#prefix caching
81 related articles
·4 min
Choosing the Wrong Agent Ruins Everything: Why the Same Model Can Perform Like Two Completely Different AIs
The same LLM API performs drastically differently under different Agent frameworks. Through a real database crash case, this article analyzes why choosing the right Agent matters more than switching models.
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·3 min
Pi Agent Configuration Guide: Building Your Own AI Coding Assistant
A step-by-step Pi Agent configuration tutorial covering installation, LLM connection, extension ecosystem, MCP setup, and Token-saving tips. Learn to build a truly controllable AI coding assistant.
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·3 min
Anthropic's Hands-On Guide: How to Scientifically Choose and Evaluate AI Models
Anthropic's Applied AI team breaks down a methodology for choosing AI models: building custom evals, avoiding three common pitfalls, measuring value by cost per success, and cutting costs with prompt caching and context engineering.
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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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·3 min
Qwen 3.8 Open Source Incoming, DeepSeek Coding Agent, and the Latest in China's AI Ecosystem
Qwen 3.8 open source incoming, DeepSeek Resonics native coding agent debuts, Kimi pauses new subscriptions amid compute pressure, and Qiyimole shows domestic GPU interconnect solution at WAIC.
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·5 min
Advanced Claude Code in Practice: Building a Top-Tier AI Development Workflow
In-depth analysis of Claude Code customization methodology: from access, knowledge injection to tooling. Master context window management, zero-overhead Hooks, and MCP & Skills plugin primitives to build a scalable AI software engineering workflow.
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·2 min
Claude Code with Open-Source Models: Why Cache Misses Happen and How to Fix Them
Why Claude Code cache misses occur with DeepSeek and MiniMax, how Prompt Cache and KV Cache work, and practical solutions including API proxy layers and stable prefix strategies to cut AI coding costs.
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·4 min
Complete Guide to Running Claude Code on Amazon Bedrock
Complete guide to deploying Claude Code on Amazon Bedrock: environment setup, cross-region inference, prompt caching, cost attribution, Guardrails, and the Mantle endpoint for enterprise compliance.
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·3 min
Codex on Both Fronts: A Complete Setup Guide for the Desktop and CLI AI Coding Clients
A complete guide to installing and configuring OpenAI Codex desktop and CLI clients, covering model settings, API relay integration, prompt caching, and real cost data for GPT-5.6 AI coding.
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·3 min
The Truth About Codex Ultra Mode: It's Not a Reasoning Level
Deep dive into OpenAI Codex's Ultra mode: it's not a reasoning level but a system prompt switcher. Learn why Ultra burns tokens, the V1/V2 agent system issues, and how Claude Code Workflows gets it right.
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·3 min
Render Code as Images and Send to Claude — Cut API Costs by 70%
PXPipe renders system prompts and tool docs as images sent to Claude, exploiting visual token pricing. Real-world results: 68% fewer input tokens, 59–70% lower bills.
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·4 min
Two Paths to Better AI Reasoning: Test-Time Scaling and Formal Verification
This article synthesizes two MSR India Summit talks, exploring two key paths to better AI reasoning: test-time scaling with variable granularity search, and a formal verification framework for trustworthy agent execution.
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Deep Dives
Deep Dive into OpenAI's Official GPT-5…
·3 min
Deep Dive into OpenAI's Official GPT-5.6 Prompting Guide: The Shift from Manual to Automatic
A deep dive into OpenAI's official GPT-5.6 Sol prompting guide: conciseness-first, outcome-oriented design, autonomy boundaries, tool routing, and reasoning intensity tuning.
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MemStitch Zero-Copy Context Bridging: …
·2 min
MemStitch Zero-Copy Context Bridging: How It Achieves 25x TTFT Speedup in vLLM
A deep dive into how MemStitch's zero-copy context bridging achieves 25x TTFT speedup in vLLM. Covers KV Cache optimization, prefill acceleration, and practical developer value.
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GPT-5.6 Trio Launches: Luna, Terra, an…
·3 min
GPT-5.6 Trio Launches: Luna, Terra, and Sol Built for Long-Running Agentic Tasks
OpenAI officially launches the GPT-5.6 family: Luna, Terra, and Sol, with 1M token context and a focus on long-running agentic performance. A deep dive into three-tier pricing, Agents' Last Exam results, the SWE-Bench Pro controversy, and new API features like programmatic tool calling and native multi-agent support.
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·5 min
Practical AI Cost Optimization: A Deep Dive into Intelligent Routing and Context Compaction
AI bills keep soaring? This article breaks down two core cost-reduction strategies: intelligent routing via an LLM Gateway, and context compaction to cut Token usage—no major refactoring needed.
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·5 min
The Five Unsolved Challenges of Production MLOps: A Deep Dive into GPU Scheduling, Fault-Tolerant Training, and Model Observability
A deep dive into the five genuinely tough challenges of production MLOps: fault-tolerant training on Spot instances, cross-team GPU scheduling, data reproducibility, model observability, and inference cost optimization.
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·4 min
GPT-5.6 Released: Coding Benchmarks Surpass Rivals, Inference Speed 10x Faster
Just 12 days after a rival launch, OpenAI released GPT-5.6, scoring 91.9% on Terminal-Bench 2.1 to surpass competitors. Ultra mode supports multi-agent collaboration, inference hits 750 tokens/sec.
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·6 min
A Deep Dive into AI Agent Harnesses: The Core Engineering Behind Agent Frameworks
What is an AI Agent's harness? This article systematically dissects the core components of agent frameworks: context management, tool use, control loops, and caching strategies—revealing why the same model performs so differently across harnesses.
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·4 min
LingBot-World 2.0 Meets SGLang: A Deep Dive into the Evolution of Real-Time Interactive World Models
SGLang-Diffusion now officially supports LingBot-World 2.0, delivering leaps in resolution and temporal consistency. With live sessions, chunked streaming, and camera control, world models achieve low-latency controllable interaction.
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