標籤 #build-vs-buy
6 篇文章。
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· Paul Lukic
AI Agents Cause Outages? Fix the Input, Not Just the Model
Engineers report ~17 hours a week debugging AI-generated code while median productivity gains sit near 7.7%. The bottleneck is context retrieval, not model quality.
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· Paul Lukic
McKinsey: 30% of Firms Lose Productivity With Agentic AI. Why?
Agentic AI spend is climbing while output flatlines. The usual culprit isn't the model — it's how much irrelevant code the agent reads before it does anything useful.
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· Paul Lukic
Agentic Coding is Here. Are You Ready for the Token Bill?
Autonomous coding agents multiply engineering output and API spend at the same time. Most of that spend is context waste, and a code graph cuts it by two thirds.
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· Paul Lukic
AI Tool Sprawl: Your Devs Are Drowning in Inefficient Agents
Running five AI coding tools doesn't give you five times the leverage. It gives you five agents grepping the same repo, and a token bill that quietly compounds.
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· Paul Lukic
Build vs. Buy Code Intelligence: The Real ROI
Building in-house code graphs costs $250k+ and 6 months. Adopting Coograph cuts AI spend 80% while freeing engineers to ship product.
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· Paul Lukic
MCP Server：把 AI 編程 agent 規模化，又不浪費
MCP server 統一了 AI agent 讀取你程式碼庫的方式。在一個代表性任務上，圖譜式上下文砍掉約 80% 的 token、約 4 倍的工具呼叫——本文說明這為什麼重要，以及自建還是採購該怎麼想。