#LLM coding
1875 related articles
·1 min
Agentic Coding in Practice: Test Feedback Loops, LLM Benchmarks, and an Engineering Adoption Guide
An in-depth look at agentic coding: how test-driven loops enable AI self-correction, the real limits of LLM benchmarks, and key engineering lessons on context management and human-AI collaboration.
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·4 min
From Enter to First Token: Decrypting the Full LLM Inference Pipeline (Autoregression, KV Cache & Decoding Strategies)
From pressing Enter to the first character appearing, what happens inside an LLM? This article breaks down autoregressive generation, KV cache acceleration, and decoding strategies like temperature, Top-k, and Top-p.
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·2 min
DeepSeek Speculative Decoding Algorithm Merged into vLLM Main Branch — Single-User Token Speed Jumps Dramatically
DeepSeek's speculative decoding algorithm (DSpark) is now merged into vLLM main branch, natively supporting Qwen3 and Gemma. Tests show ~150× single-user token speed gains and ~40–50% throughput improvement.
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·7 min
AI Coding in Practice: Generating a Minecraft Clone in 90 Minutes as LLMs Enter Project-Level Development
A major breakthrough in AI coding! Hands-on tests show new LLMs generating a Minecraft clone in 90 minutes and a TMNT game in 30 minutes, building 3D scenes, animation, and game logic in one shot.
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·5 min
Why Did Ollama Rise So Fast? Decoding the Success of a Local LLM Tool
How did Ollama grow from a niche open-source project into developers' default choice for running local LLMs? This article breaks down its rise across product design, technical strategy, and ecosystem building.
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·5 min
DeepSeek DSpec Hands-On: How Speculative Decoding Accelerates LLM Inference by 6x
Hands-on report on DeepSeek's open-source inference acceleration toolkit DSpec: draft model + smart scheduling delivers lossless speedup, hitting acceptance length 6 on GSM8K and reproducing official data.
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Building a Coding Agent with LLM: A De…
·3 min
Building a Coding Agent with LLM: A Deep Dive into Simon Willison's llm-coding-agent Experiment
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.
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·6 min
What Is Vibe Coding? A Complete Breakdown of the LLM + Agent + Workflow Three-Layer Architecture
Vibe Coding, coined by ex-Tesla AI Director Karpathy, redefines AI programming. This article breaks down the LLM + Agent + Workflow three-layer architecture.
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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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Product Reviews·2 min
AI Coding Appliance vs Cloud LLMs: Can ¥480K in Annual Fees Buy 4 Local Deployment Solutions?
A deep cost comparison between AI coding appliances and cloud LLM APIs. A 20-person team spending ¥480K/year on tokens can deploy 4 local OnePanel units at ¥99K each, breaking even in 2.5 months.
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·3 min
Ollama API Key Proxy: Multi-Key Rotation to Solve Rate Limiting Issues
Learn how Ollama API Key Proxy solves cloud LLM rate limiting through reverse proxy with round-robin key rotation, 429 auto-cooldown, and smart retry logic.
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·1 min
Ollama vs OpenCode: Real-World GLM Quota Difference Analysis (380 vs 880 Requests)
Real-world comparison of Ollama vs OpenCode GLM5.2 quota consumption — analyzing the gap between 380 and 880 requests per cycle and the key factors behind it.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
Gemini Managed Agents API Update: Environment Hooks and Free Tier Fully Explained
Google Gemini Managed Agents API introduces environment hooks, model selection, free tier support, and default model upgrades—empowering AI Agent developers with stronger execution control and lower barriers to entry.
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·2 min
OpenAI Open-Sources Codex Security Components: Setting a New Standard for AI Coding Safety
OpenAI open-sources Codex Security components, bringing automated security detection to AI code generation. Analysis of its strategic value, developer impact, and the industry shift from capability to security.
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·2 min
OpenAI Open-Sources Codex Security Components: Setting a New Standard for AI Coding Safety
OpenAI open-sources Codex Security components, bringing automated security detection to AI code generation. Analysis of its strategic significance and industry impact.
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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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·3 min
Lessons from Claude Code's 80% Prompt Reduction: Less Is More in Prompt Engineering
Anthropic cut Claude Code's system prompt by 80% and got better performance. Learn why verbose prompts hurt, how to streamline them, and key takeaways for AI developers.
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