#context decay
157 related articles
·2 min
Do Longer Contexts Break LLM Instructions? Understanding Instruction Decay and How to Fix It
As conversation context grows, LLMs gradually "forget" early instructions — this is instruction decay. Learn the causes, attention mechanism principles, Lost in the Middle effect, and practical mitigation strategies.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·4 min
GPT-5.6 Value Maxing Practical Guide: From Token Stacking to Output Optimization Methodology
Deep dive into OpenAI GPT-5.6 Value Maxing strategies covering Sol/Terra/Luna model selection, KV cache optimization, Prompt compression, and programmatic tool calling to help developers achieve more output with fewer Tokens.
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·5 min
Is the Self an Illusion? Neuroscience Reveals the True Nature of Identity
Emory neuroscientist Gregory Burns argues the self is a story the brain weaves. From the Ship of Theseus to memory confabulation, explore how identity is shaped by narrative and how we can rewrite it.
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·3 min
How GitHub Established Reliable Ownership for 14,000 Repositories in 45 Days
GitHub had over 14,000 internal repos with less than half having clear ownership. In 45 days, they verified ownership for every active repo through activity filtering, validation, and archiving.
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·3 min
The Software 3.0 Era: An AI Programming Evolution Guide for Frontend Engineers
Natural language programming is reshaping frontend development. This article explains AI code generation, Prompt formulas, pitfall avoidance, RAG, Agent orchestration, and skill maintenance.
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·4 min
Claude Code from Scratch: A Complete Guide from Installation to Enterprise-Grade Development
A complete guide to Claude Code from beginner to enterprise practice: covering CLI installation, connecting domestic LLMs via CC Switch, basic commands, Git workflow integration, automated bug fixing, and engineering capabilities like MCP and SubAgents.
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·3 min
D-Flash: The Secret Behind 3.5x LLM Inference Speedup with Diffusion Drafting
D-Flash uses fast diffusion parallel drafting and target feature KV injection to solve the autoregressive Drafter latency bottleneck in speculative decoding. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, surpassing EAGLE3 and MTP.
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·2 min
D-Flash: How Diffusion Drafting Delivers 3.5x Faster LLM Inference
D-Flash solves the autoregressive drafter latency bottleneck in speculative decoding via fast diffusion parallel drafting and target feature KV injection. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, beating EAGLE3 and MTP.
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·6 min
The Three-Layer GTM Agent Architecture: Enabling AI to Truly Recognize Buyer Intent and Identity
An in-depth analysis of the three-layer GTM Agent architecture—the Signal, Buyer Intelligence, and Action layers—revealing how context graphs identify anonymous visitors and capture purchase intent.
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·7 min
Claude Code in Action: Building an Embedded Tetris Game with AI in Under 3 Hours
A hands-on case: using Claude Code and the LVGL graphics library to build a Tetris game running on an embedded board from scratch in under 3 hours.
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·7 min
Claude Code in Action: Building an Embedded Tetris Game with AI in 3 Hours
A hands-on case study: using Claude Code and the LVGL graphics library to build a Tetris game running on an embedded dev board from scratch in under 3 hours.
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·3 min
From Word Vectors to Large Language Models: The Complete Evolution of AI 2.0 Technology
From word vectors and embeddings to RNNs, BERT, Transformers, and ChatGPT — a complete guide to the technical evolution of large language models and the AI 2.0 era.
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·6 min
Hands-On with Qwen 3.8 Max: How an Open-Source Model Closes In on Top-Tier Closed Models
Qwen 3.8 Max has 2.4 trillion parameters and will be open-sourced. In KingBench testing it scored 81.25%, ranking second, beating Claude Opus 4.8 and trailing only Fable 5. A deep dive into its performance across 8 tests.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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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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·7 min
Harness Engineering: Why a Powerful Model Isn't Enough — Here's What Really Matters
A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.
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·4 min
DeepSeek Open-Sources DeepSpec: A Full-Stack Breakdown of Speculative Decoding
DeepSeek open-sources DeepSpec, a full speculative decoding training and evaluation toolkit featuring three draft model algorithms (Ego3, DeepFlash, DeepSpark), 12 checkpoints, MIT license, and 60–85% real-world speedup.
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·4 min
How Engineers Can Avoid Becoming Mere Code Reviewers in the AI Era: Addy Osmani's Answer
Google's Addy Osmani at AI Engineer conference: as AI agents outpace human review, engineers' core value lies in the "verdict" — deciding what's worth building and owning outcomes.
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