: A Comprehensive Analysis of Architecture, Long-Context Processing, and Reasoning Capabilities
Deep dive into Moonshot AI's Kimi-K3 technical report, analyzing its long-context processing, MoE architecture, reasoning improvements, and its position in global AI competition.
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·3 min
GitHub Agentic Workflows in Practice: Automating Cross-Repository Documentation Updates
Learn how GitHub Agentic Workflows automate cross-repo documentation updates. See the Aspire team's AI-driven approach: event triggers, smart drafting, and SME review to keep docs in sync with code.
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·3 min
Automating GitHub Releases: Design and Practice of a Tag-Triggered Release Action
A ten-year open source maintainer shares how to build a universal tag-to-release GitHub Action, covering its opinionated design philosophy, dogfooding validation, and AI-assisted development.
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·3 min
Claude Opus's Autonomous Debugging Capabilities: What It Means When AI Proactively Writes Test Harnesses
New Claude Opus proactively writes test harnesses to observe runtime behavior. We analyze how this shift from passive code generation to autonomous debugging marks a key evolution in AI programming.
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·7 min
Auditing Cryptographic Libraries with AI: What We Found in Cloudflare's CIRCL
An in-depth look at using AI LLMs to audit Cloudflare's open-source cryptographic library CIRCL, covering constant-time detection, side-channel vulnerabilities, and human-AI collaboration.
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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
Getting Started with Claude Code: Five Dimensions That Show Why It Outperforms Ordinary 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 ordinary AI across five dimensions: interaction, context, execution, memory, and tool calling.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·4 min
Vibe Coding vs. AI-Assisted Engineering: An In-Depth Look at Enterprise-Level Development
Can AI coding tools let non-developers replace programmers? This deep dive examines Vibe Coding's real limits, compares Claude Code vs. Codex, and reveals the methodology behind enterprise-grade AI-assisted software engineering.
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